hypothesis enzyme lock key

Lock-and-key model

strong>Lock-and-key model n., [lɑk ænd ki ˈmɑdl̩] Definition: a model for enzyme-substrate interaction

Table of Contents

Lock-and-key model Definition

Lock-and-key model is a model for enzyme-substrate interaction suggesting that the enzyme and the substrate possess specific complementary geometric shapes that fit exactly into one another. In this model, enzymes are depicted as highly specific. They must bind to specific substrates before they catalyze chemical reactions . The term is a pivotal concept in enzymology to elucidate the intricate interaction between enzymes and substrates at the molecular level. In the lock-and-key model, the enzyme-substrate interaction suggests that the enzyme and the substrate possess specific complementary geometric shapes that fit exactly into one another. Like a key  into a  lock , only the correct size and shape of the substrate ( the key ) would fit into the  active site  ( the keyhole ) of the enzyme ( the lock ).

Compare: Induced fit model   See also: enzyme , active site , substrate

Lock-and-key vs. Induced Fit Model

At present, two models attempt to explain enzyme-substrate specificity; one of which is the lock-and-key model , and the other is the Induced fit model . The lock and key model theory was first postulated by  Emil Fischer   in 1894. The lock-and-key enzyme action proposes the high specificity of enzymes. However, it does not explain the stabilization of the transition state that the enzymes achieve. The induced fit model (proposed by Daniel Koshland in 1958) suggests that the active site continues to change until the substrate is completely bound to the active site of the enzyme, at which point the final shape and charge are determined. Unlike the lock-and-key model, the induced fit model shows that enzymes are rather flexible structures. Nevertheless, Fischer’s Lock and Key theory laid an important foundation for subsequent research, such as during the refinement of the enzyme-substrate complex mechanism, as ascribed in the induced fit model. The lock-and-key hypothesis has opened ideas where enzyme action is not merely catalytic but incorporates a rather complex process in how they interact with the correct substrates with precision.

Key Components

Components of the lock and key model:

• Enzyme : the enzyme structure is a three-dimensional protein configuration, with an active site from where the substrate binds.
• Substrate : often an organic molecule, a substrate possesses a structural feature that complements the geometry of the enzyme’s active site.

In the lock and key model, both the enzymes and the substrates facilitate the formation of a complex that lowers the activation energy needed for a chemical transformation to occur. Such reduction in the activation energy allows the chemical reaction to proceed at a relatively faster rate, making enzymes crucial in various biological and molecular processes.

Lock-and-key Model Examples

Some of the common examples that are often discussed in the context of the Lock and Key Model are as follows:

• Enzyme lactate dehydrogenase with a specific active site for its substrates, pyruvate and lactate. The complex facilitates the interconversion of pyruvate and lactate during anaerobic respiration
• Enzyme carbonic anhydrase with a specific active site for the substrates carbon dioxide and water. The complex facilitates the hydration of carbon dioxide, forming bicarbonate
• Enzyme lysozyme binding with a bacterial cell wall peptidoglycan, which is a vital immune function

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• Aryal, S. and Karki, P. (2023).  “Lock and Key Model- Mode of Action of Enzymes”. Microbenotes.com. https://microbenotes.com/lock-and-key-model-mode-of-action-of-enzymes/
• Farhana, A., & Lappin, S. L. (2023, May).  Biochemistry, Lactate Dehydrogenase . Nih.gov; StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK557536/

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Last updated on January 11th, 2024

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Structural Biochemistry/Protein function/Lock and Key

In the Lock and Key Model, first presented by Emil Fisher, the lock represents an enzyme and the key represents a substrate. It is assumed that both the enzyme and substrate have fixed conformations that lead to an easy fit. Because the enzyme and the substrate are at a close distance with weak attraction, the substrate must need a matching shape and fit to join together. At the active sites, the enzyme has a specific geometric shape and orientation that a complementary substrate fits into perfectly. The theory behind the Lock and Key model involves the complementarity between the shapes of the enzyme and the substrate. Their complementary shapes make them fit perfectly into each other like a lock and a key. According to this theory, the enzyme and substrate shape do not influence each other because they are already in a predetermined perfectly complementary shape. As a result, the substrate will be stabilized. This theory was replaced by the induced fit model which takes into account the flexibility of enzymes and the influence the substrate has on the shape of the enzyme in order to form a good fit.

The active site is the binding site for catalytic and inhibition reaction of the enzyme and the substrate; structure of active site and its chemical characteristic are of specificity for binding of substrate and enzyme. Three models of enzyme-substrate binding are the lock-and-key model, the induced fit model, and the transition-state model. The lock-and-key model assumes that active site of enzyme is good fit for substrate that does not require change of structure of enzyme after enzyme binds substrate.

• Book:Structural Biochemistry

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Molecular Docking: From Lock and Key to Combination Lock

Ashutosh tripathi.

1 Department of Molecular and Cellular Medicine, College of Medicine, Texas A&M Health Sciences Center, College Station, Texas, USA

Vytas A Bankaitis

2 Department of Biochemistry and Biophysics, A&M Health Sciences Center, Texas, USA

3 Department of Chemistry, A&M Health Sciences Center, Texas, USA

Accurate modeling of protein ligand binding is an important step in structure-based drug design, is a useful starting point for finding new lead compounds or drug candidates. The ‘Lock and Key’ concept of protein-ligand binding has dominated descriptions of these interactions, and has been effectively translated to computational molecular docking approaches. In turn, molecular docking can reveal key elements in protein-ligand interactions-thereby enabling design of potent small molecule inhibitors directed against specific targets. However, accurate predictions of binding pose and energetic remain challenging problems. The last decade has witnessed more sophisticated molecular docking approaches to modeling protein-ligand binding and energetics. However, the complexities that confront accurate modeling of binding phenomena remain formidable. Subtle recognition and discrimination patterns governed by three-dimensional features and microenvironments of the active site play vital roles in consolidating the key intermolecular interactions that mediates ligand binding. Herein, we briefly review contemporary approaches and suggest that future approaches treat protein-ligand docking problems in the context of a ‘combination lock’ system.

Introduction

In 1894, Emil Fischer suggested that the specificity of an enzyme towards its substrate is based on the two components exhibiting complementary geometric shapes that fit perfectly like a ‘key in a lock’. This simple ‘lock and key’ analogy succinctly conceptualized the essence of enzyme substrate interaction where the ‘lock’ describes the enzyme and the ‘key’ describes the substrate or some other small molecule ligand (e.g. a small molecule inhibitor). In such systems, it is a requirement that the ‘key’ (substrate) fit appropriately into the key hole (active site/binding pocket) of the ‘lock’ (enzyme/receptor) for productive biochemistry to take place. Keys that are too small, too large, or with incorrectly positioned notches and grooves, will not fit into the lock ( Figure 1 ).

Illustration of ‘Lock and Key’ (top), Induced fit (middle) and Combination Lock (bottom) model of protein-ligand binding interaction.

But, enzymes show conformational flexibility and, on that basis, Daniel Koshland proposed a modification to the ‘lock and key’ model. Koshland’s suggestion was that active sites of enzymes are reshaped during interactions with substrate. This ‘induced fit’ model conceptualizes the ‘lock’ (enzyme) as a dynamic entity and that the ‘key’ (substrate) modulates the shape of the ‘key hole’. This concept paints a picture of an enzyme∷ligand interaction that is more akin to that of a ‘pin tumbler lock’. That is, a device where the pointed teeth and notches on the key allow the pins and wafers in the lock to move up and down until they align with the shear line of the cylindrical grooves of the key. The cylinder moves or rotates within the lock until that fit configuration is reached and the ‘lock’ opens. In an analogous manner, a ‘correct’ substrate aligns with active site residues of the enzyme to induce the appropriate conformational changes required for the desired outcome. ‘Induced fit’ is an attractive hypothesis as it accounts for why certain ligands are not substrates for an enzyme – even though they seemingly satisfy the specific shape requirements to bind to the active site ( Figure 1 ). Computational chemists are now using these basic ideas to model protein-substrate interactions. For reasons of its greater tract ability, the ‘lock and key’ paradigm has, for better or for worse, dominated the philosophical underpinnings of molecular docking approaches. In many respects, ‘induced fit’ approaches are more powerful-albeit more complicated. Below, we review these issues as these apply to molecular docking.

Molecular docking reaches for two major goals. The first is to correctly predict and identify the most favorable binding mode of a given ligand in the active site or binding pocket of a given protein. The second is to correctly rank a family of ligands in accordance to their corresponding experimentally-determined binding affinities [ 1 , 2 ]. The high-throughput version of docking, often referred to as virtual screening or in silico screening, aims to harvest small lists of potential active compounds for downstream experimental testing from a database of millions of compounds [ 3 ]. All docking protocols have two essential components: (1) a good positioning algorithm, and (2) a robust ranking or scoring system. Docking requires extensive sampling of conformational space for a ligand in the binding pocket of a protein and thereby generates large numbers of potential poses that orient a ligand within the active site. A good positioning algorithm samples ‘all’ possible binding modes, while the scoring system ranks all the solutions and identifies the most likely ‘binding mode’ of the ligand ( Figure 2 ).

Illustrates docking and scoring scheme as a two-step process. First step involves generation of poses within the binding cavity and second step involves energetic evaluation of poses to find best scoring pose that would mimic the native protein-ligand binding.

As simple as the process may sound, both components are themselves complex problems that pose significant challenges [ 4 , 5 ]. Positioning requires exhaustive exploration of accessible conformational space and binding orientations within the active site so as to extensively map interactions between active site residues and ligand. This requires that the process for generating binding modes respect a fine balance between speed and accuracy. That is, the process must not miss valuable solutions while maintaining sufficient computational efficiency to triage nonsensical binding modes. The ability to correctly score and rank the binding modes generated for a ligand presents an even bigger challenge. In cases where a number of different ligands are being interrogated, the scoring function aims to generate a rank list that corresponds to the binding affinity. This is a challenging task as many scoring functions fail to accurately predict binding affinity and often simply report a score which may or may not be at all congruent with experimentally measured binding affinities [ 6 ].

Considering the vast conformational sampling space that must often be negotiated in docking experiments, it is not computationally feasible to explore all the degrees of translational and rotational freedom of the ligand along with the internal conformational degree of freedom for protein-ligand complex. Therefore, docking experiments are typically coarse-grained so that only a restricted sampling space is covered, and a limited number of the possible binding modes are sampled. To optimize docking and scoring functions, several methods have recently been developed to add layers of sophistication to simple ‘key into lock’ ideas.

Defining the ‘Lock’

The identification and mapping of a binding site from crystal structure data can reveal key elements in protein-ligand binding [ 7 ]. Such knowledge is indispensable for docking and rational drug design since, in the majority of cases, receptor-drug interactions are specific in nature. However, this is not as trivial an undertaking as it may initially seem. The first requirement for any successful docking simulation is to define an active site or binding pocket as this is a critical step in structure-based drug design, and provides a starting point for finding new lead compounds or drug candidates [ 8 ]. A broad suite of cavity detection methods has been developed to address these issues in docking and virtual screening simulations [ 9 , 10 ].

The success of docking and structure-based design of a drug molecule for a specific target site depended largely on the quality of information regarding active site architecture because it is the size and shape of active site or binding cavity that dictates the three-dimensional geometry of ligands that will bind within. Pocket architecture also governs the directional and non-directional intermolecular interactions that mediate protein-ligand binding. Thus, clear definition of a binding pocket surface, coupled with identification of protein∷ligand interaction sites, provides a feature set for ligand orientation within a binding substructure. A target protein may have several pockets or cavities for a ligand to bind. Some might be deeply buried in the protein interior, while some might be displayed on the protein surface. However, the precise architecture of these pockets may not be absolutely clear from standard inspection of structural data as these cavities and protrusions are frequently interconnected via small and narrow channels, or are interspersed with numerous holes or voids [ 9 ]. The shape and size of binding pockets are also potentially subject to significant variations brought on by rotation of amino acid side-chains, backbone movements, loop motions, and/or ligand-induced conformational changes [ 9 ]. Fundamental uncertainties of this nature conspire to make identification of optimal dock solutions more difficult.

