VASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand Binding Specificity

Many algorithms that compare protein structures can reveal similarities that suggest related biological functions, even at great evolutionary distances. Proteins with related function often exhibit differences in binding specificity, but few algorithms identify structural variations that effect specificity. To address this problem, we describe the Volumetric Analysis of Surface Properties (VASP), a novel volumetric analysis tool for the comparison of binding sites in aligned protein structures. VASP uses solid volumes to represent protein shape and the shape of surface cavities, clefts and tunnels that are defined with other methods. Our approach, inspired by techniques from constructive solid geometry, enables the isolation of volumetrically conserved and variable regions within three dimensionally superposed volumes. We applied VASP to compute a comparative volumetric analysis of the ligand binding sites formed by members of the steroidogenic acute regulatory protein (StAR)-related lipid transfer (START) domains and the serine proteases. Within both families, VASP isolated individual amino acids that create structural differences between ligand binding cavities that are known to influence differences in binding specificity. Also, VASP isolated cavity subregions that differ between ligand binding cavities which are essential for differences in binding specificity. As such, VASP should prove a valuable tool in the study of protein-ligand binding specificity.     

A regionalizable statistical model of intersecting regions in protein-ligand binding cavities

Finding elements of proteins that influence ligand binding specificity is an essential aspect of research in many fields. To assist in this effort, this paper presents two statistical models, based on the same theoretical foundation, for evaluating structural similarity among binding cavities. The first model specializes in the "unified" comparison of whole cavities, enabling the selection of cavities that are too dissimilar to have similar binding specificity. The second model enables a "regionalized" comparison of cavities within a user-defined region, enabling the selection of cavities that are too dissimilar to bind the same molecular fragments in the given region. We applied these models to analyze the ligand binding cavities of the serine protease and enolase superfamilies. Next, we observed that our unified model correctly separated sets of cavities with identical binding preferences from other sets with varying binding preferences, and that our regionalized model correctly distinguished cavity regions that are too dissimilar to bind similar molecular fragments in the user-defined region. These observations point to applications of statistical modeling that can be used to examine and, more importantly, identify influential structural similarities within binding site structure in order to better detect influences on protein-ligand binding specificity.     

VASP-E: Specificity Annotation with a Volumetric Analysis of Electrostatic Isopotentials

Algorithms for comparing protein structure are frequently used for function annotation. By searching for subtle similarities among very different proteins, these algorithms can identify remote homologs with similar biological functions. In contrast, few comparison algorithms focus on specificity annotation, where the identification of subtle differences among very similar proteins can assist in finding small structural variations that create differences in binding specificity. Few specificity annotation methods consider electrostatic fields, which play a critical role in molecular recognition. To fill this gap, this paper describes VASP-E (Volumetric Analysis of Surface Properties with Electrostatics), a novel volumetric comparison tool based on the electrostatic comparison of protein-ligand and protein-protein binding sites. VASP-E exploits the central observation that three dimensional solids can be used to fully represent and compare both electrostatic isopotentials and molecular surfaces. With this integrated representation, VASP-E is able to dissect the electrostatic environments of protein-ligand and protein-protein binding interfaces, identifying individual amino acids that have an electrostatic influence on binding specificity. VASP-E was used to examine a nonredundant subset of the serine and cysteine proteases as well as the barnase-barstar and Rap1a-raf complexes. Based on amino acids established by various experimental studies to have an electrostatic influence on binding specificity, VASP-E identified electrostatically influential amino acids with 100% precision and 83.3% recall. We also show that VASP-E can accurately classify closely related ligand binding cavities into groups with different binding preferences. These results suggest that VASP-E should prove a useful tool for the characterization of specific binding and the engineering of binding preferences in proteins.     

Thermodynamics of water mediating protein-ligand interactions in cytochrome P450cam: a molecular dynamics study.

Ordered water molecules are observed by crystallography and nuclear magnetic resonance to mediate protein-ligand interactions. Here, we examine the energetics of hydrating cavities formed at protein-ligand interfaces using molecular dynamics simulations. The free energies of hydrating two cavities in the active site of two liganded complexes of cytochrome P450cam were calculated by multiconfigurational thermodynamic integration. The complex of cytochrome P450cam with 2-phenyl-imidazole contains a crystallographically well defined water molecule mediating hydrogen bonds between the protein and the inhibitor. We calculate that this water molecule is stabilized by a binding free energy of -11.6 +/- kJ/mol. The complex of cytochrome P450cam with its natural substrate, camphor, contains a cavity that is empty in the crystal structure although a water molecule in it could make a hydrogen bond to camphor. Here, solvation of this cavity is calculated to be unfavorable by +15.8 +/- 5.0 kJ/mol. The molecular dynamics simulations can thus distinguish a hydrated interfacial cavity from an empty one. They also provide support for the notion that protein-ligand complexes can accommodate empty interfacial cavities and that such cavities are likely to be unhydrated unless more than one hydrogen bond can be made to a water molecule in the cavity.     

