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Binding site
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Glucose binds to hexokinase in the active site at the beginning of glycolysis.

In biochemistry and molecular biology, a binding site is a region on a macromolecule such as a protein that binds to another molecule with specificity.[1] The binding partner of the macromolecule is often referred to as a ligand.[2] Ligands may include other proteins (resulting in a protein–protein interaction),[3] enzyme substrates,[4] second messengers, hormones, or allosteric modulators.[5] The binding event is often, but not always, accompanied by a conformational change that alters the protein's function.[6] Binding to protein binding sites is most often reversible (transient and non-covalent), but can also be covalent reversible[7] or irreversible.[8][9]

Function

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Binding of a ligand to a binding site on protein often triggers a change in conformation in the protein and results in altered cellular function. Hence binding site on protein are critical parts of signal transduction pathways.[10] Types of ligands include neurotransmitters, toxins, neuropeptides, and steroid hormones.[11] Binding sites incur functional changes in a number of contexts, including enzyme catalysis, molecular pathway signaling, homeostatic regulation, and physiological function. Electric charge, steric shape and geometry of the site selectively allow for highly specific ligands to bind, activating a particular cascade of cellular interactions the protein is responsible for.[12][13][14]

Catalysis

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Activation energy is decreased in the presence of an enzyme to catalyze the reaction.

Enzymes incur catalysis by binding more strongly to transition states than substrates and products. At the catalytic binding site, several different interactions may act upon the substrate. These range from electric catalysis, acid and base catalysis, covalent catalysis, and metal ion catalysis.[11] These interactions decrease the activation energy of a chemical reaction by providing favorable interactions to stabilize the high energy molecule. Enzyme binding allows for closer proximity and exclusion of substances irrelevant to the reaction. Side reactions are also discouraged by this specific binding.[15][11]

Types of enzymes that can perform these actions include oxidoreductases, transferases, hydrolases, lyases, isomerases, and ligases.[16]

For instance, the transferase hexokinase catalyzes the phosphorylation of glucose to make glucose-6-phosphate. Active site residues of hexokinase allow for stabilization of the glucose molecule in the active site and spur the onset of an alternative pathway of favorable interactions, decreasing the activation energy.[17]

Inhibition

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Protein inhibition by inhibitor binding may induce obstruction in pathway regulation, homeostatic regulation and physiological function.

Competitive inhibitors compete with substrate to bind to free enzymes at active sites and thus impede the production of the enzyme-substrate complex upon binding. For example, carbon monoxide poisoning is caused by the competitive binding of carbon monoxide as opposed to oxygen in hemoglobin.

Uncompetitive inhibitors, alternatively, bind concurrently with substrate at active sites. Upon binding to an enzyme substrate (ES) complex, an enzyme substrate inhibitor (ESI) complex is formed. Similar to competitive inhibitors, the rate at product formation is decreased also.[4]

Lastly, mixed inhibitors are able to bind to both the free enzyme and the enzyme-substrate complex. However, in contrast to competitive and uncompetitive inhibitors, mixed inhibitors bind to the allosteric site. Allosteric binding induces conformational changes that may increase the protein's affinity for substrate. This phenomenon is called positive modulation. Conversely, allosteric binding that decreases the protein's affinity for substrate is negative modulation.[18]

Types

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Active site

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Main article: Active site

At the active site, a substrate binds to an enzyme to induce a chemical reaction.[19][20] Substrates, transition states, and products can bind to the active site, as well as any competitive inhibitors.[19] For example, in the context of protein function, the binding of calcium to troponin in muscle cells can induce a conformational change in troponin. This allows for tropomyosin to expose the actin-myosin binding site to which the myosin head binds to form a cross-bridge and induce a muscle contraction.[21]

In the context of the blood, an example of competitive binding is carbon monoxide which competes with oxygen for the active site on heme. Carbon monoxide's high affinity may outcompete oxygen in the presence of low oxygen concentration. In these circumstances, the binding of carbon monoxide induces a conformation change that discourages heme from binding to oxygen, resulting in carbon monoxide poisoning.[4]

Competitive and noncompetitive enzyme binding at active and regulatory (allosteric) site respectively.

Allosteric site

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At the regulatory site, the binding of a ligand may elicit amplified or inhibited protein function.[4][22] The binding of a ligand to an allosteric site of a multimeric enzyme often induces positive cooperativity, that is the binding of one substrate induces a favorable conformation change and increases the enzyme's likelihood to bind to a second substrate.[23] Regulatory site ligands can involve homotropic and heterotropic ligands, in which single or multiple types of molecule affects enzyme activity respectively.[24]

Enzymes that are highly regulated are often essential in metabolic pathways. For example, phosphofructokinase (PFK), which phosphorylates fructose in glycolysis, is largely regulated by ATP. Its regulation in glycolysis is imperative because it is the committing and rate limiting step of the pathway. PFK also controls the amount of glucose designated to form ATP through the catabolic pathway. Therefore, at sufficient levels of ATP, PFK is allosterically inhibited by ATP. This regulation efficiently conserves glucose reserves, which may be needed for other pathways. Citrate, an intermediate of the citric acid cycle, also works as an allosteric regulator of PFK.[24][25]

Single- and multi-chain binding sites

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Binding sites can be characterized also by their structural features. Single-chain sites (of "monodesmic" ligands, μόνος: single, δεσμός: binding) are formed by a single protein chain, while multi-chain sites (of "polydesmic" ligands, πολοί: many)[26] are frequent in protein complexes, and are formed by ligands that bind more than one protein chain, typically in or near protein interfaces. Recent research shows that binding site structure has profound consequences for the biology of protein complexes (evolution of function, allostery).[27][28]

Cryptic binding sites

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Cryptic binding sites are the binding sites that are transiently formed in an apo form or that are induced by ligand binding. Considering the cryptic binding sites increases the size of the potentially "druggable" human proteome from ~40% to ~78% of disease-associated proteins.[29] The binding sites have been investigated by: support vector machine applied to "CryptoSite" data set,[29] Extension of "CryptoSite" data set,[30] long timescale molecular dynamics simulation with Markov state model and with biophysical experiments,[31] and cryptic-site index that is based on relative accessible surface area.[32]

Binding curves

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Sigmoidal versus hyperbolic binding patterns demonstrate cooperative and noncooperative character of enzymes.

