UV difference spectroscopy of ligand binding to maltose-binding protein.

We have used UV absorbance spectroscopy to study the binding of linear and circular maltodextrins to maltose-binding protein (MBP). Titrations with maltose yield three isosbestic points in the difference spectrum of MBP, consistent with two protein conformations: ligand-free and ligand-bound. In contrast, titrations with maltotetraose reveal three conformations: ligand-free, a low-affinity liganded state, and a high affinity liganded state. These results confirm and extend the results from tritium NMR spectroscopy, namely, that MBP can bind maltodextrin either by the sugar's anomeric end (high affinity) or by the middle of the maltodextrin chain (low affinity).     

Energetic contributions of amino acid residues and its cross-talk to delineate ligand-binding mechanism

Receptor-based QSAR approaches can enumerate the energetic contributions of amino acid residues toward ligand binding only when experimental binding affinity is associated. The structural data of protein-ligand complexes are witnessing a tremendous growth in the Protein Data Bank deposited with a few entries on binding affinity. We present here a new approach to compute the E nergetic CONT ributions of A mino acid residues and its possible C ross-T alk (ECONTACT) to study ligand binding using per-residue energy decomposition, molecular dynamics simulations and rescoring method without the need for experimental binding affinity. This approach recognizes potential cross-talks among amino acid residues imparting a nonadditive effect to the binding affinity with evidence of correlative motions in the dynamics simulations. The protein-ligand interaction energies deduced from multiple structures are decomposed into per-residue energy terms, which are employed as variables to principal component analysis and generated cross-terms. Out of 16 cross-talks derived from eight datasets of protein-ligand systems, the ECONTACT approach is able to associate 10 potential cross-talks with site-directed mutagenesis, free energy, and dynamics simulations data strongly. We modeled these key determinants of ligand binding using joint probability density function (jPDF) to identify cross-talks in protein structures. The top two cross-talks identified by ECONTACT approach corroborated with the experimental findings. Furthermore, virtual screening exercise using ECONTACT models better discriminated known inhibitors from decoy molecules. This approach proposes the jPDF metric to estimate the probability of observing cross-talks in any protein-ligand complex. The source code and related resources to perform ECONTACT modeling is available freely at https://www.gujaratuniversity.ac.in/econtact /.     

Protein Conformational Changes Are Detected and Resolved Site Specifically by Second-Harmonic Generation

We present here a straightforward, broadly applicable technique for real-time detection and measurement of protein conformational changes in solution. This method is based on tethering proteins labeled with a second-harmonic generation (SHG) active dye to supported lipid bilayers. We demonstrate our method by measuring the conformational changes that occur upon ligand binding with three well-characterized proteins labeled at lysine residues: calmodulin (CaM), maltose-binding protein (MBP), and dihydrofolate reductase (DHFR). We also create a single-site cysteine mutant of DHFR engineered within the Met20 catalytic loop region and study the protein’s structural motion at this site. Using published x-ray crystal structures, we show that the changes in the SHG signals upon ligand binding are the result of structural motions that occur at the labeled sites between the apo and ligand-bound forms of the proteins, which are easily distinguished from each other. In addition, we demonstrate that different magnitudes of the SHG signal changes are due to different and specific ligand-induced conformational changes. Taken together, these data illustrate the potential of the SHG approach for detecting and measuring protein conformational changes for a wide range of biological applications.     

Ligand binding to a high‐energy partially unfolded protein

The conformational energy landscape of a protein determines populations of all possible conformations of the protein and also determines the kinetics of the conversion between the conformations. Interaction with ligands influences the conformational energy landscapes of proteins and shifts populations of proteins in different conformational states. To investigate the effect of ligand binding on partial unfolding of a protein, we use Escherichia coli dihydrofolate reductase (DHFR) and its functional ligand NADP⁺ as a model system. We previously identified a partially unfolded form of DHFR that is populated under native conditions. In this report, we determined the free energy for partial unfolding of DHFR at varying concentrations of NADP⁺ and found that NADP⁺ binds to the partially unfolded form as well as the native form. DHFR unfolds partially without releasing the ligand, though the binding affinity for NADP⁺ is diminished upon partial unfolding. Based on known crystallographic structures of NADP⁺‐bound DHFR and the model of the partially unfolded protein we previously determined, we propose that the adenosine‐binding domain of DHFR remains folded in the partially unfolded form and interacts with the adenosine moiety of NADP⁺. Our result demonstrates that ligand binding may affect the conformational free energy of not only native forms but also high‐energy non‐native forms.     