After defining the binding site surface, the next crucial step is to locate the interaction sites or “hot spots” within the binding site [ 11 , 12 ]. The primary goal of interaction mapping is to understand the chemical microenvironment of binding so that interaction points can be used to constrain pose possibilities and thereby restrict sampling space to a manageable size. Thus, binding site mapping is a critical step as it defines ‘lock’ parameters and sets the constraints for positioning the ligand in the defined binding region. In addition to preparing the active site for docking, the physicochemical properties and/or interaction can be represented as fields that can be mapped and visualized, interactively, in three dimensions. Using interaction maps, the spatial distributions of properties such as charge, hydrophobicity, etc. can be qualitatively analyzed [ 12 – 15 ]. Points of interaction between the ligand and active site might be elucidated and assessed qualitatively and, in some cases, semi-quantitatively. The importance of mapping interacting features is a critical endeavor since the number of ‘hot spots’ and their contributions to the larger binding process are essential for hypothesis generation. Quality interaction mapping also facilitates the docking process by defining a set of constraints that can be quantified in terms of how many, and which, interaction points might be matched by a ligand or a library of compounds. However, the harsh reality is that, even after defining the binding region for docking and extracting interaction sites, the docking process remains fraught with uncertainties that stem from the inherently dynamic physicochemical properties of the protein-ligand system.

Protein flexibility

Proteins leverage their intrinsic conformational flexibilities to carry out a wide range of biochemical processes in catalysis, protein-protein interaction and functional regulation [ 16 ]. In many cases, subtle motions in domains, flexibilities in the protein main chain, or re-orientation of side chains, changes the shape and size of the ligand binding envelope [ 17 ]. Ligand binding itself can also effect a change in the topography of binding pocket by inducing loop movements and other conformational shifts. These range from hinge movements of entire domains, to small side-chain rearrangements in residues of the binding pocket [ 18 , 19 ], and even structural transitions that involve opening/closing of otherwise rigid structural elements of the protein about flexible joints. For these reasons, it is always useful to compare holo- and apo-structures of a protein of interest whenever possible. Although most contemporary docking approaches treat ligands as flexible, it remains a challenging task to incorporate protein flexibility into the docking regime. A thorough analysis of side chain flexibility may provide invaluable insights for improving docking run and for optimizing protein-ligand interactions. Despite some recent advancements in considering protein side-chain flexibility in optimizing simulation of protein-ligand interactions, protein flexibility remains one of the most important factors in improvement of methods for docking ligands to their flexible protein partner [ 20 ].

Considering the role of water

H 2 O molecules play myriad roles in biological structure and functions. The importance of structured water molecules in biological systems cannot be overstated given their critical roles in modulating protein–ligand interactions, and these considerations take center stage in the context of drug design and discovery [ 21 ]. When a structured water molecule is displaced by a ligand and banished to “bulk” solvent, the act of displacement increases system entropy and helps drive ligand binding. That is, ligand binding is thermodynamically more favorable if the ligand displaces a tightly bound water molecule by replicating its interaction with protein [ 22 ]. For protein-ligand complexes, many water molecules are retained in the active site and contribute to the energetics of protein∷ligand interactions independent of entropic considerations. For example, waters can bridge protein and ligand and license what would otherwise represent unfavorable interactions between two chemically incompatible groups (e.g. two bases). Water molecules can also alter the “shape” and microenvironment of the active site by tightly associating with specific residues and thereby present a steric and electrostatic binding pocket profile that is different to the one presented by an anhydrous active site [ 23 , 24 ]. These varied functional involvements of water define yet another set of important considerations that must be respected in quality docking experiments and in rational design of high affinity lead molecules. Accessible surface areas of water molecules, the hydrogen bonds that involve water, the conservation and/or displacement of water, as well as the interaction energetics of water molecules are some of the factors that must be considered in docking simulations. The reality is that contemporary state-of-the art docking algorithms, and the scoring functions that accompany them, do not adequately consider all the explicit and implicit contributions of water molecules to the binding equation. Nonetheless, several docking routines include methods for identifying relevant water molecules and including those contributions in pose generation and in calculating free energies of ligand binding [ 25 ].

Protonation and ionization states of binding site residues

In addition to managing issues associated with protein flexibility and solvent, both the computational intensities and uncertainties of the docking problem are compounded for protein∷ligand systems with variable ionization states, and contributions of metals and counter ions [ 26 ]. Protein ligand interactions are sensitive to subtle changes in microenvironment of the binding site. Change in pH, buffer, ionic strength, and temperature conditions under which the data are collected also affect the microenvironment of an active site [ 27 ]. Protonation states of active site residues are typically not well-assigned, even in high resolution X-ray crystal structures, and therefore present little information to prepare the structure for docking [ 28 ]. Moreover, protein crystals are typically solvent rich (30–70%)-values that often include the crystallization buffer [ 29 ]. The accompanying ions and solvent molecules are distributed throughout the protein molecule in accord with the electrostatic properties of the solvent-accessible pockets. Altering ambient pH often alters the ionization states of residues and thereby influences the shape and electrostatic properties of the binding pocket, and ultimately the set of ligand-binding solutions [ 30 ]. Multiplicity of protonation states in ligand–protein complexes is an often overlooked aspect in protein structure preparation as emphasized by the fact that current modeling techniques frequently ignore the possibility of multiple protonation states.

There is recent progress on this front, however. New algorithms such as the computational titration protocol implemented in Hydropathic Interaction (HINT) seek to identify and optimize all possible protonation states so that rational models with atomic details can be constructed and applied to model ligand-binding energetic [ 26 , 30 , 31 ]. By modeling all ionizable residues in the binding pocket, and calculating all the possible protonation states of residues and functional groups within the active site, the computational-titration methodology realistically samples the dynamic behavior of labile H-atoms in the active site microenvironment. In particular, an important aspect of the active site microenvironment that is often ignored is the dielectric constant within the active site [ 32 , 33 ]. While comprehensive estimations of polarizability and binding energies are computationally expensive endeavors, simplified models that use macroscopic dielectric models, either uniform or distance-dependent, are being productively applied to descriptions of binding site microenvironments [ 34 , 35 ]. The message is that accurate prediction of binding free energies requires that pH, ionization and entropic contributions be taken into account in docking and virtual screening experiments.

Entropic considerations, as well as the contributions of hydrophobicity, in ligand binding cannot be overstated but are often poorly characterized and poorly quantified [ 36 , 37 ]. Entropy and hydrophobicity are difficult to measure and therefore difficult to computationally model. It is for this reason that these parameters are sacrificed in favor of computational efficiency. Most approaches consider enthalpic and entropic contributions separately and sum these interactions to a cumulative score [ 38 ]. However, protein-ligand binding is a concerted event, and entropy and hydrophobicity are thermodynamic quantities which cannot be accurately described by a simple summation. Solvation and desolvation effects that involve hydrophobic interactions are significant factors in protein∷ligand interactions but are particularly difficult to model computationally. But, the effort is worthwhile. Docking simulations that adequately consider the entropic, solvation/desolvation, and thermodynamic components of a binding reaction yield information whether the binding is enthalpy- or entropy driven and provide vital insights into the free-energy changes in the system [ 39 – 43 ].

Finding the right ‘key’

Once the ‘lock’ is defined (i.e boundary and interacting features within the binding pocket are delineated) the next core issue is to find a suitable key for the lock. To accomplish this task, the first step is fitting the ligand (key) into the binding pocket (key hole) and finding the best fit. That effort involves sampling different ligand conformations and orientations within the binding pocket and measuring the fitness of different alternative poses to identify the most favorable fit. Thus, docking approaches share two components: (i) a search algorithm that generates a sufficient set of different poses so that it exhaustively samples nearly all possible conformations and orientations for a ligand, and (ii) a scoring algorithm which evaluates the generated poses, approximates their binding energies, and identifies an optimal binding pose(s). Several different search algorithms have evolved over the past decades that were based on a variety of computational approaches [ 44 – 47 ]. Interestingly, the evolution of computational docking approaches offers interesting parallels to the evolution of thought from ‘lock and key’ to ‘induced fit’ hypotheses. Several approaches, with different degrees of sophistication, evolved from ‘rigid body’ considerations to ‘flexible ligand’ docking methods, and are still evolving into ever more sophisticated and computationally intensive ‘flexible-ligand and flexible receptor’ methods [ 48 – 51 ]. In rigid body approaches both the receptor and ligand are treated as static units and search algorithm tries to orient a rigid ligand within a rigid binding pocket [ 52 – 54 ]. Flexible-ligand methods treat the receptor (protein) as a rigid entity, but impart flexibility to the ligand and explore different conformations in systematic or random stochastic manners [ 48 – 51 , 55 ]. By contrast, ‘flexible-ligand and flexible-receptor’ approaches treat both receptor and ligand as flexible entities [ 56 – 59 ]. Despite the significant progress made in flexible protein-ligand docking, significant improvement is still needed.

One of the earliest docking approaches involved systematic search logic [ 60 , 61 ]. However, the search becomes ever more complex with increasing ligand flexibility as the number of degree of freedom of the ligand molecule obviously increases. Such an approach was implemented in methods where ligand and binding pocket were considered to be rigid and ligand was fitted using shape complementarity as determined by point complementarity or distance geometry approaches [ 62 , 63 ]. In such docking methods, the shape of both the receptor site and the ligand is interrogated based on criteria of shape and pharmacophoric points. Orientations are generated through various alignment procedures in order to maximize the pharmacophoric constraints and shape complementarity. However, it is not feasible to exhaustively explore available conformational space, and an acceptable balance has to be struck between speed and accuracy so that as many binding modes can be explored as is feasible. Fragment-based approaches that involve either incremental construction of ligand in the binding pocket, or by simply placing and joining the fragment, circumvent problems associated with combinatorial explosion of conformers generated by the previous approaches [ 64 – 66 ].

Stochastic methods involving random sampling of conformational space of ligand in the binding pocket are also being widely applied in many docking algorithms. Algorithms using Monte Carlo sampling, coupled with Metropolis criterion, are applied to exhaustively interrogate the conformational space [ 67 ]. Simulated annealing protocols, combined with grid-based energy evaluations, can be coupled with such an approach to overcome high conformational energy barriers in the sampling regime [ 68 ]. Another such stochastic approach that has been successfully implemented in docking algorithm is the genetic algorithm-based sampling of conformational space [ 69 – 71 ]. In this approach, multi-conformers referred as chromosomes are evaluated, crossed and mutated and the best possible solution is selected based on a fitness function. The ultimate solution is represented by the best scored conformation of the total conformers after a suitable number of generations. GOLD (Genetic Optimization for Ligand Docking) is the most widely used algorithm of this type for flexible molecular docking [ 72 ].

In contrast to systematic and stochastic approaches, molecular dynamics-based and heuristic tabu searches are also implemented to explore the sample space [ 73 , 74 ]. However, molecular dynamics is computationally expensive which restricts its use in docking. To circumvent the problem of exhaustive sampling, tabu search approaches are adopted where a list of already explored conformations is maintained and only unexplored spaces are sampled [ 75 ]. This avoids reinvestigating space already sampled by associating previously sampled conformations with a degree of penalty. Apart from these deterministic approaches, hybrid consensus logic combine features from other two approaches [ 76 , 77 ]. Although these approaches can exhaustively generate and sample all possible conformations within the active site, it remains a fact that the success of any docking program is measured by how well it reproduces experiment.

The success of whole molecule docking, de novo construction of molecules into a target site, or screening large virtual combinatorial libraries is ultimately dependent on the accuracy of the scoring function that ranks the compounds. Ligand orientations can be evaluated on the fly as the ligand or fragment is positioned within the cavity, or all the generated poses can be scored in the end. The scoring methods that are used in high throughput settings i.e. that deal with thousands of diverse compounds, can be evaluated by how well the corresponding relative binding affinities can be predicted. That need has spurred development of multiple methods which can be subdivided in four major approaches: force field-based methods, semi-empirical approaches, empirical scoring methods, knowledge-based potentials, and consensus scoring functions that are a combination of multiple scoring functions [ 78 – 80 ].