Binding curves describe the binding behavior of ligand to a protein. Curves can be characterized by their shape, sigmoidal or hyperbolic, which reflect whether or not the protein exhibits cooperative or noncooperative binding behavior respectively.[33] Typically, the x-axis describes the concentration of ligand and the y-axis describes the fractional saturation of ligands bound to all available binding sites.[4] The Michaelis Menten equation is usually used when determining the shape of the curve. The Michaelis Menten equation is derived based on steady-state conditions and accounts for the enzyme reactions taking place in a solution. However, when the reaction takes place while the enzyme is bound to a substrate, the kinetics play out differently.[34]

Modeling with binding curves are useful when evaluating the binding affinities of oxygen to hemoglobin and myoglobin in the blood. Hemoglobin, which has four heme groups, exhibits cooperative binding. This means that the binding of oxygen to a heme group on hemoglobin induces a favorable conformation change that allows for increased binding favorability of oxygen for the next heme groups. In these circumstances, the binding curve of hemoglobin will be sigmoidal due to its increased binding favorability for oxygen. Since myoglobin has only one heme group, it exhibits noncooperative binding which is hyperbolic on a binding curve.[35]

Applications

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Biochemical differences between different organisms and humans are useful for drug development. For instance, penicillin kills bacteria by inhibiting the bacterial enzyme DD-transpeptidase, destroying the development of the bacterial cell wall and inducing cell death. Thus, the study of binding sites is relevant to many fields of research, including cancer mechanisms,[36] drug formulation,[37] and physiological regulation.[38] The formulation of an inhibitor to mute a protein's function is a common form of pharmaceutical therapy.[39]

Methotrexate inhibits dihydrofolate reductase by outcompeting the substrate folic acid. Binding site in blue, inhibitor in green, and substrate in black.

In the scope of cancer, ligands that are edited to have a similar appearance to the natural ligand are used to inhibit tumor growth. For example, methotrexate, a chemotherapeutic, acts as a competitive inhibitor at the dihydrofolate reductase active site.[40] This interaction inhibits the synthesis of tetrahydrofolate, shutting off production of DNA, RNA and proteins.[40] Inhibition of this function represses neoplastic growth and improves severe psoriasis and adult rheumatoid arthritis.[39]

In cardiovascular illnesses, drugs such as beta blockers are used to treat patients with hypertension. Beta blockers (β-Blockers) are antihypertensive agents that block the binding of the hormones adrenaline and noradrenaline to β1 and β2 receptors in the heart and blood vessels. These receptors normally mediate the sympathetic "fight or flight" response, causing constriction of the blood vessels.[41]

Competitive inhibitors are also largely found commercially. Botulinum toxin, known commercially as Botox, is a neurotoxin that causes flaccid paralysis in the muscle due to binding to acetylcholine dependent nerves. This interaction inhibits muscle contractions, giving the appearance of smooth muscle.[42]

A number of computational tools have been developed for the prediction of the location of binding sites on proteins.[22][43][44][45] These can be broadly classified into sequence based or structure based.[44] Sequence based methods rely on the assumption that the sequences of functionally conserved portions of proteins such as binding site are conserved. Structure based methods require the 3D structure of the protein. These methods in turn can be subdivided into template and pocket based methods.[44] Template based methods search for 3D similarities between the target protein and proteins with known binding sites. The pocket based methods search for concave surfaces or buried pockets in the target protein that possess features such as hydrophobicity and hydrogen bonding capacity that would allow them to bind ligands with high affinity.[44] Even though the term pocket is used here, similar methods can be used to predict binding sites used in protein-protein interactions that are usually more planar, not in pockets.[46]

References

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Binding site

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In biochemistry and , a binding site is a specific on a , such as a protein, where another —known as a —binds reversibly and noncovalently with high specificity and affinity, enabling key biological functions like enzymatic and . These sites are typically pockets or cavities formed by the three-dimensional folding of the , involving residues that interact with the through forces such as hydrogen bonding, electrostatic interactions, van der Waals forces, and hydrophobic effects. The strength of this binding is quantified by the (Kd), which ranges from millimolar for weak interactions to picomolar or femtomolar for tight ones, reflecting the site's evolutionary optimization for physiological efficiency. Binding sites play pivotal roles across cellular processes, with distinct types tailored to their functions; for instance, active sites in enzymes accommodate substrates to facilitate chemical reactions, while allosteric sites bind regulatory molecules to modulate protein activity. In receptors, ligand-binding sites on membrane-bound proteins like G-protein-coupled receptors initiate signaling cascades upon or attachment. Beyond proteins, binding sites occur on nucleic acids, such as binding motifs on DNA, underscoring their ubiquity in molecular recognition. Structural techniques like , NMR , and cryo-electron have elucidated these sites' architectures, revealing dynamic conformational changes upon binding that enhance specificity. The study of binding sites is fundamental to fields like and , as they serve as primary targets for ; small molecules mimicking natural ligands can occupy these sites to inhibit or activate proteins, as seen in inhibitors binding to active sites. Computational methods, including sequence-based and structure-based predictions, aid in identifying cryptic or transient sites, advancing predictions of protein-ligand interactions and functional annotations. Notable examples include the avidin-biotin complex, exemplifying one of the strongest noncovalent interactions (Kd ≈ 10−15 M), and thrombin-hirudin binding, critical for mechanisms.

Overview

Definition

A binding site is a specific or on a , such as a protein or , where a —exemplified by a substrate, inhibitor, or effector—binds selectively through non-covalent interactions. These interactions include hydrogen bonds, van der Waals forces, electrostatic forces, and hydrophobic effects, which collectively stabilize the ligand-biomolecule complex without forming covalent bonds. Binding at these sites is reversible and exhibits high specificity, explained by models such as the lock-and-key hypothesis proposed by in 1894, which posits a rigid complementary fit between the and site, or the induced fit model developed by Daniel E. Koshland in 1958, where binding induces conformational changes in the to enhance interaction precision. The affinity of this binding, reflecting its strength, is quantified by the KdK_d, defined as the concentration at which half the sites are occupied at equilibrium. This affinity governs the stability of the complex and is crucial for functional outcomes. The binding equilibrium is represented as: Ligand+Receptor⇌Complex\text{Ligand} + \text{Receptor} \rightleftharpoons \text{Complex} with Kd=[L][R][LR]K_d = \frac{[\text{L}][\text{R}]}{[\text{LR}]} where [L][\text{L}], [R][\text{R}], and [LR][\text{LR}] denote the concentrations of free ligand, free receptor, and the ligand-receptor complex, respectively. The notion of binding sites originated in the early from enzyme-substrate interaction studies by and in , who introduced a model of reversible binding to explain enzymatic reaction kinetics, laying the foundation for understanding molecular recognition.

Biological Significance

Binding sites are pivotal in facilitating essential cellular processes, including where substrates bind to active sites to accelerate biochemical reactions, through receptor-ligand interactions that propagate intracellular messages, molecular transport such as oxygen delivery via carrier proteins, and immune responses where antigen-binding sites on antibodies recognize and neutralize pathogens. Disruptions in binding sites, often due to genetic mutations, can lead to severe physiological impairments and diseases; for instance, altering enzyme binding sites result in metabolic deficiencies like , where impaired activity causes toxic accumulation of metabolites. Binding sites exhibit strong evolutionary conservation owing to their critical functional roles, with residues in these regions evolving more slowly under purifying selection, which allows for targeted that exploits similarities across species. A large proportion of proteins in the human proteome are predicted to contain ligand-binding sites, which significantly influence by affecting drug distribution and , as well as by modulating therapeutic efficacy through target engagement. A representative example is hemoglobin's oxygen-binding sites, which enable that enhances oxygen loading in the lungs and unloading in tissues, ensuring efficient respiratory transport.