Insights into human Lck SH3 domain binding specificity: different binding modes of artificial and native ligands

We analyzed the ligand binding specificity of the lymphocyte specific kinase (Lck) SH3 domain. We identified artificial Lck SH3 ligands using phage display. In addition, we analyzed Lck SH3 binding sites within known natural Lck SH3 binding proteins using an Lck specific binding assay on membrane-immobilized synthetic peptides. On one hand, from the phage-selected peptides, representing mostly special class I' ligands, a well-defined consensus sequence was obtained. Interestingly, a histidine outside the central polyproline motif contributes significantly to Lck SH3 binding affinity and specificity. On the other hand, we confirmed previously mapped Lck SH3 binding sites in ADAM15, HS1, SLP76, and NS5A, and identified putative Lck SH3 binding sites of Sam68, FasL, c-Cbl, and Cbl-b. Without exception, the comparatively diverse Lck SH3 binding sites of all analyzed natural Lck SH3 binding proteins emerged as class II proteins. Possible explanations for the observed variations between artificial and native ligands-which are not due to significant K(D) value differences as shown by calculating Lck SH3 affinities of artificial peptide PD1-Y(-3)R as well as for peptides comprising putative Lck SH3 binding sites of NS5A, Sos, and Sam68-are discussed. Our data suggest that phage display, a popular tool for determining SH3 binding specificity, must-at least in the case of Lck-not irrevocably mirror physiologically relevant protein-ligand interactions.     

The roles of contact residue disorder and domain composition in characterizing protein-ligand binding specificity and promiscuity

Most protein chains interact with only one ligand but a small number of protein chains can bind several ligands, and many examples are available in the protein-ligand complex database of PDB. Among these proteins, some show preferences for the ligands or types of ligands they bind; however, so far we have only poor understanding of what determines protein-ligand binding and its specificity. Here we investigate the structural and functional properties of proteins in protein-ligand complexes. Analysis of the protein-ligand complex dataset from the PDB structure database reveals that proteins with more interactions have more disordered contact residues. Those proteins containing few disordered contact residues that bind multiple ligands have a tendency to consist of several domains. Analysis of physicochemical properties of hub contact residues binding multiple ligands indicates that they are enriched for hydrophilic, charged, polar and His-Asp catalytic triad residues. Finally, in order to differentiate proteins binding different classes of ligands, we mapped the three most prominent classes of ligands onto different superfamily domains. Our results demonstrate that contact residue disorder and ordered multiple domains are complementary factors that play a crucial role in determining ligand binding specificity and promiscuity.     

Molecular mechanisms and design principles for promiscuous inhibitors to avoid drug resistance: Lessons learned from HIV‐1 protease inhibition

Molecular recognition is central to biology and ranges from highly selective to broadly promiscuous. The ability to modulate specificity at will is particularly important for drug development, and discovery of mechanisms contributing to binding specificity is crucial for our basic understanding of biology and for applications in health care. In this study, we used computational molecular design to create a large dataset of diverse small molecules with a range of binding specificities. We then performed structural, energetic, and statistical analysis on the dataset to study molecular mechanisms of achieving specificity goals. The work was done in the context of HIV‐1 protease inhibition and the molecular designs targeted a panel of wild‐type and drug‐resistant mutant HIV‐1 protease structures. The analysis focused on mechanisms for promiscuous binding to bind robustly even to resistance mutants. Broadly binding inhibitors tended to be smaller in size, more flexible in chemical structure, and more hydrophobic in nature compared to highly selective ones. Furthermore, structural and energetic analyses illustrated mechanisms by which flexible inhibitors achieved binding; we found ligand conformational adaptation near mutation sites and structural plasticity in targets through torsional flips of asymmetric functional groups to form alternative, compensatory packing interactions or hydrogen bonds. As no inhibitor bound to all variants, we designed small cocktails of inhibitors to do so and discovered that they often jointly covered the target set through mechanistic complementarity. Furthermore, using structural plasticity observed in experiments, and potentially in simulations, is suggested to be a viable means of designing adaptive inhibitors that are promiscuous binders. Proteins 2015; 83:351–372. © 2014 Wiley Periodicals, Inc.     