Force field-based methods

Force field-based scoring methods generally use a molecular mechanics force field. This parameter contains terms for intramolecular forces (e.g. bond, angle and dihedral terms) between atoms bonded to each other, plus energy terms for intermolecular forces that describe the forces between non-bonded atoms (e.g. Van der Waals and Coulombic terms). There are also a number of widely and successfully applied molecular mechanics-based scoring functions [ 81 – 84 ]. Their popularity in virtual screening programs is a reflection of their simplicity. Though faster and simpler, these functions are not ideal for simulating biomolecular interactions as those methods were developed for calculating gas phase enthalpy of binding. Thus, this class of scoring approaches has many drawbacks, primarily that these ignore hydrophobic interactions, and solvation and entropic effects.

Empirical scoring methods

Empirical scoring methods offer an alternative approach to pure molecular mechanics-based force field scoring methods [ 85 ]. The principle is that the binding free energy of a non-covalent protein-ligand complex can be factorized into a sum of localized and chemically intuitive interactions. The terms accounting for different contributions such as hydrogen bonds, hydrophobic interactions, entropic effects are normalized by weighting factors derived from regression analyses of data from training sets comprised of well characterized protein-ligand complexes. Based on the assumption of additivity, the binding affinity is estimated as a sum of interactions multiplied by weighting factors and solved by equation of the type ( 1 ):

Where fi is a simple geometrical function of the ligand (rl) and receptor (rp) coordinates [ 6 ]. However, accuracy of these methods depends upon the quality of the experimental binding data and of the crystallographic structural data of the training set.

Semi-empirical approaches

Semi-empirical scoring functions combine the above two approaches and incorporate empirical, or empirically calibrated, energetic terms for interactions that cannot be computed by pure molecular mechanics-based methods. Thus, implicit binding energy terms such as hydrogen bonding, solvent effects, hydrophobicity and entropic terms are included in the scoring functions. In contrast to force field-based scoring functions, semi-empirical scoring terms also more accurately estimate binding energies by accounting for entropic and solvation effects known to significantly affect biological interactions in aqueous medium [ 86 – 89 ].

Knowledge-based scoring

Knowledge-based scoring functions [ 90 ] are rule-based regimes where rules are derived from the analysis of structural data of known and well characterized receptor-ligand interactions. The exponential growth and availability of protein-ligand crystal structures is enabling derivation and formulation of rule sets based on frequencies of chemical interactions. Scoring functions of this type seek to capture the knowledge about protein-ligand binding that is implicitly stored in the protein data bank by means of statistical analysis of structural data. That is, potentials are obtained by statistical analysis of atom-pairing frequencies observed in crystal structures of protein-ligand complexes [ 91 ]. Again, the accuracy of knowledge-based scoring function depends on the quality of experimental data, as it incorporates structural knowledge without considering inconsistencies in experimental and structural data.

Consensus scoring

Although multiple approaches have been implemented for derivation of a robust scoring function, none of the scoring functions are ideal. Invariably, various approximations are made to strike a balance between speed and accuracy. Taking into consideration the limitations of anyone scoring function, the concept of consensus scoring evolved from the base premise that a combination of different scoring functions will buffer inherent weaknesses in individual functions and offer better performance [ 92 ]. A consensus between a set of scoring functions can be reached either by averaging the rank assigned by each scoring function, or averaging the score value calculated by different functions. Ideally, the best scoring function should be able to discriminate between native and non-native binding modes and be able to calculate the actual free energy of binding.

Combination Lock and Key

Traditional docking approaches largely operate on ‘lock and key’ concepts, and this philosophy has enjoyed some successes in estimating the native binding poses of small molecule ligands. A variety of sophisticated approaches have come on-line in recent years that consider conformational flexibility for both ligand and protein [ 93 ]. However, the fact remains that both ‘lock and key’ and ‘induced fit’ approaches provide a simplistic views of ligand-binding phenomena that in actuality represent intricate molecular recognition/interaction processes. For this reason, we prefer to view protein-ligand recognition and binding reactions in terms of a ‘combination lock’ system ( Figure 1 ). In this scenario, a tandem combination of complementary features provided by both the protein and the ligand match as in case of a ‘combination lock’. Upon satisfying a suitable combination of features a binding event then ensues. For matching to occur, both feature variables on protein and ligand fine-tune and adapt in a search for the best complementarity. That is, the better the feature matching the tighter the binding. The questions then come to: (i) what are these features, (ii) how are these features encoded in the three-dimensional structure, and (iii) how is the three-dimensional feature code decoded by binding partners? The features could be geometric properties based on the three-dimensional structure of the molecule (e.g. shape, size, volume, surface area, etc.) and/or physicochemical features described by intrinsic electronic properties of a molecule (e.g. electrostatic, hydropathic and van der Waals energetic components). While the energy-based features are more dynamic in nature, and manifest themselves in three-dimensional interaction fields, the geometry-based properties are static in character. It is the sum of pharmacophoric chemical features (e.g. hydrogen bond donor/acceptors, aromatic centers, etc.), geometric features, and intrinsic electronic features of the molecules that define unique interaction fingerprints. The spatial arrangement of these various properties is a particularly discriminating property as electronic, hydropathic and van der Waals energetic properties have varying intensities in three-dimensional space and thereby form unique fields the strength of which vary from point to point and are distance dependent. The patterning of these feature sets in three-dimensional space forms the essence of molecular recognition.

Using the ‘combination lock’ concept, the essential challenge in developing the next generation of robust and predictive docking model is to accurately derive the critical interaction features and map their arrangement in three-dimensional space. These encoded features and properties must first be extracted to define exclusive ‘interaction fingerprints’ for both a ligand binding substructure on the receptor and for the ligand. These unique features and ‘interaction fingerprints’ can be stored as mathematical representations in two- or three-dimensional matrices. Subsequently, machine learning and feature matching algorithms can extract the relevant features and simulate the corresponding protein-ligand binding interactions [ 94 , 95 ]. Features extracted from physical-chemical properties and energies will have broad applicability in deriving target-focused docking and scoring in addition to developing regimes for generating target-focused libraries in silico ( Figure 3 ).

Schematic of ‘Combination Lock’ hypothesis based on feature matching. Protein and Ligand’s physicochemical properties are mapped and relevant binding features are extracted. Matching the combination of best complementary features between protein and ligand ensues optimal fit.

The availability of substantially more protein-ligand complex data and robust machine learning algorithms suggests that feature matching methodology may now be even more effective approach to predict and characterize protein-ligand binding. Recently, a combination of structure-based QSAR approach was implemented to generate descriptive and predictive models for phosphodiesterase-4 inhibitors [ 96 ]. This approach applies machine learning methodology to describes protein-ligand binding based on matching of ligand pharmacophore feature pairs with those of the target binding pocket. The method takes advantage of structure of binding pocket to derive feature sets or descriptors which is used as a reference for matching and makes it unique and target specific. Similar feature sets are generated for ligands followed by generation of structure-based pharmacophore key (SBPPK) from the protein-ligand complex based on their feature matching patterns with the binding pocket. Once the feature pairs are generated for both the receptor and ligands machine learning methods can be employed to determine pattern matches to build descriptive and predictive models of protein-ligand interactions. The method was successfully applied to study the SAR (Structure Activity Relationship) of 35 PDE-4 inhibitors. In another similar approach, atom based Interaction Fingerprint (IF) were applied to describe the patterns of ligand pharmacophores that interacted with proteins in complex [ 97 ]. These fingerprints are calculated from the distance of pairs of ligand pharmacophore features that interact with protein atoms delineating important geometrical patterns of ligand pharmacophores. From a physicochemical and pharmacological perspective, the detected patterns of ligand features would facilitate an understanding of the structure-activity relationship of the protein-ligand interactions. The method further allows a comparison of the interaction patterns of a target with those of several other targets and facilitates in sillico screening against other homologous proteins. Some of these approaches are applied as a pre-screen and to filter large databases of small molecules before they are actually docked into the protein binding pocket. This database filtering procedure was applied to virtually screen HIV protease inhibitors from ZINC database [ 98 ]. The method involved identification of binding site topology and generating site interaction points based on physicochemical property. The resultant functional/interaction properties are saved as a receptor site’s distance matrix. Similar to receptor site distance matrix, functional interaction points are located in small molecule ligand and a similar topological matrix is generated. The methodology can be seen as a comparison and matching of the ligand’s distance matrices with receptor’s matrices. Overlay and matching of receptor and ligand site matrices with each complementary pair, describes ligand’s functionalities mapped onto receptor’s binding pocket. Similar matrices can be generated for small molecules and large databases can be screened as comparing the matrices is a simple matter of matching each molecule’s distance matrix with the one generated from the protein’s binding pocket. The high proportion of known active compounds recovered in the top ranks along with target specificity signifies a promising future for the feature matching approaches for virtual screening. Such hybrid QSAR, machine learning approach that take into account ligand features as well have been applied and benchmarked against traditional rigid body docking methods and affords similar or better enrichment ratios in virtual screening [ 99 – 102 ]. We suggest that ‘combination lock’-driven approaches better capture the complex inter-relationships between feature properties of interacting biomolecules, and that implementation of such approaches will herald significant progress in our ability to model protein-ligand binding events with superior accuracy.

A primary aim of structure-based drug design is to adequately describe the binding interactions between a drug and its target. Traditionally, and perhaps in a tired analogy, protein-ligand binding is treated as a ‘Lock and Key’ system. Although pioneering studies in flexible docking and free energy calculation are making significant progress towards improving the accuracy of docking and virtual screening regimes these technologies remain complex, are time consuming and, for a variety of reasons, still suffer errors. Paradigm shifts in docking and scoring regimes are being driven by the evolution of artificial intelligence and machine learning algorithms for pose scoring and evaluation. With the availability of experimental binding data from bioactivity databases the molecular docking field is witnessing the emergence of hybrid approaches that combine ligand-based and structure-based approaches. Some of the current methods extend ligand-based machine learning strategies and principles in the direction of structure-based approaches. Based on feature extraction and correlation with crystallographic and bioactivity data, robust predictive models can now be generated complementing structure-based approach. Such hybrid ‘Combination Lock’ approaches are evolving technology and albeit with number of limitations, holds great promise for future progress in drug discovery and development.

Acknowledgments

This work was supported by grants GM44530, GM112591 from the National Institutes of Health and BE- 0017 from the Robert A. Welch Foundation (VAB). We also extend our thanks to The Laboratory for Molecular Simulation and High Performance Research Computing (HPRC) at Texas A&M University for providing software, support, and computer time.

Encyclopedia of Biophysics pp 1–6 Cite as

Molecular Recognition: Lock-and-Key, Induced Fit, and Conformational Selection

• Norman Tran 4 &
• Todd Holyoak 4  
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• First Online: 27 October 2020

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In the most general sense, molecular recognition is the mechanism by which two or more molecules come together to form a specific complex. These types of molecular interactions are widespread throughout biology and include diverse processes such as enzyme catalysis, antibody–antigen recognition, protein synthesis, receptor–ligand interactions, and transcriptional regulation, to name a few. Because of the universal importance of molecular recognition in biological function, understanding how molecules unambiguously recognize and interact with one another is fundamentally important to appreciating biological systems as a whole.

Introduction

Just as the field biochemistry grew out of the study of biological fermentation, much of the field of molecular recognition grew out of the study of enzyme selectivity (Voet and Voet 2004 ). Early studies led to the conclusion that substrates combine with enzymes at a specific location on each enzyme’s surface. These conclusions generated...

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Tran, N., Holyoak, T. (2021). Molecular Recognition: Lock-and-Key, Induced Fit, and Conformational Selection. In: Roberts, G., Watts, A. (eds) Encyclopedia of Biophysics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35943-9_468-1

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Introduction to enzymes and their applications

Saurabh Bhatia Published September 2018 • Copyright © IOP Publishing Ltd 2018 Pages 1-1 to 1-29

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Enzyme catalysis is an area of fundamental importance in different areas. This chapter offers a concise overview to the fundamental principles and mechanisms of action, catalysis inhibition and its pharmaceutical applications. Additionally, this section also covers basics information related with enzymes such as its structure, function and different properties.