Dynamical persistence of active sites identified in maltose-binding protein

This study identifies dynamical properties of maltose-binding protein (MBP) useful in unveiling active site residues susceptible to ligand binding. The described methodology has been previously used in support of novel topological techniques of persistent homology and statistical inference in complex, multi-scale, high-dimensional data often encountered in computational biophysics. Here we outline a computational protocol that is based on the anisotropic elastic network models of 14 all-atom three-dimensional protein structures. We introduce the notion of dynamical distance matrices as a measure of correlated interactions among 370 amino acid residues that constitute a single protein. The dynamical distance matrices serve as an input for a persistent homology suite of codes to further distinguish a small subset of residues with high affinity for ligand binding and allosteric activity. In addition, we show that ligand-free closed MBP structures require lower deformation energies than open MBP structures, which may be used in categorization of time-evolving molecular dynamics structures. Analysis of the most probable allosteric coupling pathways between active site residues and the protein exterior is also presented.     

Incorporating replacement free energy of binding‐site waters in molecular docking

Binding‐site water molecules play a crucial role in protein‐ligand recognition, either being displaced upon ligand binding or forming water bridges to stabilize the complex. However, rigorously treating explicit binding‐site waters is challenging in molecular docking, which requires to fully sample ensembles of waters and to consider the free energy cost of replacing waters. Here, we describe a method to incorporate structural and energetic properties of binding‐site waters into molecular docking. We first developed a solvent property analysis (SPA) program to compute the replacement free energies of binding‐site water molecules by post‐processing molecular dynamics trajectories obtained from ligand‐free protein structure simulation in explicit water. Next, we implemented a distance‐dependent scoring term into DOCK scoring function to take account of the water replacement free energy cost upon ligand binding. We assessed this approach in protein targets containing important binding‐site waters, and we demonstrated that our approach is reliable in reproducing the crystal binding geometries of protein‐ligand‐water complexes, as well as moderately improving the ligand docking enrichment performance. In addition, SPA program (free available to academic users upon request) may be applied in identifying hot‐spot binding‐site residues and structure‐based lead optimization. Proteins 2014; 82:1765–1776. © 2014 Wiley Periodicals, Inc.     

Insights into the conformational equilibria of maltose-binding protein by analysis of high affinity mutants

The affinity of maltose-binding protein (MBP) for maltose and related carbohydrates was greatly increased by removal of groups in the interface opposite the ligand binding cleft. The wild-type protein has a KD of 1200 nM for maltose; mutation of residues Met-321 and Gln-325, both to alanine, resulted in a KD for maltose of 70 nM; deletion of 4 residues, Glu-172, Asn-173, Lys-175, and Tyr-176, which are part of a poorly ordered loop, results in a KD for maltose of 110 nM. Combining the mutations yields an increased affinity for maltodextrins and a KD of 6 nM for maltotriose. Comparison of ligand binding by the mutants, using surface plasmon resonance spectroscopy, indicates that decreases in the off-rate are responsible for the increased affinity. Small-angle x-ray scattering was used to demonstrate that the mutations do not significantly affect the solution conformation of MBP in either the presence or absence of maltose. The crystal structures of selected mutants showed that the mutations do not cause significant structural changes in either the closed or open conformation of MBP. These studies show that interactions in the interface opposite the ligand binding cleft, which we term the "balancing interface," are responsible for modulating the affinity of MBP for its ligand. Our results are consistent with a model in which the ligand-bound protein alternates between the closed and open conformations, and removal of interactions in the balancing interface decreases the stability of the open conformation, without affecting the closed conformation.     