A Structural Portrait of the PDZ Domain Family

PDZ (PSD-95/Discs-large/ZO1) domains are interaction modules that typically bind to specific C-terminal sequences of partner proteins and assemble signaling complexes in multicellular organisms. We have analyzed the existing database of PDZ domain structures in the context of a specificity tree based on binding specificities defined by peptide-phage binding selections. We have identified 16 structures of PDZ domains in complex with high-affinity ligands and have elucidated four additional structures to assemble a structural database that covers most of the branches of the PDZ specificity tree. A detailed comparison of the structures reveals features that are responsible for the diverse specificities across the PDZ domain family. Specificity differences can be explained by differences in PDZ residues that are in contact with the peptide ligands, but these contacts involve both side-chain and main-chain interactions. Most PDZ domains bind peptides in a canonical conformation in which the ligand main chain adopts an extended β-strand conformation by interacting in an antiparallel fashion with a PDZ β-strand. However, a subset of PDZ domains bind peptides with a bent main-chain conformation and the specificities of these non-canonical domains could not be explained based on canonical structures. Our analysis provides a structural portrait of the PDZ domain family, which serves as a guide in understanding the structural basis for the diverse specificities across the family.     

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 /.     

General and targeted statistical potentials for protein-ligand interactions

We present a novel atom-atom potential derived from a database of protein-ligand complexes. First, we clarify the similarities and differences between two statistical potentials described in the literature, PMF and Drugscore. We highlight shortcomings caused by an important factor unaccounted for in their reference states, and describe a new potential, which we name the Astex Statistical Potential (ASP). ASP's reference state considers the difference in exposure of protein atom types towards ligand binding sites. We show that this new potential predicts binding affinities with an accuracy similar to that of Goldscore and Chemscore. We investigate the influence of the choice of reference state by constructing two additional statistical potentials that differ from ASP only in this respect. The reference states in these two potentials are defined along the lines of Drugscore and PMF. In docking experiments, the potential using the new reference state proposed for ASP gives better success rates than when these literature reference states were used; a success rate similar to the established scoring functions Goldscore and Chemscore is achieved with ASP. This is the case both for a large, general validation set of protein-ligand structures and for small test sets of actives against four pharmaceutically relevant targets. Virtual screening experiments for these targets show less discrimination between the different reference states in terms of enrichment. In addition, we describe how statistical potentials can be used in the construction of targeted scoring functions. Examples are given for cdk2, using four different targeted scoring functions, biased towards increasingly large target-specific databases. Using these targeted scoring functions, docking success rates as well as enrichments are significantly better than for the general ASP scoring function. Results improve with the number of structures used in the construction of the target scoring functions, thus illustrating that these targeted ASP potentials can be continuously improved as new structural data become available.     

Identification of specificity and promiscuity of PDZ domain interactions through their dynamic behavior

PDZ domains (PDZs), the most common interaction domain proteins, play critical roles in many cellular processes. PDZs perform their job by binding specific protein partners. However, they are very promiscuous, binding to more than one protein, yet selective at the same time. We examined the binding related dynamics of various PDZs to have insight about their specificity and promiscuity. We used full atomic normal mode analysis and a modified coarse‐grained elastic network model to compute the binding related dynamics. In the latter model, we introduced specificity for each single parameter constant and included the solvation effect implicitly. The modified model, referred to as specific‐Gaussian Network Model (s‐GNM), highlights some interesting differences in the conformational changes of PDZs upon binding to Class I or Class II type peptides. By clustering the residue fluctuation profiles of PDZs, we have shown: (i) binding selectivities can be discriminated from their dynamics, and (ii) the dynamics of different structural regions play critical roles for Class I and Class II specificity. s‐GNM is further tested on a dual‐specific PDZ which showed only Class I specificity when a point mutation exists on the βA‐βB loop. We observe that the binding dynamics change consistently in the mutated and wild type structures. In addition, we found that the binding induced fluctuation profiles can be used to discriminate the binding selectivity of homolog structures. These results indicate that s‐GNM can be a powerful method to study the changes in binding selectivities for mutant or homolog PDZs. Proteins 2009. © 2009 Wiley‐Liss, Inc.     

Open-ITC: an alternate computational approach to analyze the isothermal titration calorimetry data of complex binding mechanisms

Isothermal titration calorimetry (ITC) is an important technique used in quantitatively analyzing the global mechanism of protein-protein or protein-ligand interactions through thermodynamic measurements. Among different binding mechanisms, the parallel and ligand induced protein oligomerization mechanisms are technically difficult to analyze compared with a sequential binding mechanism. Here, we present a methodology implemented as a program "Open-ITC" that eliminates the need for exact analytical expressions for free ligand concentrations [L] and mole fractions of bound ligand θ that are required for the thermogram analysis. Adopting a genetic algorithm-based optimization, the thermodynamic parameters are determined, and its standard error is evaluated at the global minimum by calculating the Jacobian matrix. This approach yielded a statistically consistent result for a single-site and a two-site binding protein-ligand system. Further, a comparative simulation of a two-step sequential, a parallel, and a ligand induced oligomerization model revealed that their mechanistic differences are discernable in ITC thermograms, only if the first binding step is weaker compared with the second binding step (K(1) 相似文献   