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1.1. Introduction

The cell is the structural and functional unit of life—the basic building block of living systems. Cells have the capability to effectively utilize biocatalysts, known as enzymes, which have outstanding catalytic efficiency and both substrate and reaction specificity. Enzymes have amazing catalytic power and their high level of specificity for their substrate makes them suitable for biological reactions. They are crucial for cellular metabolism. Each and every chemical reaction that takes place in plants, micro-organisms and animals proceeds at a quantifiable rate as a direct result of enzymatic catalysis. Most of the history of biochemistry is directly or indirectly related to the history of enzyme research. Catalysis in biological systems was initially reported in the early 1800s based on research into the digestion of meat. In this report the catalytic activity of secretions from the stomach, the conversion of starch into sugar by saliva, and various plant extracts were reported.

In 1837, Berzelius documented the catalytic nature of fermentation. In the 1850s Louis Pasteur reported that fermentation was a process initiated by living organisms. During this study it was reported that the fermentation of sugar into alcohol by yeast was catalyzed by ferments. He also hypothesized that these ferments are close to the structure of yeast. These ferments were later called enzymes (in yeast). The key breakthrough in the history of enzymes came in 1897 when Edward Buchner isolated, from yeast cells, the soluble active form of the set of enzymes that catalyzes the fermentation of sugar to alcohol. Emul Fischer reported the first systematic studies on enzyme specificity in the early twentieth century [ 1 ]. Later, in 1926, James Sumner extracted urease in pure crystalline form from jack beans [ 2 ]. He also recognized the protein nature of urease . In 1930, John Northrop and his co-workers crystallized pepsin and trypsin and established them as proteins [ 3 ]. In subsequent years enzymology developed rapidly (table 1.1 ). The important developments during this period are: the elucidation of major metabolic pathways, such as the glycolysis and tricarboxylic acid cycle; the detection of numerous biochemical events of digestion, coagulation, muscular contraction and endocrine function, and their roles in the maintenance, control and integration of complex metabolic processes; the kinetic backgrounds to explain the observations of enzyme action and inhibition; and the development of protocols for examining the structures of functionally sensitive proteins. There has been exhaustive research on enzyme-catalyzed reactions and enzymes involved in cell metabolism. At present, 2000 different enzymes have been recognized, each of which catalyzes a different chemical reaction. Currently, more focus is being directed towards the application of enzymes. The high efficiency of enzymes makes them commercially valuable and their specificity of action is offering diverse advantages in clinical medicine.

Table 1.1.   Chronology of enzyme studies.

1.2. Properties of enzymes

Enzymes are the complex protein molecules, often called biocatalysts, which are produced by living cells. They are highly specific both in the reactions that they catalyze and in their choice of reactants, which are known as substrates. An enzyme typically catalyzes a single chemical reaction or a set of closely related reactions [ 4 ]. Side reactions resulting in the wasteful formation of by-products are rare in enzyme-catalyzed reactions, in comparison to uncatalyzed ones. Enzymes can also be defined as soluble, colloidal and organic catalysts that are produced by living cells, but are capable of acting independently of the cells [ 4 ]. Enzymes are currently being used in diverse areas in the food, feed, paper, leather, agriculture and textiles industries, resulting in major cost reductions. Simultaneously, rapid scientific progress is now encouraging the chemistry and pharmacological industries to embrace enzyme technology, a trend supported by concerns regarding energy, raw materials, health and the environment. One of the most common advantages of enzymes is their ability to function continuously even after their removal or separation from the cells. This means that even after the separation of cells from in vivo environments, they continue to work efficiently under in vitro conditions; we can conclude that these biocatalysts remain in an active state even after their isolation. Principally, enzymes are non-toxic, biodegradable and can be produced in ample amounts by micro-organisms for industrial applications. In this chapter, the isolation, production, purification, utilization and application of enzymes (in soluble and immobilized or insoluble form) are discussed in detail. Procedures such as recombinant DNA technology and protein engineering are frequently used to produce more efficient and beneficial enzymes. The industrial production and utilization of enzymes is an important part of industry. Interdisciplinary collaboration between areas such as chemistry, process engineering, microbiology and biochemistry is required to develop the best possible enzyme technology, and eventually to achieve increased production and maintain the enzyme's physico-chemical properties under in vitro environments.

For catalytic action, small quantities of an enzyme are sufficient, where this quantity of enzyme is much smaller in comparison to its substrates. The overall concentration of substrate transformed per mass of enzyme is often very large. Without exception, all enzymes are proteinaceous and exhibit all the properties of a protein. The treatment of enzymes by extreme temperature or extreme pH, or by treatment with other denaturing agents, results in the complete loss of catalytic activity. Structural configurations such as the primary, secondary, tertiary and quaternary structures of enzyme proteins are essential for their catalytic activity. The degree of catalytic activity chiefly depends on the integrity of the enzyme's structure as a protein. As per reports, enzymes have molecular weights ranging from about 12 000 to over 1 million Da. A number of enzymes consist only of polypeptides and contain no chemical groups other than amino acid residues, e.g. pancreatic ribonuclease. Numerous enzymes require a specific, heat stable, low molecular weight organic molecule, known as a co-enzyme. Moreover, a number of enzymes require both a co-enzyme and one or more metal ions for activity. A complete biochemically active compound is formed by the combination of a catalytically active enzyme (also called the protein part) with a co-enzyme or a metal ion—this is called a holoenzyme. The protein part of a holoenzyme is called an apoenzyme. In this arrangement a co-enzyme may bind covalently or noncovalently to the apoenzyme. In certain enzymes the co-enzyme or metal ion is only loosely and transiently bound to the protein. However, in others it is tightly and permanently bound, in which case it is known as a prosthetic group. A prosthetic group signifies a covalently bound co-enzyme. According to reports, co-enzymes and metal ions are stable under heating, while the protein part of an enzyme (the apoenzyme), is denatured by heat.

Prosthetic groups may be classified functionally into two major classes: co-enzymes and co-factors. Co-enzymes may be considered to be biosynthetically related to the vitamins, such as the co-enzyme nicotinamide adenine dinucleotide (NAD) which is vital for cellular energy metabolism and integrates the vitamin niacin into its chemical makeup. Moreover, a co-enzyme may be considered as a co-substrate, experiencing a chemical transformation throughout the enzyme reaction (NAD is reduced to NADH), the reversal of which requires a separate enzyme, perhaps from a different cellular site. Co-enzymes might thus travel intra-cellularly between apo-enzymes and, by transferring chemical groupings, integrate several metabolic processes. Table 1.2 shows a list of the more common co-enzymes and their functions. In contrast to co-enzymes, co-factors, such as pyridoxal phosphate or hem groups, remain with one enzyme molecule and in conjunction complete a cycle of a chemical change brought about by one enzyme turnover [ 5 ]. Other enzymes, such as carboxypeptidase, require metal ions as co-factors, the divalent cations Mg 2+ , Zn 2+ and Mn 2+ being the most common; these are often called enzyme activators [ 6 ]. Table 1.3 lists several enzymes and their respective co-factors.

Table 1.2.   Several co-enzymes employed in the transfer of specific atoms or functional groups.

Table 1.3.   Several enzymes and their co-factors.

1.3. Catalysis

The role of a catalyst is to increase the speed of a chemical reaction. When the rate of a chemical reaction is governed by a soluble catalyst, which may result in a further increase in the rate of chemical reaction, it is called homogeneous catalysis. In this case catalysis occurs in a solution. When the catalyst is in a separate phase from the reactants, or when catalysis occurs on a insoluble surface or an immobilized matrix, it is known as heterogeneous catalysis. Enzymes are also called biological catalysts. These biological catalysts generally have the properties of homogeneous catalysts, however, a number of enzymes present in membranes are insoluble, and thus are called heterogeneous catalysts. Enzyme specificity is the absolute specificity of protein catalysts to identify and bind to only one or a few molecules. In this process the enzyme carries a defined arrangement of atoms in their active site to bind with the substrate. This active site on the enzyme should have a shape that accurately matches the substrates. Thus specificity is achieved when an enzyme with an active site binds with the chemical reactants (the substrates) at their active sites via weak bond interactions. To undergo a chemical reaction, this active site carries certain residues that form a temporary bond with the chemical reactants, termed the binding site, whereas the catalytic site carries the residues that are responsible for catalysis. Specificity is achieved when a substrate binds to an enzyme that has a defined arrangement of atoms in the active site. An enzyme always catalyzes a single type of chemical reaction, which involves the formation and breakdown of covalent bonds. Since they are specific to one particular reaction, this feature of enzymes is called reaction specificity, also known as absolute reaction specificity, i.e. no by-products are formed.

1.4. The structure of enzymes

Enzymes always act as catalysts and small quantities compared to their substrate are required to considerably increase the rate of chemical reactions, wherein the enzymes themselves experience no overall change [ 7 , 8 ]. In contrast to all true catalysts, an enzyme does not alter the ultimate equilibrium position of a reaction, which is thermodynamically determined, thus merely the rate of completion of equilibrium of a feasible reaction is augmented. In addition to catalytic properties, enzymes exhibit the physico-chemical behavior of proteins: their solubility, electrophoretic properties, electrolytic behaviors and chemical reactivity [ 7 , 8 ]. The primary structural configuration and catalytic action of enzymes is determined by the linear chain of amino acid residues linked via peptide bonds, which constitute a protein molecule. Localized folding of the primary structure is called a secondary structure, whereas the complete folding of the molecule is known as a tertiary structure. In contrast to these structural configurations, a quaternary structure is the agglomeration of several folded chains. The structural features of enzymes are shown in figures 1.1 and 1.2 . In contrast to traditional chemical catalysts, e.g. hydrogen ions, heavy metals or metal oxides, which are most effective in organic solvents, at very high temperatures or at extreme pH values, enzymes operate most efficiently under very mild conditions. When using enzymes, there are certain issues that require attention, such as deviation from homogeneous aqueous solutions, physiological pH and temperature, which can rapidly destroy enzyme activity. However, under normal conditions the increase in reaction rate is rarely matched by their non-protein counterparts.

Figure 1.1.  Structural features of enzyme.

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Figure 1.2.  Principle components of an enzyme.

1.5. Structural features: primary and secondary structures

Three-dimensional analysis of the amino acid sequence of lysozyme of hen's egg white has demonstrated some features essential for primary structure [ 9 , 10 ]. These are:

• •   Molecules derived from a similar source have a similar order of amino acid residues and appear to be random with no obvious predictability.
• •   Even though numerous enzymes are intramolecularly crosslinked via disulfide bridges of cysteine, no branching occurs.

Current databases suggest that a small number of amino acids are extra and most are 'functional', i.e. the majority of them co-operatively control the higher orders of structural organization and therefore the catalytic activity. When comparing the primary structures of enzymes performing similar functions, wide structural homologies are detected in their sequence, mainly in the patterns of their nonpolar residues. For example, pancreatic juice contains five inactive precursors (zymogens), namely chymotrypsinogen A, B and C, trypsinogen and proelastase; all of these are activated to the respective proteases by proteolytic cleavage [ 11 ].

1.6. The mechanism of action of enzymes

The mechanism of action is based on a chemical reaction, in which the enzyme binds to the substrate and finally forms an enzyme–substrate complex. This reaction take place in a relatively small area of the enzyme called the active or catalytic site. In other words, the mechanism of enzyme action is based on the nature of the enzyme–substrate interaction, which accounts for the reaction specificity of the biological catalysts. The active or catalytic site of an enzyme is constituted by several amino acids, located at some distance from each other in the peptide chain. These amino acids are brought close together by the folding resulting from the secondary and tertiary structure of the enzymes. Side chains of amino acid residues at the catalytic site provide groups for binding with specific groups of the substrate. Co-factors assist the catalysis. The substrate forms bonds with amino acid residues in the substrate binding domain of the active site. The binding induces a conformational reaction in the active site. During the reaction, the enzyme forms a transition-state complex. As the products of the reaction disassociate, the enzyme returns to the original state. Two different models postulated for the mechanism of enzyme action are given below.