Evidence of Conformational Selection Driving the Formation of Ligand Binding Sites in Protein-Protein Interfaces

Many protein-protein interactions (PPIs) are compelling targets for drug discovery, and in a number of cases can be disrupted by small molecules. The main goal of this study is to examine the mechanism of binding site formation in the interface region of proteins that are PPI targets by comparing ligand-free and ligand-bound structures. To avoid any potential bias, we focus on ensembles of ligand-free protein conformations obtained by nuclear magnetic resonance (NMR) techniques and deposited in the Protein Data Bank, rather than on ensembles specifically generated for this study. The measures used for structure comparison are based on detecting binding hot spots, i.e., protein regions that are major contributors to the binding free energy. The main tool of the analysis is computational solvent mapping, which explores the surface of proteins by docking a large number of small “probe” molecules. Although we consider conformational ensembles obtained by NMR techniques, the analysis is independent of the method used for generating the structures. Finding the energetically most important regions, mapping can identify binding site residues using ligand-free models based on NMR data. In addition, the method selects conformations that are similar to some peptide-bound or ligand-bound structure in terms of the properties of the binding site. This agrees with the conformational selection model of molecular recognition, which assumes such pre-existing conformations. The analysis also shows the maximum level of similarity between unbound and bound states that is achieved without any influence from a ligand. Further shift toward the bound structure assumes protein-peptide or protein-ligand interactions, either selecting higher energy conformations that are not part of the NMR ensemble, or leading to induced fit. Thus, forming the sites in protein-protein interfaces that bind peptides and can be targeted by small ligands always includes conformational selection, although other recognition mechanisms may also be involved.     

Role of ligand binding in structural organization of add A-riboswitch aptamer: a molecular dynamics simulation

The specific binding of ligands is the first step of gene expression or translation regulation by riboswitches. However, understanding the mechanism of the specific binding is still difficult because the tertiary structures of the riboswitch aptamers are available almost only for ligand-bound state at present. In this paper we hope to give some insights into this problem through the studies of the role of ligand-aptamer interaction in the structural organization of add A-riboswitch aptamer, based on the crystal structure of the ligand-bound aptamer. We use all-atom molecular dynamics to simulate the behaviors of the aptamer in ligand-bound, free and mutated states by Amber force field. The results show that the correct paring of the ligand adenine with the nucleotide U74 in the binding pocket is crucial to stabilizing the conformations of the ligand-bound aptamer, especially the helix P1 connecting the expression platform. Our results also suggest that both the nucleotide U74 and U51 may be the key sites of the ligand recognition but the former has much higher probability as the initial docking site. This is in agreement with previous experimental results.     

Crystal Structure of the Tetrameric Fibrinogen-like Recognition Domain of Fibrinogen C Domain Containing 1 (FIBCD1) Protein

The high resolution crystal structures of a recombinant fragment of the C-terminal fibrinogen-like recognition domain of FIBCD1, a vertebrate receptor that binds chitin, have been determined. The overall tetrameric structure shows similarity in structure and aggregation to the horseshoe crab innate immune protein tachylectin 5A. The high affinity ligand N-acetylmannosamine (ManNAc) binds in the S1 site, predominantly via the acetyl group with the oxygen and acetamide nitrogen hydrogen-bonded to the protein and the methyl group inserted into a hydrophobic pocket. The binding of the ManNAc pyranose ring differs markedly between the two independent subunits, but in all structures the binding of the N-acetyl group is conserved. In the native structure, a crystal contact results in one of the independent protomers binding the first GlcNAc of the Asn³⁴⁰ N-linked glycan on the other independent protomer. In the ligand-bound structure this GlcNAc is replaced by the higher affinity ligand ManNAc. In addition, a sulfate ion has been modeled into the electron density at a location similar to the S3 binding site in L-ficolin, whereas in the native structure an acetate ion has been placed in the S1 N-acetyl binding site, and a sulfate ion has been placed adjacent to this site. These ion binding sites are ideally placed to receive the N-acetyl and sulfate groups of sulfated GalNAc residues of glycosaminoglycans such as chondroitin and dermatan sulfate. Together, these structures give insight into important determinants of ligand selectivity, demonstrating versatility in recognition and binding while maintaining conservation in N-acetyl and calcium binding.     