Structural Features

Composition and Location

Binding sites in biomolecules, particularly proteins and nucleic acids, are primarily composed of specific amino acid residues or nucleotide sequences arranged within structural features such as pockets, clefts, or surface grooves. In proteins, these sites frequently incorporate polar amino acids like serine, threonine, asparagine, and glutamine to form hydrogen bonds with ligands, alongside aromatic residues such as phenylalanine, tyrosine, and tryptophan that support π-stacking interactions. In nucleic acid-binding contexts, protein sites interacting with DNA or RNA often feature basic residues like arginine and lysine for electrostatic interactions with phosphate backbones, complemented by polar groups for specific base recognition. These compositional elements are typically embedded in hydrophobic environments but designed to remain accessible, allowing ligand entry while maintaining structural integrity. The location of binding sites varies significantly, occurring either intrachain within a single polypeptide chain or interchain at interfaces between multiple chains. Intrachain sites, common in enzymes, are often internal pockets shielded by the protein fold, as seen in the catalytic cleft of serine proteases. In contrast, interchain sites predominate at quaternary structure interfaces in multi-subunit complexes, such as the 2,3-bisphosphoglycerate (2,3-BPG) binding site in deoxyhemoglobin, located at the interface involving beta subunits. Solvent-exposed sites on extracellular receptors, like those in G-protein coupled receptors, facilitate interactions with hydrophilic ligands in aqueous environments, whereas deeply buried pockets in intracellular enzymes protect reactive intermediates from solvent interference. The positioning of binding sites is largely dictated by into tertiary and structures, where hydrophobic collapse and secondary element packing create defined cavities or interfaces. Post-translational modifications, such as , further modulate site location and accessibility by adding bulky moieties that can sterically hinder or reposition surface-exposed regions, as observed in many membrane proteins. These factors ensure that binding sites are optimally oriented for physiological interactions. The composition and arrangement of residues in these sites also underpin specificity, though detailed mechanisms are addressed elsewhere. Typical binding sites accommodating small-molecule ligands exhibit volumes ranging from approximately 150 to 600 ų, providing sufficient space for precise molecular recognition without excessive flexibility.

Specificity Determinants

The specificity of binding sites is primarily governed by shape complementarity, which ensures a steric fit between the binding pocket and the , minimizing unfavorable van der Waals clashes and maximizing contact area. This geometric matching is complemented by electrostatic interactions, including charge distribution that aligns complementary polar groups, and bonding networks that form directional, specific connections between donor and acceptor atoms on the binding partners. For instance, in protein-protein interfaces, bonds contribute significantly to selectivity, with antibody-antigen complexes exhibiting an average of 7.6 such bonds, predominantly involving the heavy chain complementarity-determining regions (CDRs). Two classical models describe how these determinants facilitate recognition: the rigid lock-and-key model, proposed by in 1894, posits that the binding site maintains a fixed conformation precisely complementary to the , akin to a key fitting a lock, emphasizing inherent structural specificity. In contrast, the , introduced by Daniel Koshland in 1958, accounts for conformational flexibility, where initial binding induces adjustments in the binding site's structure to achieve optimal complementarity, enhancing specificity through dynamic adaptation while excluding non-cognate ligands that fail to stabilize the fitted state. This flexibility is crucial for sites that must accommodate varied ligands without compromising selectivity. Water molecules play a pivotal role in modulating specificity by either bridging interactions between the binding site and via bonds or being excluded from hydrophobic pockets to drive binding. In trypsin-ligand complexes, for example, conserved molecules mediate polar contacts in the S1 pocket, stabilizing specific ligand orientations, while displacement of buried waters upon binding releases , favoring high-affinity interactions and excluding mismatched ligands that cannot fully desolvate the site. These water-mediated effects fine-tune selectivity by compensating for imperfect direct contacts. Quantitatively, specificity is reflected in the binding free energy, ΔG=ΔH−TΔS\Delta G = \Delta H - T\Delta S, where the enthalpic term (ΔH\Delta H) arises from favorable specific interactions like hydrogen bonds and , and the entropic term (−TΔS-T\Delta S) incorporates desolvation penalties and conformational restrictions, with optimal specificity achieved when ΔG\Delta G is sufficiently negative for ligands but positive for others. A representative example is antibody-antigen binding, where hypervariable loops (CDRs) in the variable domains form a diverse that achieves high specificity through canonical conformations tailored to epitopes, as seen in structures where CDR-H3 loops position key residues for precise recognition.

Functions

Catalysis

Binding sites in enzymes, often referred to as active sites, play a central role in by binding substrates in precise orientations that promote chemical transformations. This binding stabilizes the of the reaction, thereby lowering the barrier and accelerating the reaction rate compared to the uncatalyzed process. The mechanisms employed by these sites include substrate orientation to bring reactive groups into proximity, acid-base where residues donate or accept protons to facilitate bond breaking and formation, covalent catalysis involving transient enzyme-substrate intermediates, and electrostatic stabilization that neutralizes charges in the through interactions with charged residues or metal ions. The kinetics of enzymatic are commonly described by the Michaelis-Menten model, which quantifies the relationship between substrate concentration and reaction velocity. In this framework, the initial velocity vv is given by: v=Vmax⁡[S]Km+[S]v = \frac{V_{\max} [S]}{K_m + [S]} where Vmax⁡V_{\max} is the maximum velocity at saturating substrate concentration [S][S], and KmK_m is the Michaelis constant representing the substrate concentration at which v=12Vmax⁡v = \frac{1}{2} V_{\max}. For many enzymes, KmK_m approximates the KdK_d of the enzyme-substrate complex when the catalytic step is rate-limiting, providing insight into binding affinity at the . Representative examples illustrate these principles in action. In serine proteases such as , the features a consisting of serine, , and aspartate residues; the acts as a base to deprotonate the serine hydroxyl, enabling nucleophilic attack on the and formation of a covalent acyl-enzyme intermediate, which is subsequently hydrolyzed. Similarly, in the metalloenzyme , a coordinated within the polarizes a bound , facilitating its deprotonation to generate a hydroxide nucleophile that attacks , yielding ; this electrostatic and acid-base mechanism achieves rapid interconversion essential for physiological regulation and respiration. These catalytic strategies enable enzymes to achieve extraordinary rate enhancements, with some reactions accelerated by up to 102010^{20}-fold relative to their uncatalyzed counterparts, underscoring the evolutionary optimization of binding sites for transition-state complementarity.