1.6.1. The Fisher template model (lock and key model)

This is a rigid model of the catalytic site, proposed by Emil Fischer in 1894 [ 12 ]. The model explains the interaction between a substrate and an enzyme in terms of a lock and key analogy. In this model, the catalytic site is presumed to be preshaped. The substrate fits as a key fits into a lock. The drawback of this model is the implied rigidity of the catalytic site. The model cannot explain changes in enzyme structure in the presence of allosteric modulators.

1.6.2. Induced fit model

In contrast to the above method, this model suggests a flexible mode for the catalytic site. To overcome the problems of the lock and key model owing to the rigid catalytic site, Koshland [ 13 – 15 ] suggested an induced fit model in 1963. The important feature of this procedure is the flexibility of the active site. In the induced fit model, the substrate induces a conformational change in the active site of the enzyme so that the substrate fits into the active site in the most convenient way so as to promote the chemical reaction. This method suggests competitive inhibition, allosteric modulation and inactivation of enzymes on denaturation.

The Michaelis–Menten theory of enzyme action [ 16 ] offers the basis for most current research on the mechanism of enzyme action. This concept of the enzyme–substrate complex scheme assumes the combination of the enzyme and substrate in phase one (occasionally known as the transition phase) of the enzyme activity and liberation of the enzyme and the products of the catalysis in phase two of the reaction.

1.6.3. Covalent catalysis

Covalent catalysis is evidenced in enzymes capable of forming covalent bonds between the substance and the catalytic group of the active site [ 17 ]. A number of enzymes react with their substrates to form very unstable, covalently joined enzyme–substrate complexes, which undergo further reaction to yield products much more readily than in an uncatalyzed reaction. Several of the enzymes that exhibit covalent catalytic behavior are listed in table 1.4 .

Table 1.4.   Various enzymes exhibiting covalent catalytic behavior.

1.7. Catalysis via chymotrypsin

Hummel and Kalnitzky suggested an enzyme mechanism through the depiction of the sequential transition states experienced by the enzyme–substrate complex during catalysis [ 18 ]. Chymotrypsin is a digestive enzyme, responsible for proteolysis (breakdown of proteins and polypeptides) in the duodenum. Chymotrypsin favorably breaks peptide amide bonds (the carboxyl side of the amide bond is a large hydrophobic amino acid). These amino acids contain an aromatic ring in their side chain that fits into a 'hydrophobic pocket' of the enzyme. It is stimulated in the presence of trypsin. Trypsin and chymotrypsin are both serine proteases with high sequence and structural similarities, but with different substrate specificity [ 19 , 20 ].

1.7.1. Intermediary stages of chymotrypsin

As discussed above, chymotrypsin is a protease enzyme that cuts on the C-terminal phenylalanine, tryptophan and tyrosine on peptide chains [ 21 ]. Additionally, it is more specific for aromatic amino acids because of its hydrophobic pocket. Comparable to other serine proteases, chymotrypsin also catalyzes the hydrolysis of certain esters [ 22 ]. The molecular events involved in catalysis are called intermediary enzymology. Chymotrypsin, a protease, favorably accelerates breakdown of peptide bonds in which the aromatic amino acid (Phy, Try, or Trp) or bulky nonpolar R group (Met) contribute a carboxyl group. The synthetic substrate p-nitrophenyl acetate allows colorimetric analysis of chymotrypsin activity, as hydrolysis to p-nitrophenol, which is alkali, changes into the chromophore anionic forms.

1.7.2. Kinetic behavior of α -chymotrypsin

The kinetics of chymotrypsin of p-nitrophenyl acetate can be considered in a 'stop-flow' apparatus. This procedure utilizes substrate quantities of enzymes and measures the events in the first few milliseconds [ 23 ]. The use of p-nitrophenyl acetate as a substrate offers the prospect of investigating solvent effects on both the acylation of the enzyme and the hydrolysis (deacylation) of the acyl enzyme [ 23 ]. The significant features of the slow-flow kinetics of chymotrypsin are:

• - a burst phase featuring rapid liberation of an anion.
• - a subsequent 'steady-state' phase, with slower release of extra anion.
• •   A 'charge relay network' acts as a proton shuttle during catalysis by chymotrypsin. The charge relay network of chymotrypsin encompasses three aminoacyl residues that are far apart in a primary structural sense, but close together in a tertiary structural sense. While most of the charged residues of chymotrypsin are present at the surface of the molecule, those of the charge relay network are hidden in the otherwise nonpolar inner side of the protein. These charges transmit residues which activate sequential proton shifts that shuttle protons in the opposite direction. An equivalent series of proton shifts is assumed to accompany the hydrolysis of the physiologic chymotrypsin substrate, e.g. a peptide.

1.7.3. Selective proteolysis in creation of the catalytic sites of enzymes

Various enzymes, hormones and other physiologically active proteins are produced as inactive precursors (zymogens) that are further transformed to the active form by selective enzymatic cleavage (limited proteolysis) of peptide bonds. The final step to activating enzymatic function is limited proteolysis, either in a single activation step or in a consecutive series (cascade). The specificity of each activation reaction is evaluated by the complementarity of the zymogen substrate and the active site of the attacking protease. The arrangement of successive activation reactions is controlled by the specificity of each enzyme, while the extent of amplification of the initial stimulus is evaluated by the effectiveness of each activating step. Zymogen activation produces a prompt and irreversible response to a physiological stimulus, and is capable of initiating new physiological functions. Classical examples are the processes of hormone production, fibrinolysis, complement activation, blood coagulation, supra-molecular assembly, metamorphosis, fertilization and digestion. The zymogens of the pancreatic serine proteases, in particular, have functioned as models for detailed studies of the nature of the molecular changes that are involved in the intense increase in enzymatic activity that results upon incomplete proteolysis of the zymogen.

Specific proteolysis is a common means of activating enzymes and other proteins in biological systems. A number of proteins are manufactured and released in the form of inactive precursor proteins called proproteins. Various enzymes attain full enzymatic activity as they suddenly fold into their characteristic three-dimensional forms. In contrast, other enzymes are produced as inactive precursors that are successively activated by breakdown of one or a few specific peptide bonds. The inactive precursor is known as a zymogen (or a pro-enzyme). In other words, when the proteins are enzymes, the proteins are called pro-ezymes or zymogens (table 1.5 ). An energy source (ATP) is not required for cleavage [ 11 ]. Thus, in comparison to reversible regulation by phosphorylation, even proteins sited outside cells can be triggered by this means. An additional noteworthy difference is that proteolytic activation, in comparison with allosteric control and reversible covalent modification, occurs just once in the life of an enzyme molecule. Transformation of a proprotein to the mature protein includes selective proteolysis. This transforms the proproteins by one or more consecutive proteolytic clips to a arrangement in which the individual activity of the mature protein (its enzymatic activity) is expressed, e.g. the hormone insulin (proinsulin), the digestive enzyme chymotrypsin (chymotrypsinogen), a number of factors for blood clotting and for the blood clot dissolution cascades, and the connective tissue protein collagen (procollagen). Chymotrypsinogen consists of 245 amino acid residues, and is practically devoid of enzymatic activity. As the reaction starts, it is converted into a fully active enzyme. This occurs when the peptide bond joining arginine 15 and isoleucine 16 is cleaved by trypsin. The subsequent active enzyme, known as π-chymotrypsin, then acts on other π -chymotrypsin molecules. Two dipeptides are eliminated to form α -chymotrypsin (the stable form of the enzyme) [ 11 ]. The three subsequent chains in α -chymotrypsin remain interconnected to each another by two interchain disulfide bonds. The outstanding feature of this process is that cleavage of a single specific peptide bond alters the protein from a catalytically inactive form into one that is fully active. The transformation of prochymotrypsin (Pro-CT), a 2,4,5-aminoacyl residue polypeptide, to the active enzyme α -chymotrypsin includes three proteolytic clips and the formation of an active intermediate called π -chymotrypsin ( π -CT) and consequently to the mature catalytically active enzyme α -chymotrypsin ( α -CT). Examples of gastric and pancreatic zymogens are listed in table 1.5 .

Table 1.5.   Gastric and pancreatic zymogens.

1.7.4. Kinetic models for enzymes

Generally, enzyme kinetics is defined as the study of the rate of reactions, i.e., how the substrate concentration impacts the velocity of the reaction. Enzyme kinetics involves optimization of bio-catalytic reactions to allow process design and scaling up processes to further increase the production and minimize the overall overhead costs of various procedures. Kinetic investigations in the branch of biochemistry concerned with enzymes can be categorized into three types:

• •   Transient-state kinetics : This is the stage of reaction before the steady or rapid-equilibrium state, and involves quick reactions between the enzymes and substrate. These sudden changes in the reaction mixture when the substrate and enzymes are mixed require advance equipment to monitor the reaction before it changes into the steady state. The mechanisms of the reaction are associated with the enzyme structural configuration. Basic steps are involved during an enzyme-catalyzed reaction, which allow the direct study of the intermediates and products formed during a single enzyme cycle, which may further help in direct analysis of individual reaction steps for short times. In this type of reaction a sufficient concentration of enzymes is used to witness the intermediate and product formation.
• •   Steady-state kinetics : This is the phase in which the rate of formation of intermediates and the rate of decomposition remain the same, and thus the concentrations of reactive intermediates remain the same. During this reaction substrate concentration is greater than enzyme concentration. The Michaelis–Menten enzyme kinetic (figure 1.3 ) can be considered as the most often studied reaction for several enzymes. For example, chymotrypsin (protease) with a high concentration of substrate achieves maximum velocity of the reaction (called the first order of reaction) but at a certain point the substrate occupies all binding sites of the enzyme, after which further addition of substrate does not increase the rate. This is called the zeroth order of reaction (the steady state). It is the phase in which the enzyme and substrate concentrations cannot be determined using the dissociation constant. Thus steady-state enzyme kinetics is based on the theory that a catalytic reaction remains constant if the reaction is not exposed to continuous changes.
• •   Rapid-equilibrium kinetics : This the phase in which both the enzyme and substrate concentrations can be determined using the dissociation constant. During this procedure total enzyme concentration remains constant during the reaction and the concentration is very small compared to the amount of substrate. In this reaction, before the rate-determining reaction, the reactions are in equilibrium with their components, thus this stage is called rapid-equilibrium kinetics.

Figure 1.3.  The Michaelis–Menten enzyme kinetic.

According to reports, factors that affect enzyme-catalyzed reactions also affect the velocity of a reaction. These factors are called modifiers of enzyme-catalyzed reactions. These modifiers can be divided into two classes: inorganic modifiers (enzyme activators) and organic modifiers (enzyme inhibitors). These factors can have different types of effects on the velocity of the reaction; nevertheless the most vital effect is that they offer many pathways to products, e.g. when one modifier is bound to an enzyme, it alters the rate of reaction and thus forms two rate constants. However, when two modifiers participate, there are five self-regulating equilibria, resulting in three paths for making products.