Advances in all atom sampling methods for modeling protein-ligand binding affinities

Conformational dynamics plays a fundamental role in the regulation of molecular recognition processes. Conformational heterogeneity and entropy variations upon binding, although not always evident from the analysis of structural data, can substantially affect affinity and specificity. Computer modeling is able to provide some of the most direct insights into these aspects of molecular recognition. We review recent physics-based computational studies that employ advanced conformational sampling algorithms and effective potentials to model the three main classes of degrees of freedom relevant to the binding process: ligand positioning relative to the receptor, ligand and receptor internal reorganization, and hydration. Collectively these studies show that all of these elements are important for proper modeling of protein-ligand interactions.     

Methodology for protein-ligand binding studies: application to a model for drug resistance, the HIV/FIV protease system.

A protocol for the rapid energetic analysis of protein-ligand complexes has been developed. This protocol involves the generation of protein-ligand complex ensembles followed by an analysis of the binding free energy components. We apply this methodology toward understanding the origin of binding specificity within the human immunodeficiency virus/feline immunodeficiency virus (HIV/FIV) protease system, a model system for drug resistance studies. A distinct difference in the internal strain of an inhibitor within each protein environment clearly favors the HIV protease complex, as observed experimentally. Our analysis also predicts that residues within the S2-S3 pockets of the FIV protease active site are responsible for this strain. Close examination of the active site residue contributions to interaction energy and desolvation energy identifies specific amino acids that may also play a role in determining the binding preferences of these two enzymes. Proteins 1999;36:318-331.     

Exploring sequence-structure relationships in the tyrosine kinome space: functional classification of the binding specificity mechanisms for cancer therapeutics

MOTIVATION: Evolutionary and structural conservation patterns shared by more than 500 of identified protein kinases have led to complex sequence-structure relationships of cross-reactivity for kinase inhibitors. Understanding the molecular basis of binding specificity for protein kinases family, which is the central problem in discovery of cancer therapeutics, remains challenging as the inhibitor selectivity is not readily interpreted from chemical proteomics studies, neither it is easily discernable directly from sequence or structure information. We present an integrated view of sequence-structure-binding relationships in the tyrosine kinome space in which evolutionary analysis of the kinases binding sites is combined with computational proteomics profiling of the inhibitor-protein interactions. This approach provides a functional classification of the binding specificity mechanisms for cancer agents targeting protein tyrosine kinases. RESULTS: The proposed functional classification of the kinase binding specificities explores mechanisms in which structural plasticity of the tyrosine kinases and sequence variation of the binding-site residues are linked with conformational preferences of the inhibitors in achieving effective drug binding. The molecular basis of binding specificity for tyrosine kinases may be largely driven by conformational adaptability of the inhibitors to an ensemble of structurally different conformational states of the enzyme, rather than being determined by their phylogenetic proximity in the kinome space or differences in the interactions with the variable binding-site residues. This approach provides a fruitful functional linkage between structural bioinformatics analysis and disease by unraveling the molecular basis of kinase selectivity for the prominent kinase drugs (Imatinib, Dasatinib and Erlotinib) which is consistent with structural and proteomics experiments.     

QSAR-by-NMR: quantitative insights into structural determinants for binding affinity by analysis of 1H/15N chemical shift differences in MMP-3 ligands

A novel strategy is applied to obtain quantitative insights on factors influencing biological affinity in protein-ligand complexes. This approach is based on the detection of ligand binding by (15)N and (1)H amide chemical shift differences in two-dimensional (15)N-heteronuclear single-quantum correlation spectra. Essential structural features linked to affinity can be extracted using statistical analysis of (15)N and (1)H amide chemical shift differences in congeneric series relative to uncomplexed protein spectra, as demonstrated for 20 MMP-3 inhibitors in complex with human matrix metalloproteinase stromelysin (MMP-3). The statistical analysis using PLS led to a significant model, while its chemical interpretation, highlighting the importance of particular residues for affinity, are in agreement to an X-ray structure of one key compound in the homologue MMP-8 binding site.     

Alignment of weakly interacting molecules to protein surfaces using simulations of chemical shift perturbations

Structural studies of protein-ligand complexes are often limited by low solubility, poor affinity, and interfacial motion and, in NMR structures, by the lack of intermolecular NOEs. In the absence of other structural restraints, we use a procedure that compares simulated chemical shift perturbations to observed perturbations to better define the binding orientation of ligands with respect to protein surfaces.