Regulation and Inhibition

Binding sites play a crucial role in regulating enzymatic activity through the binding of effectors that induce conformational changes, thereby activating or inhibiting the enzyme. Allosteric effectors bind to specific regulatory sites distinct from the , altering the enzyme's shape and modulating substrate affinity or catalytic efficiency; for instance, positive effectors stabilize the active conformation, while negative effectors promote an inactive state. This mechanism allows precise control of metabolic pathways by responding to cellular signals. at regulatory binding sites, often on serine, , or residues, introduces negative charges that can repel substrates or attract inhibitory proteins, thereby reducing activity; in bacterial metabolism, such modifications have been shown to control fluxes in and other pathways by altering . Cofactor binding to dedicated sites can similarly regulate enzymes by facilitating or hindering conformational shifts necessary for , ensuring activity aligns with nutrient availability. Enzyme inhibition occurs when molecules bind to binding sites and impede function, with reversible types classified by their interaction with the enzyme-substrate (ES) complex. Competitive inhibitors bind directly to the , competing with the substrate and increasing the apparent Michaelis constant (Km) without affecting maximum velocity (Vmax), as higher substrate concentrations can outcompete the inhibitor. The apparent Km in competitive inhibition is given by: Kmapp=Km(1+[I]Ki)K_{m}^{app} = K_m \left(1 + \frac{[I]}{K_i}\right) where [I] is the inhibitor concentration and Ki is the inhibitor dissociation constant. Non-competitive inhibitors bind to a separate site on the or ES complex, reducing Vmax by decreasing the number of functional enzymes while leaving Km unchanged, as they do not interfere with substrate binding. Uncompetitive inhibitors bind exclusively to the ES complex, forming an ESI complex that cannot proceed to product. This lowers both Km (by shifting the E + S ⇌ ES equilibrium toward ES per ) and Vmax (by reducing the effective concentration of productive ES). A prominent example of regulatory inhibition is feedback inhibition in metabolic pathways, where end products bind to upstream sites to prevent overproduction. In , ATP acts as an allosteric inhibitor of phosphofructokinase-1 (PFK-1) by binding a regulatory site, inducing a conformational change that reduces substrate affinity and slows the pathway when energy is abundant; this mechanism maintains cellular ATP by integrating energy status with glycolytic .

Types

Active Sites

The active site of an is the specific region where substrate molecules bind and the catalytic reaction takes place, forming a transient enzyme-substrate complex that facilitates the conversion to products. This site typically consists of a cleft or groove on the enzyme's surface, formed by residues contributed from different segments of the polypeptide chain that are brought into proximity through the protein's tertiary structure. These residues often include catalytic groups, such as the serine-histidine-aspartate triad in serine proteases, which directly participate in bond breaking and forming, and may incorporate cofactors like metal ions to enhance reactivity. Active sites exhibit several key characteristics that ensure their efficiency in . They are generally small, encompassing an average of 7 to 25 residues within a localized region close to the reaction center, allowing for precise substrate positioning and minimal interference from the surrounding protein scaffold. This compact nature contributes to high specificity, as the site's geometry and chemical properties are finely tuned to the substrate's shape and electronic requirements. Moreover, active sites display high structural and conservation across homologous enzymes, reflecting to maintain catalytic function; for instance, the core residues in active sites remain invariant despite variations in the overall protein . A prominent example of an feature is the hole in , a , where the backbone groups of 193 and serine 195 form bonds that stabilize the negatively charged in the tetrahedral during . This stabilization lowers the by approximately 2.6 kcal/mol, as evidenced by slower deacylation rates in mutants lacking a functional hole. In ribozymes, RNA-based enzymes, s similarly orchestrate ; the ribozyme's , located at the junction of loops A and B, positions nucleotide G+1 to form bonds with residues like A38 and C25, enabling cleavage with a rate enhancement of approximately 10^5- to 10^6-fold compared to the uncatalyzed reaction. From an evolutionary perspective, s demonstrate low sequence variability in their catalytic residues due to stringent functional constraints, which tightly limit tolerable substitutions to preserve reaction specificity and efficiency. This conservation extends beyond immediate regions, influencing long-range protein stability and folding to support , as seen in superfamilies where key motifs persist despite divergent overall sequences. Such evolutionary pressures highlight the active site's role as a focal point for selective optimization in enzyme function.

Allosteric Sites

Allosteric sites are distinct regions on a protein, separate from the , where effector molecules bind to induce conformational changes that modulate the protein's activity at a remote functional site. This phenomenon, known as allostery, involves the transmission of structural or dynamic perturbations from the allosteric site to the , often through interconnected networks of residues. The concept was formalized in seminal models, including the Monod-Wyman-Changeux (MWC) model, which posits that allosteric proteins exist in equilibrium between tense (T) and relaxed (R) conformational states, with effectors stabilizing one state to alter binding affinity. In contrast, the Koshland-Némethy-Filmer (KNF) sequential model describes induced-fit mechanisms where binding at the allosteric site sequentially alters subunit conformations, promoting cooperative interactions. These sites are frequently located at subunit interfaces in multimeric proteins, facilitating global structural shifts upon effector binding. For instance, allosteric effectors can shift the T-to-R equilibrium in the MWC framework, enhancing or inhibiting activity by changing the protein's overall conformation. Such sites are typically less conserved evolutionarily than active sites, allowing selective modulation without disrupting core function. A classic example is human hemoglobin, where the active sites at the heme groups bind oxygen, while the allosteric site in the central cavity binds 2,3-bisphosphoglycerate (2,3-BPG), reducing oxygen affinity to facilitate release in tissues. Structural studies reveal that 2,3-BPG interacts with positively charged residues in the β-subunits of the deoxy (T-state) form, stabilizing this low-affinity conformation. Another prominent case is Escherichia coli aspartate transcarbamoylase (ATCase), a key enzyme in pyrimidine biosynthesis, where the allosteric site binds cytidine triphosphate (CTP) to inhibit activity or ATP to activate it, demonstrating heterotropic regulation. In ATCase, CTP binding at the regulatory subunit interface promotes the T-state, reducing substrate affinity at the catalytic sites. The degree of cooperativity induced by allosteric binding is quantified by the Hill coefficient (nH), derived from the Hill equation, which describes sigmoidal binding curves. A value of nH > 1 indicates positive , as seen in where oxygen binding yields nH ≈ 2.8, reflecting enhanced affinity after initial binding due to allosteric transitions. This metric underscores how allosteric sites amplify regulatory responses in biological systems.