There are two mechanisms, single-substrate and multiple-substrate, that are helpful in studying the different stages of enzymatic reactions. Understanding these stages helps in understanding the properties of enzymes. Certain enzymes have single substrates (a single substrate binding site), e.g. triosephosphate isomerase, whereas certain enzymes have multiple substrates molecules (multiple binding sites), such as dihydrofolate reductase, and bind with multiple substrates. After the exploration of specific RNA sequences required for RNA replication, new biocatalysts in the form of ribozymes have emerged with the potential to catalyze specific biochemical reactions. There is a misconception about biological catalysts that all biological catalysts are made up of proteins, which is not true; some are RNA-based catalysts (ribozymes and ribosomes). Both are important for many cellular functions. A major difference between enzymes and ribozymes is that RNA-based catalysts are restricted to only a few reactions; however, their reaction mechanisms and kinetics can be studied and classified by similar procedures. Enzyme-based mutation, in particular site-directed mutagenesis, is an important approach to alter genes and investigate the functional and structural features of enzymes, e.g. mutation of the enzyme present in Coprinus cinereus peroxidase offers an understanding of its increased thermostability. Challenges involved in studying cascades of reactions catalyzed by a multi-enzyme, e.g. proteasome involved in the ubiquitin–proteasome pathway, can be overcome by establishing understanding of the complex structure and the respective biochemical reactions. This understanding allows exploration of active sites, intermediate compounds, final products and their interrelation with complex machinery, as well as biochemical reactions. It has been well understood that enzymes that accelerate complex reactions have numerous substrates and involve complex enzyme kinetic mechanisms. As discussed above, most of the biochemical reactions occurring in the body are multi-substrate reactions. In such reactions two substrates are involved and yield two products (figure 1.4 ). These types of reactions involve the transfer of a compound from one compoment to another, e.g. when glucose reacts with ATP in the presence of hexokinase it forms glucose 6-phophaste and ADP. Here, phosphate from ATP is transfered to glucose to form glucose 6 phosphate. The mechanism of catalysis involves two types of reactions: sequential and non-sequential reactions. Sequential reaction results in the formation of a ternary complex. This means that both of the substrates involved in the reaction bind with an enzyme to form the product (figure 1.4 ). Sequential reaction is further divided into two types: the random and compulsory order mechanisms. As the name suggests, in a 'random' mechanism, either substrate can bind first and any product can leave first. In contrast to the random order mechanism, in the compulsory order mechanism the order of binding of the substrate and order of release of the product is specific; this is also called the Theorell–Chance mechanism (figure 1.4 ). In a non-sequential reaction, also called the 'ping-pong' mechanism, formation of ternary complex does not take place. In these types of reactions, when the first substrate binds with enzyme its product is released, and then the second substrate binds and its product is released. Such a reaction is called a double placement reaction. Thus only a single substrate binds at a time; this may be due to the presence of a single binding site on the enzyme. Major differences between the sequential and non-sequential reactions are that the formation of a ternary complex takes place only in the sequential reaction, and that in the sequential reaction both substrates bind to the enzyme and release products, while in the non-sequential mechanism the substrates bind and release their products one after the other (figure 1.4 ).

Figure 1.4.  Multi-substrate reactions.

Another type of sequential mechanism is the systematic mechanism, which involves the addition of substrates and formation of products in a specific order.

1.7.5. Enzyme mediated acid–base (general) catalysis

Several protein enzymes use general acid–base catalysis as a way to increase reaction rates [ 26 ]. The amino acid histidine is optimized for this function because it has a pK(a) (where K(a) is the acid dissociation constant) near physiological pH [ 26 ].

When the substrate has been bound at the catalytic site, the charged functional groups of the side chains of neighboring aminoacyl residues may contribute in catalysis by behaving as acidic or basic catalysts. There are two extensive groups of acid–base catalysis by enzymes: general and specific (acid or base) catalysis. Specific acid or specific base catalysis are those reactions in which the reaction rates fluctuate under the influence of changes in H + or H 3 O + concentration, but are independent of the concentrations of the other acids or bases present in the solution. In contrast to specific catalysis, general acid or general base catalysis are the reactions whose rates are very reactive to all acids (proton donors) or bases (proton acceptors) present in the solution. To examine whether a given enzyme-catalyzed reaction is a general or specific acid or base catalysis, the rate of reaction is determined under two sets of circumstances:

• •   at different pH values at a constant buffer concentration, and
• •   at constant pH values but at different buffer concentrations. Against this background, if the degree of the reaction deviates as a function of pH at a constant buffer concentration, the reaction is specific base/acid catalyzed if the pH is above/below 7.0. If the reaction rate at a constant pH rises as the buffer concentration increases, the reaction is general base/acid catalysis, if the pH is above/below 7.0.

1.7.6. Metallozymes

Almost 25% of all enzymes include tightly bound metal ions or need them for activity. The major role of these metal ions is investigated using techniques such as x-ray crystallography, magnetic resonance imaging (MRI) and electron spin resonance (ESR). A metalloprotein is a protein that contains a metal ion co-factor. Metallozymes contain a certain amount of functional metal ion that is retained during the course of purification [ 27 ]. A metal-activated enzyme binds with metals less firmly, but needs to be activated by addition of metals. Four types of complexes are possible for the tertiary complexes of the catalytic site (Enz), a metal ion (M) and substrate (S) that exhibit 1:1:1 stoichiometry:

All of these complexes are possible for metal-activated enzymes. Metallozymes cannot form the EnzSM complex (substrate–bridge complexes), as the purified enzyme exists as Enz–M. Three generalization can be made:

• •   The majority of the kinases (ATP: phosphotransferases) form substrate–bridge complexes of the type enzyme–nucleotide–M.
• •   Phosphotransferases (phosphoenolpyruvate or pyruvate used as the substrate), enzymes catalyzing other reactions of phosphoenolpyruvate and carboxylases, form metal bridge complexes (Enz–M–S).
• •   A particular enzyme may form one type of bridge complex with one substrate and a different type with another.

The metal ions participate in each of the four mechanisms by which the enzymes are known to accelerate the rates of chemical reaction:

• •   Approximation of reactants.
• •   Covalent catalysis.
• •   General acid–base catalysis.
• •   Induction of strain in the enzyme or substrate.

Metal ions are electrophiles (attracted to electrons) and share an electron pair forming a sigma bond. They may also be considered as super acids as they exist in neutral solutions, frequently having a positive charge which is greater than their quantity. Mn 2+ , Ca 2+ and Mg 2+ are the metal ions that are most commonly used in enzymatic catalysis. Two metal ions, iron and manganese are used in the form of haemprotein. Metal ions have the potential to accept electrons via sigma or pi bonds to successively activate electrophiles or nucleophiles. By means of donating electrons, metals can activate nucleophiles or act as nucleophiles themselves. The co-ordination sphere of a metal may bring together the enzyme and substrate or form chelate-producing distortion in either the enzyme or substrate [ 28 ]. A metal ion may also mask a nucleophile and thus avoid an otherwise probable side reaction. Metals can also function as three-dimensional templates for the co-ordination of basic groups on the enzyme or substrate.

1.8. Enzyme inhibition

Enzyme inhibition decreases the activity of an enzyme without significantly disrupting its three-dimensional macromolecular structure. Inhibition is therefore distinct from denaturation and is the result of a specific action by a reagent directed or transmitted to the active site region. When low molecular weight compounds interfere with the activity of enzymes by partially reducing or completely inhibiting the enzyme activity either reversibly or irreversibly, it is known as enzyme inhibition. The compounds responsible for such inhibition are called enzyme inhibitors. To protect the enzyme catalytic site from any change, a ligand binds with a critical side chain in the enzyme. Chemical modification can be performed to test the inhibitor for any drug value. Studies of enzymes can yield much information about the following:

• •   A number of drugs useful in medicine, which seem to function because they can inhibit certain enzymes in malfunctioning cells.
• •   The convenience of elucidating metabolic pathways in cells.
• •   The mechanism of the catalytic activity.
• •   The nature of the functional group at the active site.
• •   The substrate specificity of the enzyme.

The pharmacological action of drugs is mainly based on enzyme inhibition, e.g. sulfonamides and other antibiotics. In the majority of cases the enzyme inhibited is known. The development of nerve gases, insecticides and herbicides is based on enzyme inhibition studies. There are two major types of enzyme inhibition: reversible and irreversible.

Reversible inhibitors efficiently bind to enzymes by forming weak non-covalent interactions, e.g. ionic bonds, hydrophobic interactions and hydrogen bonds. Reversible inhibitors do not form any strong chemical bonds or reactions with the enzyme, they are formed quickly and can easily be removed, in contrast to irreversible inhibitors. Reversible inhibition includes competitive inhibition, uncompetitive inhibition and noncompetitive inhibition. Irreversible inhibition includes group specific inhibition (reacts only to a certain chemical group), reactive substrate analogs (affinity label) and inhibitors that are structurally similar to the substrate and will bind to the active site, and mechanism-based inhibitors (enzymes transform the inhibitor into a reactive form within the active site).

1.9. Pharmaceutical applications

Currently, enzymes are often utilized for a broad range of applications such as: washing powders (e.g. proteases, lipases, amylases); textile manufacture (amylases and catalase to remove the starch); the leather industry (proteases to hydrolyze proteins); the paper industry; improvement of the environment; food production (enzyme-modified cheese/butter), processing (glucose oxidase for dough strengthening) and preservation; and medical applications. According to current reports, several enzymes are produced industrially and there are significant applications in the food industry (45% of use), detergent industry (35%), textiles industry (10%) and leather industry (3%). Details on the applications of individual enzymes are provided in table 1.6 .

Table 1.6.   Industrially produced enzymes from plant sources and their applications.

1.9.1. Diagnostic applications of enzymes

Enzymes have been used widely in diagnostic applications varying from immunoassays to biosensors. Enzyme immunoassay methods hold great promise for application under a wide variety of conditions. Under laboratory conditions they can be as sensitive as radio-immunoassays, but they can also be adapted as simple field screening procedures [ 29 , 30 ]. The examination of enzyme quantity in the extracellular body fluids (blood plasma and serum, urine, digestive juices, amniotic fluid and cerebrospinal fluid) are vital aids to the clinical diagnosis and management of disease. Most enzyme-catalyzed reactions occur within living cells, however, when an energy imbalance occurs in the cells because of exposure to infective agents, bacterial toxins, etc, enzymes 'leak' through the membranes into the circulatory system. This causes their fluid level to be raised above the normal cell level. Estimation of the type, extent and duration of these raised enzyme activities can then furnish information on the identity of the damaged cell and indicate the extent of injury. Enzyme assays can make an important contribution to the diagnosis of diseases, as a minute change in enzyme concentration can easily be measured. Determination of the changes in enzyme level thus offers a greater degree of organ and disease differentiation in comparison to other possible clinico-chemical parameters, e.g. albumin or gamma globulin. Currently, the diagnostic specificity of enzyme tests is such that they are limited primarily to confirming diagnosis, offering data to be weighed alonside other clinical reports, owing to lack of disease specific enzymes. Table 1.7 includes a number of diagnostically important enzymes which are most often examined in clinic laboratories [ 29 , 30 ].

Table 1.7.   Diagnostically significant enzymes.

1 B, brain; E, erythrocytes; H, heart muscle; Ht, hepatobiliary tract; I, intestinal mucosa; K, kidney; L, M, skeletal muscle; Pa, pancreas; P1, placenta; Pr, prostate gland; S, saliva.

1.9.1.1. Enzyme examinations in diseases of the liver and biliary

The diseases of the liver and gastrointestinal tract were among the first to which serum enzyme tests were applied. They have proved to be most effective owing to the large size of the organs and the wide range and abundance of enzymes [ 32 – 36 ]. The liver-based enzymes GOT, GPT and AP are examined to evaluate the site and nature of liver disease. LD, GGT, OCT and CHE are also examined. Several enzymes employed in the diagnosis of liver diseases along with their respective levels are listed in table 1.8 .

Table 1.8.   Liver diseases and enzymes used in diagnosis [ 32 – 36 ].

1.9.1.2. Enzyme applications in heart disease

According to previous reports, no single enzyme has yet been reported to cure myocardial damage. The discovery of serum glutamine oxalacetic acid transaminase determination (GOT) in 1954 was considered a significant step forward in the diagnosis of acute myocardial infarction. A mixture of results from assays of CPK (creatine phosphokinase), HBD ( α -hydroxybutyrate dehydrogenase) and GOT (glutamine oxalacetic acid transaminase)—each of which has been shown to be elevated in more than 90% of cases—is used for diagnostic purposes [ 37 – 39 ]. The level of CPK starts rising three to four hours after the initial onset of pain, followed in order by GOT and AST (HBD) which appear after approximately eight hours. The maximum levels are reached in the same sequence, CPK after 24 h, LD 1 after 36 h and AST after about two days. The rise in enzyme levels is fairly moderate, AST and CPK increase by four to ten times their respective normal levels and LD 1 is approximately five-fold higher than normal. An enzyme known as hyaluronidase (hyaluronate hydrolysis) has been reported to cure heart attack [ 38 ]. The activity of many enzymes including aldolase, malic dehydrogenase, isomerase and ICD may increase following myocardial infarction [ 38 ].