Conformational dynamics and thermodynamics of protein-ligand binding studied by NMR relaxation

Protein conformational dynamics can be critical for ligand binding in two ways that relate to kinetics and thermodynamics respectively. First, conformational transitions between different substates can control access to the binding site (kinetics). Secondly, differences between free and ligand-bound states in their conformational fluctuations contribute to the entropy of ligand binding (thermodynamics). In the present paper, I focus on the second topic, summarizing our recent results on the role of conformational entropy in ligand binding to Gal3C (the carbohydrate-recognition domain of galectin-3). NMR relaxation experiments provide a unique probe of conformational entropy by characterizing bond-vector fluctuations at atomic resolution. By monitoring differences between the free and ligand-bound states in their backbone and side chain order parameters, we have estimated the contributions from conformational entropy to the free energy of binding. Overall, the conformational entropy of Gal3C increases upon ligand binding, thereby contributing favourably to the binding affinity. Comparisons with the results from isothermal titration calorimetry indicate that the conformational entropy is comparable in magnitude to the enthalpy of binding. Furthermore, there are significant differences in the dynamic response to binding of different ligands, despite the fact that the protein structure is virtually identical in the different protein-ligand complexes. Thus both affinity and specificity of ligand binding to Gal3C appear to depend in part on subtle differences in the conformational fluctuations that reflect the complex interplay between structure, dynamics and ligand interactions.     

Manipulation of ligand binding affinity by exploitation of conformational coupling

Traditional approaches for increasing the affinity of a protein for its ligand focus on constructing improved surface complementarity in the complex by altering the protein binding site to better fit the ligand. Here we present a novel strategy that leaves the binding site intact, while residues that allosterically affect binding are mutated. This method takes advantage of conformationally distinct states, each with different ligand-binding affinities, and manipulates the equilibria between these conformations. We demonstrate this approach in the Escherichia coli maltose binding protein by introducing mutations, located at some distance from the ligand binding pocket, that sterically affect the equilibrium between an open, apo-state and a closed, ligand-bound state. A family of 20 variants was generated with affinities ranging from an approximately 100-fold improvement (7.4 nM) to an approximately two-fold weakening (1.8 mM) relative to the wild type protein (800 nM).     

The effect of multiple binding modes on empirical modeling of ligand docking to proteins.

A popular approach to the computational modeling of ligand/receptor interactions is to use an empirical free energy like model with adjustable parameters. Parameters are learned from one set of complexes, then used to predict another set. To improve these empirical methods requires an independent way to study their inherent errors. We introduce a toy model of ligand/receptor binding as a workbench for testing such errors. We study the errors incurred from the two state binding assumption--the assumption that a ligand is either bound in one orientation, or unbound. We find that the two state assumption can cause large errors in free energy predictions, but it does not affect rank order predictions significantly. We show that fitting parameters using data from high affinity ligands can reduce two state errors; so can using more physical models that do not use the two state assumption. We also find that when using two state models to predict free energies, errors are more severe on high affinity ligands than low affinity ligands. And we show that two state errors can be diagnosed by systematically adding new binding modes when predicting free energies: if predictions worsen as the modes are added, then the two state assumption in the fitting step may be at fault.     

A QM protein–ligand investigation of antipsychotic drugs with the dopamine D2 Receptor (D2R)

The dopamine D2 Receptor (D2R) is a member of the G-Protein-Coupled Receptor family and plays a critical role in neurotransmission activities in the human brain. Dysfunction in dopamine receptor signaling may lead to mental health illnesses such as schizophrenia and Parkinson’s disease. D2R is the target protein of the commonly used antipsychotic drugs such as risperidone, clozapine, aripiprazole, olanzapine, ziprasidone, and quetiapine. Due to their significant side effects and non-selective profiles, the discovery of novel drugs has become a challenge for researchers working in this field. Recently, our group has focused on the interactions of these drug molecules in the active site of the D2R using different in silico approaches. We here compare the performances of different approaches in estimating the drug binding affinities using quantum chemical approaches. Conformations of drug molecules (ligands) at the binding site of the D2R taken from the preliminary docking studies and molecular dynamics simulations were used to generate protein–ligand interaction models. In a first approach, the BSSE-corrected interaction energies of the ligands with the most critical amino acid Asp114 and with the other amino acids closest to ligands in the binding cavity were calculated separately by density functional theory method in implicit water environment at the M06-2X/6-31 g(d,p) level of the theory. In a second approach, ligand binding affinities were calculated by taking into consideration not only the interaction energies but also deformation and desolvation energies of ligands with surrounding amino acid residues, in a radius of 5 Å of the protein-bound ligand. The quantum mechanically obtained results were compared with the experimentally obtained binding affinity values. We concluded that although H-bond interactions of ligands with Asp114 are the most dominant interaction in the binding site, if van der Waals and steric interactions of ligands which have cumulative effect on the ligand binding are not included in the calculations, the interaction energies are overestimated.     