Cryptic and Accessory Sites

Cryptic binding sites are latent pockets within proteins that remain hidden or collapsed in the apo (unbound) state but become accessible upon conformational changes induced by binding or environmental factors. These sites are particularly valuable in , as they enable targeting of "undruggable" proteins lacking obvious surface pockets, thereby expanding the chemical space for therapeutic intervention. For instance, in , cryptic sites in the kinase domain can be revealed by type II or III inhibitors, which stabilize inactive conformations and allow binding in regions not evident in the active , as seen in inhibitors targeting (PKA). Such sites often involve transient openings near the ATP-binding cleft, providing selectivity over conserved active sites. Accessory binding sites encompass supplementary regions on proteins that facilitate interactions beyond primary catalytic or regulatory functions, distinguished by their involvement of single polypeptide chains (intramolecular) or multiple chains (oligomeric interfaces). Single-chain accessory sites occur within a single polypeptide, enabling intramolecular stabilization or modulation, whereas multi-chain sites form at subunit interfaces in oligomeric proteins, often driving assembly or signaling. In receptors, dimerization sites serve as key examples of multi-chain accessory sites; for instance, the undergoes ligand-induced dimerization at an interface involving residues from two receptor chains, which activates downstream signaling without direct enzymatic activity. Similarly, in Fc regions, multi-chain accessory sites at the interface of the two heavy chains bind Fcγ receptors on immune cells, mediating effector functions like . Recent advances since 2020 have leveraged AI and computational methods to uncover cryptic sites, particularly in challenging targets like . Machine learning-driven simulations, such as those using enhanced sampling and Markov state models, have identified dynamic cryptic pockets in that expose upon conformational shifts, informing the design of selective inhibitors for oncogenic mutants. Tools like DynamicBind further predict ligand-specific cryptic pocket openings in and similar proteins, highlighting transient states critical for allosteric modulation. These AI approaches emphasize the role of protein dynamics in revealing sites inaccessible in static structures, advancing targeting of previously intractable targets.

Binding Dynamics

Equilibrium Binding and Curves

In equilibrium binding, the interaction between a ligand (L) and a binding site (R) follows the , where the forward association and reverse dissociation rates balance to yield a steady-state complex (RL) concentration governed by the equilibrium Kd=[R][L][RL]K_d = \frac{[R][L]}{[RL]}. This principle underpins the fractional occupancy θ=[RL][Rtotal]\theta = \frac{[RL]}{[R_{total}]}, which describes the proportion of sites occupied at equilibrium as a function of ligand concentration. To analyze binding data, the Scatchard plot linearizes the equilibrium relationship by graphing the ratio of bound to free (BF\frac{B}{F}) against bound (BB), yielding a straight line for single-site binding with slope −1/Kd-1/K_d and x-intercept equal to the total number of binding sites (BmaxB_{max}). This transformation, introduced in 1949, facilitates the extraction of affinity (KdK_d) and site density from experimental saturation data without assuming specific curve shapes. Binding curves typically depict the fractional occupancy θ\theta versus ligand concentration [L][L]. For non-cooperative single-site binding, the curve is hyperbolic, described by the Langmuir isotherm : θ=[L]Kd+[L]\theta = \frac{[L]}{K_d + [L]} This reflects independent site , reaching half-saturation at [L]=Kd[L] = K_d. In contrast, in multi-subunit proteins produces a sigmoidal , approximated by the Hill θ=[L]nKd+[L]n\theta = \frac{[L]^n}{K_d + [L]^n}, where n>1n > 1 indicates positive that steepens the transition from low to high . The KdK_d is sensitive to environmental factors, with increases in generally weakening binding by elevating KdK_d due to enhanced dissociation, while shifts can alter KdK_d by protonating or deprotonating residues at the binding interface. arises prominently in multi-site proteins like , where initial binding enhances subsequent affinities, but its extent varies with site interactions. In pharmacology, receptor-ligand saturation curves illustrate these principles; for instance, agonist binding to G-protein-coupled receptors often follows hyperbolic kinetics for single-site models, enabling estimation of therapeutic concentrations where occupancy exceeds 50% for efficacy.

Kinetic Models

The kinetics of ligand binding to a binding site are governed by the rates of association and dissociation. The association rate is described by the second-order rate equation kon[L][R]k_{\text{on}} [L][R], where konk_{\text{on}} is the association rate constant (typically in M−1^{-1} s−1^{-1}), [L] is the free ligand concentration, and [R] is the free receptor concentration. The dissociation rate follows the first-order equation koff[RL]k_{\text{off}} [RL], where koffk_{\text{off}} is the dissociation rate constant (in s−1^{-1}) and [RL] is the concentration of the ligand-receptor complex. These rates determine the temporal dynamics of complex formation and breakdown, with the equilibrium dissociation constant related as Kd=koffkonK_d = \frac{k_{\text{off}}}{k_{\text{on}}}. Binding processes can be modeled as simple one-step reactions or more complex multi-step mechanisms. In the simple one-step model, the binds directly to the receptor to form the RL complex without additional intermediates, allowing rapid equilibration under favorable conditions. Multi-step models, such as the induced fit mechanism proposed by Koshland, incorporate intermediate states where initial binding induces a conformational change in the receptor, transitioning from a loose complex to a tighter, catalytically competent state. This induced fit step can introduce kinetic barriers that influence overall binding efficiency and specificity. Experimental measurement of these kinetic parameters often employs stopped-flow techniques, which mix reactants rapidly and monitor changes in or on timescales from milliseconds to seconds, capturing association and dissociation events that are too fast for conventional methods. In diffusion-controlled binding, the association rate konk_{\text{on}} is limited by the physical encounter of and receptor, typically reaching values of 10810^8 to 10910^9 M−1^{-1} s−1^{-1} for enzymes like , where substrate capture occurs near the theoretical diffusion limit. By contrast, in enzymes such as that operate via induced fit, the kinetics are often dominated by slower conformational rearrangements following initial binding, reducing the effective konk_{\text{on}} and extending the timescale of complex formation.