1.9.1.3. Diagnosis of muscle disease

Skeletal muscle disorders include diseases of the muscle fibers (myopathies) or of the muscle nerves (neurogenic disorders) [ 40 ]. In myopathies CPJ, LD, ALD, GOT and GPT levels are raised. In the case of neurogenic diseases and hereditary diseases, CPK is occasionally raised (2–3 fold) [ 40 ]. Damage to the muscle may be due to extensive muscular exercise, drugs, physical trauma, inflammatory diseases, microbial infection or metabolic dysfunction, or it may be genetically predisposed. In muscular disorders the level of CPK is elevated in serum with the highest frequency and is assayed in the diagnosis of these disorders. An additional useful assayed enzyme is acetyl cholinesterase (AChE), which is significant in regulating certain nerve impulses [ 41 ]. Various pesticides affect this enzyme, so farm labors are frequently tested to be sure that they have not received accidental exposure to significant agricultural toxins. There are number of enzymes that are characteristically used in the clinical laboratory to diagnose diseases. There are highly specific markers for enzymes active in the pancreas, red blood cells, liver, heart, brain, prostate gland and many of the endocrine glands [ 41 ]. From the time when these enzymes became comparatively easy to examine using automated techniques, they have been part of the standard blood tests that veterinarians and medical doctors are likely to need in the diagnosis and treatment/management of diseases.

1.9.2. Enzymes in therapeutics

Enzymes have two significant features that differentiate them from all other types of drugs. First, enzymes frequently bind and act on their targeted sites with high affinity and specificity. Second, enzymes are catalytic and convert numerous target molecules to the desired products. These two important features make enzymes specific and potent drugs that can achieve therapeutic biochemistry in the body that small molecules cannot. These features have resulted in the development of many enzyme-based drugs for a wide range of disorders [ 42 ]. Currently, numerous enzymes are used as therapeutic agents, owing to the following features:

• •   High specificity to their substrates.
• •   Proficient in producing the desired effect without provoking any side effects.
• •   Water soluble.
• •   Extremely effective in a biological environment.

Enzymes as therapeutic agents also have some serious disadvantages which restrict their application. Their bulky structure, due to their large molecular weight, excludes them from the intracellular domain. Owing to their high proteinaceous nature they are highly antigenic and are rapidly cleared from blood plasma. Extensive purification from pyrogens and toxins is essential for parenteral enzymes, which increases the cost. Table 1.9 lists some therapeutically important enzymes.

Table 1.9.   Therapeutically important enzymes.

1.9.2.1. Enzyme therapy of cancer

In traditional medicine, proteolytic enzymes derived from plant extracts have been used for a long time In addition to proteolytic enzymes from natural resources such as plants, 'modern' enzyme therapy includes pancreatic enzymes. Therapeutically, the use of proteolytic enzymes is partly based on scientific reports and is partly empirical [ 43 ]. Clinical evidence of the use of proteolytic enzymes in cancer studies has typically been obtained with an enzyme preparation comprising a combination of papain, trypsin and chymotrypsin. Earlier reports proved that enzyme therapy can reduce the adverse effects caused by radiotherapy and chemotherapy. There is also a report available that, in some types of tumors, survival may be sustained. The positive effects of systemic enzyme therapy appear to be based on its anti-inflammatory potential. Nevertheless, the exact mechanism of action of systemic enzyme therapy remains unsolved. The proportion of proteinases to antiproteinases, which is regularly used as a prognostic marker in cancer studies, is likely to be influenced by the oral administration of proteolytic enzymes, most likely via induction of the synthesis of antiproteinases. In addition, there are many alterations of cytokine composition during treatment with orally administered enzymes, which might be a sign of the efficacy of enzyme therapy [ 44 ].

Proteases and their inhibitors have long been studied in several tumor systems. However, out of numerous promising serine and metalloproteinase inhibitors, not a single one is included in oncology at present. The present exploration for active antiproteolytic agents is in contrast to the traditional approach, as evidenced by John Beard, who proposed the management of advanced cancer using fresh pancreatic extracts whose antitumor activity was based on their proteolytic potential.

The enzymatic treatment of tumors is based on the idea of denying the abnormal cells their essential metabolic precursors such as amino acids, nucleic acids and folates. A number of enzymes have been examined and evidenced as antitumor agents. l -serine dehydratase, l -arginase, carboxypeptidase G (folate depletion), l -asparaginase, l -methioninase, l -phenylalanine ammonia lyase, l -glutaminase, l -tyrosinase and xanthine oxidase have been studied for their anticancer activity. Enzyme preparations such as asparaginase (amidase), bromelain (protease) and chymotrypsin (protease) have also been studied as cancer treatments (table 1.9 ).

l -asparaginase is the most widely investigated enzyme. It has been reported in treatment against three neoplastic diseases, acute lymphoblastic leukemia, leukemic lymphosarcoma and myeloblastic leukemia. It deprives the cancerous cells of their nutritional asparagine supply. Asparagine is essential for protein synthesis, which takes place inside the cell, and decreased protein synthesis perhaps accounts for the immunosuppression and toxic effects of asparaginase-based treatment.

The prospects of enzyme-based treatment against cancer are very bright, but the difficulties of antigenicity and short circulation time remain to be overcome.

1.9.2.2. Enzymes in thrombolytic treatment

Activation of the blood clotting mechanism during inflammation is part of the body's defense mechanism which requires therapeutic intervention. Under normal physiological conditions there is an equilibrium between blood coagulation (clotting) and fibrinolysis (the process of dissolving the clotted blood) [ 47 ]. Biocatalysts such as enzymes, ribozymes, pro-enzymes, activators and pro-activators are responsible for maintaining equilibrium between clot formation and fibrinolysis. Imbalances in the concentration of these bio-activators may disturb physiology. In the biological process of fibrogenesis, clot formation takes place due to the plasma protein (soluble fibrinogen), which is ultimately converted to insoluble fibrin by the enzyme thrombin. This process is dependent on the conversion of thrombin from prothrombin. This bio-conversion takes place after the cascade of enzymatic reactions which involved certain key biological compounds called clotting factors. A blood clot dissolving enzyme known as plasmin is present in the blood as the pro-enzyme plasminogen. During clot dissolution activators convert the plasminogen to plasmin. This biological process is well regulated by certain process such as vasoconstriction, formation of a fibrin and clot platelet aggregation [ 46 ].

As the body utilizes enzymes in conserving this key balance of homeostasis, in a similar way we can utilize enzymes to repair or restore the homeostatic balance once it is lost. Several reports have shown that one of the best approaches for treating such clinical conditions is the administration of enzymes capable of converting plasminogen to plasmin (the enzyme which dissolves the clot) via intraveneous injection. This type of treatment is called therapeutic thrombolysis or thrombolytic therapy. In this treatment, pharmacological agents are used to medically induce clot breakdown [ 47 ]. Various novel thrombolytic agents have been derived from different sources for therapeutic use, such as from bacteria (streptokinase), the venom of the Malayan pit viper (Arvin), a filamentous fungus Koji mold Aspergillus oryzae (brinase), a South American snake (reptilase) and human urine (urokinase) [ 47 ].

Current advancements in thrombolytic therapy are more focused on the treatment of occlusions (blockages) of blood vessels. These types of therapy can be considered as life-saving and emergency medicine for life-threatening conditions such as myocardial infarction and massive pulmonary embolism, which are the most common reasons for cardiac arrest. This life-saving treatment is more reliable in preventing the blockages of vessels in the lungs and heart. Artery blockage conditions such as pulmonary embolism in the lungs by the formation of a clot creates tension on the right side of the heart, resulting in shortness of breath and chest pain mainly upon breathing in. Enzyme-based thrombolysis for treating massive pulmonary embolism has been considered as an effective approach to dissolving clots in these large vessels. Since surgical removal raises the chances of new blood clot formation that can cause another pulmonary embolism at the same or a different site, it is considered a dangerous practice and thrombolytic therapy is considered the more effective treatment [ 47 ]. Nevertheless, reoccurrence of clot formation or clot re-formation is very common in patients who have undergone enzyme-based thrombolytic treatment. Researchers from various organizations (1971) determined the effectiveness of streptokinase over heparin in reducing the chances of death in acute myocardial infarction patients. Significant results were obtained during this experiment. As discussed above, re-formation of the clot is one of the major concerns in fibrinolytic therapy. Most clinicians start treatment with a high dose of fibrinolytic agents, which is reduced later on. This approach may reduce disease progression for some time, but often increases the chances of clot re-formation. Even after the dissolution of the clot it is very difficult to maintain the same physiologically balanced environment (homeostasis) at the site of damaged tissues and the chance of new clot formation at that particular location is very high. Therefore, fibrinolytic based treatment is always accompanied by anticoagulants, such as heparin [ 46 ].

Major concerns associated with streptokinase therapy are fever, a tendency for bleeding, antigenicity (as with any foreign protein) and the difficulty of determining the proper dose [ 47 ]. Post-enzymatic treatment bleeding is one of the major concerns and it is also a concern when anticoagulants are used alone. According to current research, urokinase (produced in the kidneys and obtained from human urine) is considered safer than streptokinase. For the production of urokinase, 2300 l of urine is required to yield only 29 mg of purified urokinase, thus considering the expense involved in its manufacture, its clinical utilization has been restricted. Other examples are Arvin and reptilase. Utilization of these has been restricted for several reasons, but they are still considered as potential replacements for heparin as anticoagulants. Some researchers have noticed that optimum dose plays an important role and is one of the key factors in determining re-clot formation. Thorough investigation is required to overcome any shortcomings and increase the acceptance of these enzymes in therapeutic use [ 47 ].

1.9.2.3. The role of enzymes in digestive disorders and inflammations

Enzymes play an essential role in the management of various digestive disorders, such as exocrine pancreatic insufficiency [ 48 ]. Supplementation with enzymes may also be advantageous for other conditions associated with poor digestion, such as lactose intolerance. Generally, pancreatic enzymes such as porcine and bovine have been the preferred form of supplementation for exocrine pancreatic insufficiency [ 48 ]. Utilization of microbe-derived lipase has presented promise with reports showing benefits alike to pancreatic enzymes, but with a lower dosage concentration and a broader pH range. The safety and efficacy of enzymes derived from microbial species in the treatment of conditions such as malabsorption and lactose intolerance is promising. Plant-derived enzymes, e.g. bromelain from pineapple, serve as active digestive aids in the breakdown of proteins. Synergistic properties have also been reported using a combination of animal-based enzymes and microbe-derived enzymes or bromelain. Buccal administration of pancreatin (derived from an alcoholic extract of animal pancreas) enhances the enzymatic digestion of starch and proteins in patients with pancreatic cysts and pancreatitis. Pancreatin in combination with lipase is used to treat patients with fatty stools. Hydrolytic enzymes such as papain and fungal extracts ( Aspergillus niger and Aspergillus otyzae ) are used to enhance absorption from the small intestine [ 49 ]. These fungal extracts comprise amylases and proteases along with cellulases, which support the breakdown of the otherwise indigestible fibers of cabbages, etc, and thus reduce dyspepsia and flatulence [ 50 ]. Currently, micro-organisms are used at a large scale for the production of therapeutic enzymes. Among various micro-organisms Saccharomyces cerevisiae, Saccharomyces fragilis, Bacillus subtilis and two Aspergillus species are considered safe by the FDA (USA) for obtaining oral β -galactosidase (from A. oryzae ) which is often used by patients suffering from inherited intestinal disease lactose deficiency [ 51 ]. Children with this genetic disorder children are incapable of digesting milk lactose. Enzymatic preparations such as β -galactosidase catalyze the conversion of lactose to glucose and galactose, which are quickly absorbed by the intestine. Other enzymatic preparations, e.g. penicillinase (from B. subtilis ) are often used to treat hypersensitivity reactions caused by the antibiotic penicillin [ 52 ]. This enzyme catalyzes the conversion of penicillin to penicillanic acid, which is non-immunogenic. In addition, microbial and plant hydrolases are also used to decrease inflammation and edema [ 53 ]. Thrombin, trypsin, chymotrypsin, papain, streptokinase, streptodornase and sempeptidase are under clinical trial investigation. These enzymatic preparations are administered orally and have considerable proteolytic activity in the serum. Streptodornase has also displayed pain-relieving action on systemic injection [ 54 ]. Preparations have also been used to clean dirty wounds and necrotic tissue and to remove debris from second and third degree burns.