Protein flexibility in ligand docking and virtual screening to protein kinases

The main complicating factor in structure-based drug design is receptor rearrangement upon ligand binding (induced fit). It is the induced fit that complicates cross-docking of ligands from different ligand-receptor complexes. Previous studies have shown the necessity to include protein flexibility in ligand docking and virtual screening. Very few docking methods have been developed to predict the induced fit reliably and, at the same time, to improve on discriminating between binders and non-binders in the virtual screening process.We present an algorithm called the ICM-flexible receptor docking algorithm (IFREDA) to account for protein flexibility in virtual screening. By docking flexible ligands to a flexible receptor, IFREDA generates a discrete set of receptor conformations, which are then used to perform flexible ligand-rigid receptor docking and scoring. This is followed by a merging and shrinking step, where the results of the multiple virtual screenings are condensed to improve the enrichment factor. In the IFREDA approach, both side-chain rearrangements and essential backbone movements are taken into consideration, thus sampling adequately the conformational space of the receptor, even in cases of large loop movements.As a preliminary step, to show the importance of incorporating protein flexibility in ligand docking and virtual screening, and to validate the merging and shrinking procedure, we compiled an extensive small-scale virtual screening benchmark of 33 crystal structures of four different protein kinases sub-families (cAPK, CDK-2, P38 and LCK), where we obtained an enrichment factor fold-increase of 1.85±0.65 using two or three multiple experimental conformations. IFREDA was used in eight protein kinase complexes and was able to find the correct ligand conformation and discriminate the correct conformations from the “misdocked” conformations solely on the basis of energy calculation. Five of the generated structures were used in the small-scale virtual screening stage and, by merging and shrinking the results with those of the original structure, we show an enrichment factor fold increase of 1.89±0.60, comparable to that obtained using multiple experimental conformations.Our cross-docking tests on the protein kinase benchmark underscore the necessity of incorporating protein flexibility in both ligand docking and virtual screening. The methodology presented here will be extremely useful in cases where few or no experimental structures of complexes are available, while some binders are known.     

How maltose influences structural changes to bind to maltose‐binding protein: Results from umbrella sampling simulation

A well‐studied periplasmic‐binding protein involved in the abstraction of maltose is maltose‐binding protein (MBP), which undergoes a ligand‐induced conformational transition from an open (ligand‐free) to a closed (ligand‐bound) state. Umbrella sampling simulations have been us to estimate the free energy of binding of maltose to MBP and to trace the potential of mean force of the unbinding event using the center‐of‐mass distance between the protein and ligand as the reaction coordinate. The free energy thus obtained compares nicely with the experimentally measured value justifying our theoretical basis. Measurement of the domain angle (N‐terminal‐domain – hinge – C‐terminal‐domain) along the unbinding pathway established the existence of three different states. Starting from a closed state, the protein shifts to an open conformation during the initial unbinding event of the ligand then resides in a semi‐open conformation and later resides predominantly in an open‐state. These transitions along the ligand unbinding pathway have been captured in greater depth using principal component analysis. It is proposed that in mixed‐model, both conformational selection and an induced‐fit mechanism combine to the ligand recognition process in MBP. Proteins 2013. © 2012 Wiley Periodicals, Inc.     

The interplay between effector binding and allostery in an engineered protein switch

The protein design rules for engineering allosteric regulation are not well understood. A fundamental understanding of the determinants of ligand binding in an allosteric context could facilitate the design and construction of versatile protein switches and biosensors. Here, we conducted extensive in vitro and in vivo characterization of the effects of 285 unique point mutations at 15 residues in the maltose‐binding pocket of the maltose‐activated β‐lactamase MBP317‐347. MBP317‐347 is an allosteric enzyme formed by the insertion of TEM‐1 β‐lactamase into the E. coli maltose binding protein (MBP). We find that the maltose‐dependent resistance to ampicillin conferred to the cells by the MBP317‐347 switch gene (the switch phenotype) is very robust to mutations, with most mutations slightly improving the switch phenotype. We identified 15 mutations that improved switch performance from twofold to 22‐fold, primarily by decreasing the catalytic activity in the absence of maltose, perhaps by disrupting interactions that cause a small fraction of MBP in solution to exist in a partially closed state in the absence of maltose. Other notable mutations include K15D and K15H that increased maltose affinity 30‐fold and Y155K and Y155R that compromised switching by diminishing the ability of maltose to increase catalytic activity. The data also provided insights into normal MBP physiology, as select mutations at D14, W62, and F156 retained high maltose affinity but abolished the switch's ability to substitute for MBP in the transport of maltose into the cell. The results reveal the complex relationship between ligand binding and allostery in this engineered switch.     