Characterization Methods

Experimental Approaches

Experimental approaches to characterize binding sites rely on direct biophysical and biochemical measurements to provide of site location, , affinity, and dynamics in proteins. These methods complement each other by addressing different aspects, such as static atomic details, thermodynamic parameters, kinetic rates, and functional roles of residues, often applied to purified proteins or complexes in solution or crystalline states. X-ray crystallography remains a cornerstone for determining high-resolution atomic structures of binding sites, particularly through co-crystallization of proteins with ligands to capture the bound conformation and reveal precise interactions like bonds and van der Waals contacts. This technique has been instrumental in mapping active sites in enzymes, such as the catalytic pocket of protease bound to inhibitors, achieving resolutions below 2 to delineate residue-ligand geometries. Limitations include the need for crystallizable samples, which can bias toward rigid conformations, but advancements in sources have enhanced throughput for fragment screening at binding sites. Nuclear magnetic resonance (NMR) excels in probing the dynamics and conformational changes at binding sites in solution, using techniques like perturbation to identify ligand-induced shifts in protein resonances near the interaction interface. For instance, in NMR reveals transient states and flexibility in allosteric sites, as seen in studies of where calcium binding alters helix orientations. This method is particularly valuable for smaller proteins (<50 kDa) and provides site-specific information without crystallization, though it requires isotopic labeling for larger systems. Isothermal titration calorimetry (ITC) directly measures the thermodynamics of binding site interactions by quantifying heat changes upon ligand titration, yielding parameters such as enthalpy (ΔH), dissociation constant (K_d), and stoichiometry without labels. In applications to protein-ligand complexes, ITC has characterized the exothermic binding of inhibitors to kinase active sites, revealing entropic contributions from solvent release. The technique's sensitivity to weak interactions (μM to mM range) makes it ideal for validating site affinity under physiological conditions. Surface plasmon resonance (SPR) enables real-time monitoring of binding kinetics at sites by detecting refractive index changes as analytes flow over immobilized proteins, providing association (k_on) and dissociation (k_off) rates to compute K_d. For example, SPR has quantified the rapid on-off kinetics of peptide binding to MHC class I grooves, highlighting site-specific dwell times. This label-free approach is suited for membrane proteins in lipid environments and supports high-throughput screening of site variants. Fluorescence quenching assays, often using intrinsic tryptophan residues, detect binding site proximity by monitoring emission intensity decreases upon ligand approach, indicating static or dynamic quenching mechanisms. In hemoglobin studies, quenching of heme-proximal tryptophans by oxygen analogs has mapped gas-binding pockets, with Stern-Volmer analysis estimating affinity. This sensitive, non-invasive method is widely used for initial screening but requires careful controls for non-specific effects. Site-directed mutagenesis, including alanine scanning, confirms the functional roles of specific residues in binding sites by substituting them and assessing impacts on affinity or activity via downstream assays. Alanine scanning of zinc finger DNA-binding domains has identified key contacts, with mutants showing up to 1000-fold affinity losses for altered sites. This genetic approach integrates with biophysical readouts to pinpoint hotspots, though it may overlook compensatory effects in flexible regions. Cryogenic electron microscopy (cryo-EM), advanced since the 2010s with direct electron detectors, resolves flexible binding sites in near-native states by averaging thousands of particle images, overcoming crystallization challenges for large or dynamic complexes. For instance, cryo-EM has visualized conformational ensembles in GPCR ligand-binding pockets, achieving 3-4 Å resolution for transient states previously inaccessible. This method's ability to handle heterogeneity has revolutionized studies of accessory sites in membrane proteins.

Computational Techniques

Computational techniques play a crucial role in predicting and simulating binding sites on proteins, enabling the identification of potential interaction regions without relying solely on experimental data. These in silico methods include molecular docking, which positions ligands within protein pockets to estimate binding poses and affinities, and molecular dynamics (MD) simulations, which model the dynamic behavior of binding sites over time. Such approaches complement experimental validation by providing atomic-level insights into site flexibility and ligand interactions. Molecular docking tools, such as , facilitate the placement of small-molecule ligands into protein binding sites by exploring conformational space and scoring potential poses based on intermolecular energies. employs a Lamarckian genetic algorithm to optimize ligand orientations within predefined grid maps of the protein's binding region, making it widely used for structure-based drug discovery. For instance, has been applied to predict ligand binding in various enzyme active sites, achieving reliable pose predictions when validated against crystallographic data. MD simulations extend docking by capturing the flexibility of binding sites on timescales ranging from nanoseconds (ns) to microseconds (µs), revealing conformational changes that influence ligand binding. These simulations solve Newton's equations of motion for all atoms in the protein-ligand-solvent system, allowing observation of transient pockets or induced-fit mechanisms that static docking might overlook. Studies have shown that µs-scale MD can sample rare events like ligand unbinding or allosteric transitions, providing quantitative measures of site dynamics such as root-mean-square fluctuations in pocket residues. Pocket detection tools like CASTp identify potential binding sites by computing the surface topography of proteins, quantifying the geometry of cavities and voids accessible to ligands. CASTp uses alpha shapes and solvent-accessible surfaces to delineate pockets, reporting metrics such as volume and area, which help prioritize druggable sites. This tool has been instrumental in annotating binding pockets in approximately 5,000 protein structures from the Protein Data Bank. Machine learning advancements, exemplified by AlphaFold3 released in 2024, further enhance site annotation by predicting protein-ligand complexes with high accuracy, including the positioning of small molecules in native binding pockets even for unliganded proteins. AlphaFold3's diffusion-based architecture achieves median ligand root-mean-square deviations below 2 Å for many targets, outperforming prior models in interaction prediction. High-throughput virtual screening (HTVS) leverages docking and scoring functions to evaluate millions of compounds against predicted binding sites, rapidly identifying potential inhibitors. HTVS pipelines, often integrated with tools like AutoDock Vina, filter libraries for favorable binding geometries and energies, reducing experimental testing to top hits. For example, HTVS has successfully prioritized SARS-CoV-2 main protease inhibitors from large chemical databases, with hit rates improved by rescoring with more accurate methods. To refine binding predictions, free energy perturbation (FEP) calculates absolute or relative binding free energies by simulating alchemical transformations between ligand states in protein and solvent environments. FEP provides rigorous thermodynamic estimates, with recent implementations achieving correlation coefficients above 0.7 with experimental affinities for diverse targets, establishing it as a gold standard for lead optimization. Recent advances in 2025 integrate AI models like RoseTTAFold All-Atom to predict cryptic binding sites, which are transient pockets not evident in static structures. RoseTTAFold All-Atom, an extension of diffusion and SE(3)-equivariant networks, models full biomolecular assemblies including ligands and cofactors, enabling the de novo design of binders to hidden sites. When combined with MD, these AI tools have expanded cryptic site discovery, identifying druggable regions in proteins like KRAS mutants with prediction accuracies surpassing 80% for pocket detection. Such integrations mark a shift toward generative AI for proactive binding site exploration, beyond traditional template-based methods.

Applications

Drug Design and Therapeutics

Structure-based drug design (SBDD) leverages detailed knowledge of binding sites to develop targeted inhibitors, particularly for enzyme active sites. In the case of HIV protease, a critical enzyme in the viral life cycle, SBDD has enabled the creation of potent inhibitors like saquinavir and ritonavir by mapping interactions within the active site cleft, allowing precise optimization of hydrogen bonding and hydrophobic contacts to achieve nanomolar affinities. This approach has been instrumental in antiretroviral therapy, where inhibitors mimic peptide substrates to occupy the dimeric enzyme's catalytic pocket, blocking polyprotein cleavage essential for viral maturation. Despite these advances, targeting binding sites presents significant challenges, including off-target binding that can lead to toxicity and resistance mutations that alter site geometry or affinity. Off-target effects arise when inhibitors bind unintended proteins with similar pockets, necessitating selectivity optimization through structure-activity relationship studies to minimize polypharmacology risks. Resistance, often driven by point mutations in the binding site (e.g., in kinases or proteases), reduces drug residence time and efficacy, prompting iterative design of second-generation inhibitors that accommodate or evade these changes. For "undruggable" targets with shallow or transient sites, allosteric modulators offer a solution by binding remote pockets to induce conformational changes that inhibit function without competing at the orthosteric site, as exemplified by sotorasib's covalent targeting of the switch-II pocket in KRAS G12C mutants. Key successes in binding site-targeted therapeutics include imatinib, which revolutionized chronic myeloid leukemia treatment by selectively inhibiting the ATP-binding site of the BCR-ABL kinase fusion protein, achieving clinical remission in over 90% of patients at diagnosis through precise occupation of the inactive conformation pocket. Monoclonal antibodies further exemplify this strategy, with trastuzumab binding the extracellular domain of the HER2 receptor to block ligand association at the dimerization site, thereby halting signaling in HER2-positive breast cancers and improving survival rates. For apoptosis regulators like BCL-2, venetoclax targets the BH3-binding groove to displace pro-apoptotic proteins, demonstrating high efficacy in chronic lymphocytic leukemia with response rates exceeding 70% in relapsed cases. Potency in these designs is routinely assessed via IC50 values, which quantify the inhibitor concentration needed for 50% target occupancy or activity inhibition, guiding lead optimization toward sub-nanomolar ranges for clinical viability. Accessibility and pharmacokinetics are evaluated through ADMET profiling, where structure-based predictions of site exposure (e.g., via solvent-accessible surface area) inform modifications to enhance membrane permeability and metabolic stability, ensuring therapeutic concentrations at the binding site.