1.10. Plants and algae enzyme systems

Plant based foods are usually consumed in their raw form [ 68 ]. This eases the main concern with animal-based enzymes by preserving the integrity of the enzymes themselves. Moreover, plant-based digestive enzymes are effective over a broad scope of pH levels. This range is usually between 3.0 and 9.0, which is highly well-matched with the human gastrointestinal environment [ 55 – 72 ]. Thus plant-based enzymes are compatible for supporting comprehensive digestive health. Protease, amylase, lipase and cellulose are the important enzymes and are present in plants. Protease breaks down protein that can be present in meat, fish, poultry, eggs, cheese and nuts. Amylase assists your body with the breakdown and subsequent absorption of carbohydrates and starches. Lipase aids the digestion of fat. When your diet includes lipase-rich foods, it eases the production burden on the gall bladder, liver and pancreas. Cellulase is present in many fruits and vegetables, and it breaks down food fibers, which increases their nutritional value to our bodies. The presence of cellulase in plant-based sources is important, because it is not naturally present in the human body. Fruits and vegetables are an ideal source for enzymes. They are enzyme-rich and easily consumed without needing to be cooked or processed, ultimately preserving the full functionality of the enzymes. By using plant biotechnology several enzymes can be produced from plants as well algal resources [ 56 – 72 ].

During algal photosynthesis various proteins and enzymes are produced which can be utilized in economic development and environment management, such as in wastewater treatment, production of fine chemicals, and biodiesel production [ 56 – 72 ]. Due to their potential to capture and fix carbon dioxide using solar energy, photosynthetic marine algae are considered as potential models for the production of proteins. It has been recently observed that algal chloroplasts can be transformed for the production recombinant proteins [ 55 ]. Five different classes of recombinant enzymes; xylanase, α-galactosidase, phytase, phosphate anhydrolase, and β-mannanase, D. tertiolecta or C. reinhardtii were in the plastids of D. tertiolecta or C. reinhardtii. Similar strategies should allow for recombinant protein production in many species of marine algae [ 55 ].

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B, brain; E, erythrocytes; H, heart muscle; Ht, hepatobiliary tract; I, intestinal mucosa; K, kidney; L, M, skeletal muscle; Pa, pancreas; P1, placenta; Pr, prostate gland; S, saliva.

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18.6: Enzyme Action

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  Learning Objectives

• To describe the interaction between an enzyme and its substrate.

Enzyme-catalyzed reactions occur in at least two steps. In the first step, an enzyme molecule (E) and the substrate molecule or molecules (S) collide and react to form an intermediate compound called the enzyme-substrate (E–S) complex . (This step is reversible because the complex can break apart into the original substrate or substrates and the free enzyme.) Once the E–S complex forms, the enzyme is able to catalyze the formation of product (P), which is then released from the enzyme surface:

\[S + E \rightarrow E–S \tag{\(\PageIndex{1}\)} \]

\[E–S \rightarrow P + E \tag{\(\PageIndex{2}\)} \]

Hydrogen bonding and other electrostatic interactions hold the enzyme and substrate together in the complex. The structural features or functional groups on the enzyme that participate in these interactions are located in a cleft or pocket on the enzyme surface. This pocket, where the enzyme combines with the substrate and transforms the substrate to product is called the active site of the enzyme (Figure \(\PageIndex{1}\)).

The active site of an enzyme possesses a unique conformation (including correctly positioned bonding groups) that is complementary to the structure of the substrate, so that the enzyme and substrate molecules fit together in much the same manner as a key fits into a tumbler lock. In fact, an early model describing the formation of the enzyme-substrate complex was called the lock-and-key model (Figure \(\PageIndex{2}\)). This model portrayed the enzyme as conformationally rigid and able to bond only to substrates that exactly fit the active site.

Working out the precise three-dimensional structures of numerous enzymes has enabled chemists to refine the original lock-and-key model of enzyme actions. They discovered that the binding of a substrate often leads to a large conformational change in the enzyme, as well as to changes in the structure of the substrate or substrates. The current theory, known as the induced-fit model, says that enzymes can undergo a change in conformation when they bind substrate molecules, and the active site has a shape complementary to that of the substrate only after the substrate is bound, as shown for hexokinase in Figure \(\PageIndex{3}\). After catalysis, the enzyme resumes its original structure.

The structural changes that occur when an enzyme and a substrate join together bring specific parts of a substrate into alignment with specific parts of the enzyme’s active site. Amino acid side chains in or near the binding site can then act as acid or base catalysts, provide binding sites for the transfer of functional groups from one substrate to another or aid in the rearrangement of a substrate. The participating amino acids, which are usually widely separated in the primary sequence of the protein, are brought close together in the active site as a result of the folding and bending of the polypeptide chain or chains when the protein acquires its tertiary and quaternary structure. Binding to enzymes brings reactants close to each other and aligns them properly, which has the same effect as increasing the concentration of the reacting compounds.

Example \(\PageIndex{1}\)

• What type of interaction would occur between an OH group present on a substrate molecule and a functional group in the active site of an enzyme?
• Suggest an amino acid whose side chain might be in the active site of an enzyme and form the type of interaction you just identified.
• An OH group would most likely engage in hydrogen bonding with an appropriate functional group present in the active site of an enzyme.
• Several amino acid side chains would be able to engage in hydrogen bonding with an OH group. One example would be asparagine, which has an amide functional group.

Exercise \(\PageIndex{1}\)

• What type of interaction would occur between an COO − group present on a substrate molecule and a functional group in the active site of an enzyme?

One characteristic that distinguishes an enzyme from all other types of catalysts is its substrate specificity . An inorganic acid such as sulfuric acid can be used to increase the reaction rates of many different reactions, such as the hydrolysis of disaccharides, polysaccharides, lipids, and proteins, with complete impartiality. In contrast, enzymes are much more specific. Some enzymes act on a single substrate, while other enzymes act on any of a group of related molecules containing a similar functional group or chemical bond. Some enzymes even distinguish between D- and L-stereoisomers, binding one stereoisomer but not the other. Urease, for example, is an enzyme that catalyzes the hydrolysis of a single substrate—urea—but not the closely related compounds methyl urea, thiourea, or biuret. The enzyme carboxypeptidase, on the other hand, is far less specific. It catalyzes the removal of nearly any amino acid from the carboxyl end of any peptide or protein.

Enzyme specificity results from the uniqueness of the active site in each different enzyme because of the identity, charge, and spatial orientation of the functional groups located there. It regulates cell chemistry so that the proper reactions occur in the proper place at the proper time. Clearly, it is crucial to the proper functioning of the living cell.

A substrate binds to a specific region on an enzyme known as the active site, where the substrate can be converted to product. The substrate binds to the enzyme primarily through hydrogen bonding and other electrostatic interactions. The induced-fit model says that an enzyme can undergo a conformational change when binding a substrate. Enzymes exhibit varying degrees of substrate specificity.

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A theory to explain the mechanism of enzymatic reactions, in which it is proposed that the enzyme and substrate(s) bind temporarily to form an enzyme–substrate complex. The binding site on the enzyme is known as the ‘active site’ and is structurally complementary to the substrate(s). Thus the enzyme and substrate(s) are said to fit together as do a lock and a key.

From:   lock-and-key theory   in  A Dictionary of Zoology »

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Lock and Key Model Enzyme Explained!

by Taha Cheema

Reading about enzymes in IGCSE or O Level Biology syllabus and books might leave you confused about how they work. The “ lock and key model enzyme ” has an elegant and simple explanation. Read on below to find out.

"Just as a key fits a lock, enzymes and substrates are built for each other.”

What is the Lock and Key Model?

Key definitions.

In order to explain how enzymes work, we need to be aware of two important terms:

Enzymes: Proteins that help speed up chemical reactions . 

Substrates : The chemical compounds that are processed by enzymes (e.g. they can be broken down)

Basics of the Lock and Key Model of Enzyme Action

The basics of the lock and key model enzymes is one of the simplest models of enzyme action to understand. The lock and key model of enzyme action is similar to how we open a lock:

Only one specific key fits and opens a given lock

Similarly, only the correct substrate can fit a given enzyme, allowing it to work

*Note: This unique pairing between enzymes and substrates maintains precision in biological processes. Specific enzymes can focus on their specific reactions and this improves efficiency in maintaining cells !

Using Lock and Key to Describe an Enzyme:

A good way to understand enzyme action is through the lock and key hypothesis :

The substrate can be thought of as a key (substrate = key)

The enzyme can be thought of as the lock (enzyme = lock)

When the substrate enters, it “ activates ” the enzyme, which starts processing the substrate

Other substrates will not be able to “activate” the enzyme, as their shape won’t match

Wrapping-Up

In conclusion, enzymes speed up ( catalyze ) some key chemical reactions in organisms . The theory of lock and key model enzymes says that a given enzyme only interacts with its precise substrates. 

We hope that by reading this concept guide, you developed a better understanding of O Level or IGCSE Biology . Another way to clear your concepts is by attempting and practicing with IGCSE or  O Level Biology past papers .

Stay tuned to  Out-Class for more study guides!

Most Common Repeated Questions:

Unlock the secrets to acing your IGCSE/O Level Biology exams with a sneak peek into the most frequently asked questions that have graced the pages of past papers!

• Explain the ‘lock and key’ hypothesis of enzyme action using a named example (5) [ Oct/Nov 2021]

The change in colour of the apple tissue is due to a series of chemical reactions. An enzyme called PPO acts as a catalyst for one of these reactions.  The colour change can be prevented by placing the cut surface of apple tissue in boiling water for a short time immediately after the fruit is cut.  Explain this observation using the lock and key hypothesis of enzyme action. (4) [ Oct/Nov 2020]

Q. What are enzymes?

Enzymes are proteins that act as catalysts, speeding up chemical reactions in living organisms. They facilitate these reactions without being consumed or altered themselves.

Related: Enzymes

Q. What is the lock and key model of enzyme action?

The lock and key model is a hypothesis explaining how enzymes interact with substrates. It compares the specificity of enzyme-substrate interactions to a lock that only opens with the correct key. In this model, enzymes (locks) have specific active sites that perfectly fit their substrates (keys).

Q. How does the lock and key model maintain precision in biological processes?

The lock and key model ensures precision by allowing only specific substrates to bind with an enzyme's active site. This specificity prevents enzymes from interacting with inappropriate substrates, maintaining accuracy and efficiency in biological reactions.

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Illustrate the lock and key hypothesis of enzyme action.

Enzymes: a catalyst is a material that acts to initiate a chemical reaction, and enzymes are specialized compounds that catalyze biological events. when the substrates are attached to the enzyme's active site, the enzyme catalyzes the reaction, and the chemical process begins. the active site is the enzyme's specific place where it is linked with the substrate. the substrate's attachment to the enzyme causes changes in the distribution of electrons in the substrate's chemical bonds. this eventually leads to reactions that aid in the creation of products. products are released from the enzyme surface in order to recycle the enzyme for use in a subsequent reaction step. the active site has a distinct geometric form that contrasts with the geometric shape of a substrate fragment. this obviously indicates that the enzymes can only react with one or a few related molecules. lock and key model: a lock and key analogy may be used to describe the fundamental action of a single substrate enzyme. in this case, the enzyme is the lock, and the substrate is the key. only the correct size key, which is the substrate, enters the keyhole, which is the active site of the lock, which is the enzyme. other keys that are too tiny, too big, or have wrongly positioned teeth do not fit into the lock. only the right-shaped key can open the lock..

Key and lock hypothesis of enzyme action was given by

The 'lock and key' model of enzyme action illustrates that a particular enzyme molecule