Understanding microscopic binding of macrophage migration inhibitory factor with phenolic hydrazones by molecular docking, molecular dynamics simulations and free energy calculations

Macrophage migration inhibitory factor (MIF), an immunoregulatory protein, is a potential target for a number of inflammatory diseases. In the current work, the interactions between MIF and a series of phenolic hydrazones were studied by molecular docking, molecular dynamics (MD) simulations, binding free energy calculations, and binding energy decomposition analysis to determine the structural requirement for achieving favorable biological activity of phenolic hydrazones. First, molecular docking was used to predict the binding modes of inhibitors in the binding site of MIF. The good correlation between the predicted docking scores and the experimental activities shows that the binding conformations of the inhibitors in the active site of MIF are well predicted. Moreover, our results suggest that the flexibility of MIF is essential in ligand binding process. Then, MD simulations and MM/GBSA free energy calculations were employed to determine the dynamic binding process and compare the binding modes of the inhibitors with different activities. The predicted binding free energies given by MM/GBSA are not well correlated with the experimental activities for the two subsets of the inhibitors; however, for each subset, a good correlation between the predicted binding free energies and the experimental activities is achieved. The MM/GBSA free energy decomposition analysis highlights the importance of hydrophobic residues for the MIF binding of the studied inhibitors. Based on the essential factors for MIF-inhibitor interactions derived from the theoretical predictions, some derivatives were designed and the higher inhibitory activities of several candidates were confirmed by molecular docking studies. The structural insights obtained from our study are useful for designing potent inhibitors of MIF.     

Ligand entry into the calyx of β‐lactoglobulin

Although the thermodynamic principles that control the binding of drug molecules to their protein targets are well understood, the detailed process of how a ligand reaches a protein binding site has been an intriguing question over decades. The short time interval between the encounter between a ligand and its receptor to the formation of the stable complex has prevented experimental observations. Bovine β‐lactoglobulin (βlg) is a lipocalin member that carries fatty acids (FAs) and other lipids in the cellular environment. Βlg accommodates a FA molecule in its highly hydrophobic cavity and exhibits the capability of recognizing a wide variety of hydrophobic ligands. To elucidate the ligand entry process on βlg, we report molecular dynamics simulations of the encounter between palmitate (PA) or laurate (LA) and βlg. Our results show that residues localized in loops at the cavity entrance play an important role in the ligand penetration process. Analysis of the short‐term interaction energies show that the forces operating on the systems lead to average conformations very close to the crystallographic holo‐forms. Whereas the binding free energy analysis using the molecular mechanics Generalized Born surface area method shows that these conformations were thermodynamically favorable. © 2013 Wiley Periodicals, Inc. Biopolymers 101: 744–757, 2014.     

Identification of substrate binding sites in enzymes by computational solvent mapping

Enzyme structures determined in organic solvents show that most organic molecules cluster in the active site, delineating the binding pocket. We have developed algorithms to perform solvent mapping computationally, rather than experimentally, by placing molecular probes (small molecules or functional groups) on a protein surface, and finding the regions with the most favorable binding free energy. The method then finds the consensus site that binds the highest number of different probes. The probe-protein interactions at this site are compared to the intermolecular interactions seen in the known complexes of the enzyme with various ligands (substrate analogs, products, and inhibitors). We have mapped thermolysin, for which experimental mapping results are also available, and six further enzymes that have no experimental mapping data, but whose binding sites are well characterized. With the exception of haloalkane dehalogenase, which binds very small substrates in a narrow channel, the consensus site found by the mapping is always a major subsite of the substrate-binding site. Furthermore, the probes at this location form hydrogen bonds and non-bonded interactions with the same residues that interact with the specific ligands of the enzyme. Thus, once the structure of an enzyme is known, computational solvent mapping can provide detailed and reliable information on its substrate-binding site. Calculations on ligand-bound and apo structures of enzymes show that the mapping results are not very sensitive to moderate variations in the protein coordinates.