Biotechnology and Engineering

In biotechnology, directed evolution has been employed to optimize binding sites in enzymes, enhancing their specificity for industrial applications. This iterative process involves generating mutant libraries through random mutagenesis or recombination and screening for improved binding affinity or selectivity, mimicking natural evolution on a laboratory timescale. For instance, directed evolution of cytochrome P450 enzymes has yielded variants with altered substrate binding sites that exhibit up to 100-fold higher specificity for non-natural substrates, enabling efficient biocatalysis in pharmaceutical synthesis. Similarly, evolution of hydrolases has refined active site binding pockets to preferentially interact with specific lignocellulosic substrates, boosting degradation efficiency in biofuel processing. De novo design of binding sites leverages computational libraries to create novel proteins with predefined structures and affinities, bypassing natural templates. Tools like RFdiffusion generate backbones with targeted pockets for ligand binding, followed by sequence optimization to achieve nanomolar affinities. A 2024 study demonstrated the design of proteins binding small molecules with tunable interaction energies, achieving experimental affinities matching computational predictions within 1 kcal/mol. In engineering contexts, such designs have produced metalloproteins with custom metal-binding sites, facilitating applications in catalysis and sensing. Computational design tools, such as those integrating Rosetta and machine learning, enable rapid iteration of these libraries. Binding sites engineered into biomolecules underpin key biotechnological applications, including biosensors and purification systems. Aptamers, short nucleic acids selected for high-affinity binding sites, serve as recognition elements in biosensors for real-time detection of analytes like toxins or metabolites. For example, thrombin-binding aptamers integrated into electrochemical platforms detect picomolar concentrations through conformational changes upon target binding, enabling portable diagnostics in environmental monitoring. In protein purification, polyhistidine (His) tags—short sequences forming coordination binding sites with nickel ions—facilitate immobilized metal affinity chromatography (IMAC). His-tagged proteins bind reversibly to Ni-NTA resins with dissociation constants around 10-100 μM, allowing one-step isolation from crude lysates with >95% purity in many cases. Synthetic biology extends binding site engineering to and tools. Proteins with designed binding sites have been incorporated into , such as self-assembling cages or scaffolds, to control cargo delivery or catalytic activity. Computational design of bifaceted protein in 2025 yielded structures with dual binding pockets for metals and substrates, enhancing stability and reactivity in aqueous environments. In CRISPR systems, guide RNAs (gRNAs) feature programmable spacer sequences that form RNA-DNA hybrid binding sites, directing nuclease to specific genomic loci for precise editing. Optimized gRNA designs, incorporating secondary structures for improved stability, achieve editing efficiencies exceeding 80% in mammalian cells while minimizing off-target binding. Recent advances in 2024-2025 highlight protein chimeras with hybrid binding sites for production, addressing limitations in natural enzymes. Multidomain chimeras fuse catalytic and substrate-binding modules from different sources, creating hybrid sites that enhance and activity under industrial conditions. For lignocellulose degradation in production, chimeric cellulases with engineered binding domains from fungal and bacterial origins improved yields by 2-3 fold compared to parental enzymes, demonstrating scalability for biorefineries. These designs fill gaps in by combining computational with for robust, high-throughput processing.

References

  1. https://bio.libretexts.org/Bookshelves/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01:_Unit_I-_Structure_and_Catalysis/05:_Protein_Function/5.01:__Binding_-_The_First_Step_Toward_Protein_Function
  2. https://www.hilarispublisher.com/open-access/structural-biochemistry-of-proteinligand-interactions-implications-for-drug-development.pdf
  3. https://www.ncbi.nlm.nih.gov/books/NBK554481/
  4. https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/binding-site
  5. https://pmc.ncbi.nlm.nih.gov/articles/PMC10308531/
  6. https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/lock-key-model
  7. https://onlinelibrary.wiley.com/doi/abs/10.1002/anie.199423751
  8. https://pmc.ncbi.nlm.nih.gov/articles/PMC3381512/
  9. https://www.ncbi.nlm.nih.gov/books/NBK9921/
  10. https://www.ncbi.nlm.nih.gov/books/NBK554403/
  11. https://pmc.ncbi.nlm.nih.gov/articles/PMC3699289/
  12. https://pmc.ncbi.nlm.nih.gov/articles/PMC5793992/
  13. https://pmc.ncbi.nlm.nih.gov/articles/PMC4804091/
  14. https://pmc.ncbi.nlm.nih.gov/articles/PMC4497952/
  15. https://pmc.ncbi.nlm.nih.gov/articles/PMC8026052/
  16. https://www.nature.com/articles/s42003-024-06117-5
  17. https://academic.oup.com/nar/article/51/D1/D403/6761731
  18. https://www.ncbi.nlm.nih.gov/books/NBK595006/
  19. https://www.sciencedirect.com/science/article/pii/S1016847823137836/fulltext
  20. https://www.mdpi.com/1422-0067/22/2/922
  21. https://digitalcommons.unomaha.edu/cgi/viewcontent.cgi?referer=&httpsredir=1&article=1013&context=interdiscipinformaticsfacpub
  22. https://pubs.acs.org/doi/10.1021/acs.accounts.5b00516
  23. https://academic.oup.com/gpb/article/22/1/qzae001/7510854
  24. https://onlinelibrary.wiley.com/doi/abs/10.1002/med.21581
  25. https://www.nature.com/scitable/topicpage/protein-structure-14122136/
  26. https://www.sciencedirect.com/science/article/pii/S0171933521000376
  27. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-S7-S9
  28. https://pmc.ncbi.nlm.nih.gov/articles/PMC4117425/
  29. https://pmc.ncbi.nlm.nih.gov/articles/PMC4978935/
  30. https://www.nature.com/articles/s41467-018-05769-2
  31. https://pmc.ncbi.nlm.nih.gov/articles/PMC4783878/
  32. http://www.bioinf.org.uk/teaching/cpd/papers/Chothia1987.pdf
  33. https://www.nature.com/articles/nsb1096-821
  34. https://www.nature.com/articles/s41467-020-18425-5
  35. https://pmc.ncbi.nlm.nih.gov/articles/PMC3744632/
  36. https://chem.libretexts.org/Courses/CSU_Chico/CSU_Chico%253A_CHEM_451_-_Biochemistry_I/CHEM_451_Test/08%253A_Transport_and_Kinetics/8.4%253A_Enzyme_Inhibition/Uncompetitive_Inhibition
  37. https://pmc.ncbi.nlm.nih.gov/articles/PMC3044288/
  38. https://pubmed.ncbi.nlm.nih.gov/8286385/
  39. https://www.jbc.org/article/S0021-9258(17)47427-8/fulltext
  40. https://academic.oup.com/nar/article/25/19/3760/2549101
  41. https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002452
  42. https://www.nature.com/articles/s41467-021-21328-8
  43. https://doi.org/10.1016/s0022-2836(63)80091-1
  44. https://doi.org/10.1074/jbc.REV123.000048
  45. https://doi.org/10.1016/S0022-2836(65)80285-6
  46. https://doi.org/10.1021/acs.biochem.0c00275
  47. https://doi.org/10.1016/s0022-2836(63)80048-8
  48. https://doi.org/10.1016/j.sbi.2023.03.010
  49. https://www.nature.com/articles/s41467-023-36699-3
  50. https://pmc.ncbi.nlm.nih.gov/articles/PMC7865889/
  51. https://pmc.ncbi.nlm.nih.gov/articles/PMC6657754/
  52. https://doi.org/10.1016/S0092-8674(00)605-X
  53. https://pmc.ncbi.nlm.nih.gov/articles/PMC10610324/
  54. https://academic.oup.com/bioinformaticsadvances/article/5/1/vbaf156/8180504
  55. https://www.nature.com/articles/s41467-024-45461-2
  56. https://pmc.ncbi.nlm.nih.gov/articles/PMC3000649/
  57. https://deepblue.lib.umich.edu/bitstream/handle/2027.42/143761/cpns0705.pdf?sequence=1
  58. https://pmc.ncbi.nlm.nih.gov/articles/PMC7452723/
  59. https://www.ncbi.nlm.nih.gov/books/NBK569501/
  60. https://www.graphpad.com/guides/prism/latest/curve-fitting/reg_association2.htm
  61. https://pmc.ncbi.nlm.nih.gov/articles/PMC8318206/
  62. https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/induced-fit-model
  63. https://www.sciencedirect.com/science/article/abs/pii/S0076687918303896
  64. https://pubmed.ncbi.nlm.nih.gov/11693526/
  65. https://pmc.ncbi.nlm.nih.gov/articles/PMC2546551/
  66. https://www.nature.com/articles/nbt0596-595
  67. https://pubmed.ncbi.nlm.nih.gov/14505463/
  68. https://www.sciencedirect.com/science/article/abs/pii/S1471489204001225
  69. https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2023.1113762/full
  70. https://journals.sagepub.com/doi/10.1177/1934578X19849296
  71. https://www.nature.com/articles/s42004-025-01542-x
  72. https://pmc.ncbi.nlm.nih.gov/articles/PMC3635485/
  73. https://pubmed.ncbi.nlm.nih.gov/23729250/
  74. https://www.sciencedirect.com/science/article/abs/pii/S0959440X00002487
  75. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0203224
  76. https://pubmed.ncbi.nlm.nih.gov/34432236/
  77. https://www.sciencedirect.com/science/article/pii/S0005273613001466
  78. https://pubmed.ncbi.nlm.nih.gov/30317506/
  79. https://pmc.ncbi.nlm.nih.gov/articles/PMC7854220/
  80. https://pubs.acs.org/doi/10.1021/acsomega.3c06006
  81. https://pubmed.ncbi.nlm.nih.gov/33654778/
  82. https://www.pnas.org/doi/10.1073/pnas.88.20.9188
  83. https://pubmed.ncbi.nlm.nih.gov/12907737/
  84. https://www.nature.com/articles/s41598-017-07010-4
  85. https://www.nature.com/articles/s41592-023-01853-8
  86. https://www.pnas.org/doi/10.1073/pnas.2115001118
  87. https://www.sciencedirect.com/science/article/pii/S0959440X23000957
  88. https://www.nature.com/articles/nprot.2016.051
  89. https://autodock.scripps.edu/
  90. https://www.sciencedirect.com/science/article/abs/pii/S1471489210001463
  91. https://pubs.acs.org/doi/10.1021/acs.jctc.3c00020
  92. https://pmc.ncbi.nlm.nih.gov/articles/PMC1538779/
  93. https://www.nature.com/articles/s41586-024-07487-w
  94. https://pubs.acs.org/doi/10.1021/acs.jcim.1c00851
  95. https://pubmed.ncbi.nlm.nih.gov/35633442/
  96. https://www.nature.com/articles/s42004-023-01019-9
  97. https://pubmed.ncbi.nlm.nih.gov/31396905/
  98. https://www.sciencedirect.com/science/article/pii/S0959440X25000387
  99. https://github.com/baker-laboratory/RoseTTAFold-All-Atom
  100. https://pubmed.ncbi.nlm.nih.gov/17508936/
  101. https://www.pnas.org/doi/10.1073/pnas.0809400106
  102. https://pubs.acs.org/doi/10.1021/jm2010332
  103. https://www.sciencedirect.com/science/article/pii/S0959440X25001769
  104. https://www.nature.com/articles/s41392-023-01589-z
  105. https://jhoonline.biomedcentral.com/articles/10.1186/s13045-018-0624-2
  106. https://www.sciencedirect.com/science/article/pii/S200103702500217X
  107. https://www.nature.com/articles/s41392-025-02176-0
  108. https://pmc.ncbi.nlm.nih.gov/articles/PMC6332083/
  109. http://csmres.co.uk/cs.public.upd/article-downloads/moroy_2012_drug-discovery-today.pdf
  110. https://www.nobelprize.org/uploads/2018/10/advanced-chemistryprize-2018.pdf
  111. https://pubs.acs.org/doi/10.1021/acscatal.3c02746
  112. https://www.science.org/doi/10.1126/science.adl5364
  113. https://www.nature.com/articles/s41586-023-06415-8
  114. https://www.science.org/doi/10.1126/sciadv.ads9687
  115. https://pubmed.ncbi.nlm.nih.gov/26096499/
  116. https://www.nature.com/articles/s41563-025-02295-7
  117. https://pmc.ncbi.nlm.nih.gov/articles/PMC5014588/
  118. https://pmc.ncbi.nlm.nih.gov/articles/PMC10927867/
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