Identification of ligand binding sites of proteins using the Gaussian Network Model

The nonlocal nature of the protein-ligand binding problem is investigated via the Gaussian Network Model with which the residues lying along interaction pathways in a protein and the residues at the binding site are predicted. The predictions of the binding site residues are verified by using several benchmark systems where the topology of the unbound protein and the bound protein-ligand complex are known. Predictions are made on the unbound protein. Agreement of results with the bound complexes indicates that the information for binding resides in the unbound protein. Cliques that consist of three or more residues that are far apart along the primary structure but are in contact in the folded structure are shown to be important determinants of the binding problem. Comparison with known structures shows that the predictive capability of the method is significant.     

Coarse Grained Normal Mode Analysis vs. Refined Gaussian Network Model for Protein Residue-Level Structural Fluctuations

We investigate several approaches to coarse grained normal mode analysis on protein residual-level structural fluctuations by choosing different ways of representing the residues and the forces among them. Single-atom representations using the backbone atoms C _α , C, N, and C _β are considered. Combinations of some of these atoms are also tested. The force constants between the representative atoms are extracted from the Hessian matrix of the energy function and served as the force constants between the corresponding residues. The residue mean-square-fluctuations and their correlations with the experimental B-factors are calculated for a large set of proteins. The results are compared with all-atom normal mode analysis and the residue-level Gaussian Network Model. The coarse-grained methods perform more efficiently than all-atom normal mode analysis, while their B-factor correlations are also higher. Their B-factor correlations are comparable with those estimated by the Gaussian Network Model and in many cases better. The extracted force constants are surveyed for different pairs of residues with different numbers of separation residues in sequence. The statistical averages are used to build a refined Gaussian Network Model, which is able to predict residue-level structural fluctuations significantly better than the conventional Gaussian Network Model in many test cases.     

Causality,transfer entropy,and allosteric communication landscapes in proteins with harmonic interactions

A fast and approximate method of generating allosteric communication landscapes in proteins is presented by using Schreiber's entropy transfer concept in combination with the Gaussian Network Model of proteins. Predictions of the model and the allosteric communication landscapes generated show that information transfer in proteins does not necessarily take place along a single path, but an ensemble of pathways is possible. The model emphasizes that knowledge of entropy only is not sufficient for determining allosteric communication and additional information based on time delayed correlations should be introduced, which leads to the presence of causality in proteins. The model provides a simple tool for mapping entropy sink‐source relations into pairs of residues. By this approach, residues that should be manipulated to control protein activity may be determined. This should be of great importance for allosteric drug design and for understanding the effects of mutations on function. The model is applied to determine allosteric communication in three proteins, Ubiquitin, Pyruvate Kinase, and the PDZ domain. Predictions are in agreement with molecular dynamics simulations and experimental evidence. Proteins 2017; 85:1056–1064. © 2017 Wiley Periodicals, Inc.     

iGNM: a database of protein functional motions based on Gaussian Network Model

MOTIVATION: The knowledge of protein structure is not sufficient for understanding and controlling its function. Function is a dynamic property. Although protein structural information has been rapidly accumulating in databases, little effort has been invested to date toward systematically characterizing protein dynamics. The recent success of analytical methods based on elastic network models, and in particular the Gaussian Network Model (GNM), permits us to perform a high-throughput analysis of the collective dynamics of proteins. RESULTS: We computed the GNM dynamics for 20 058 structures from the Protein Data Bank, and generated information on the equilibrium dynamics at the level of individual residues. The results are stored on a web-based system called iGNM and configured so as to permit the users to visualize or download the results through a standard web browser using a simple search engine. Static and animated images for describing the conformational mobility of proteins over a broad range of normal modes are accessible, along with an online calculation engine available for newly deposited structures. A case study of the dynamics of 20 non-homologous hydrolases is presented to illustrate the utility of the iGNM database for identifying key residues that control the cooperative motions and revealing the connection between collective dynamics and catalytic activity.     

Distinct roles of overlapping and non-overlapping regions of hub protein interfaces in recognition of multiple partners

Cellular functions of an organism are maintained by protein-protein interactions. Those proteins that bind multiple partners asynchronously (date hub proteins) are important to make the interaction network coordinated. It is known that many date hub proteins bind different partners at overlapping (OV) interfaces. To understand how OV interfaces of date hub proteins can recognize multiple partners, we analyzed the difference between OV and non-overlapping (Non-OV) regions of interfaces involved in the binding of different partners. By using the structures of 16 date hub proteins with various interaction partners (ranging from 5 to 33), we compared buried surface area, compositions of amino acid residues and secondary structures, and side-chain orientations. It was found that buried interface residues are important for recognizing multiple partners, while exposed interface residues are important for determining specificity to a particular ligand. In addition, our analyses reveal that residue compositions in OV and Non-OV regions are different and that residues in OV region show diverse side-chain torsion angles to accommodate binding to multiple targets.     

Protein dynamics analysis reveals that missense mutations in cancer-related genes appear frequently on hinge-neighboring residues

Missense mutations have various effects on protein structures, also leading to distorted protein dynamics that plausibly affects the function. We hypothesized that missense mutations in cancer-related genes selectively target hinge-neighboring residues that orchestrate collective structural dynamics. To test our hypothesis, we selected 69 cancer-related genes from the Cancer Gene Census database and their representative protein structures from the Protein Data Bank. We first identified the hinge residues in two global modes of motion by applying the Gaussian Network Model. We then showed that missense mutations are significantly enriched on hinge-neighboring residues in oncogenes and tumor suppressor genes. We observed that several oncogenes (eg, MAP2K1, PTPN11, and KRAS) and tumor suppressor genes (eg, EZH2, CDKN2C, and RHOA) strongly exhibit this phenomenon. This study highlights and rationalizes the functional importance of missense mutations on hinge-neighboring residues in cancer.     

The gaussian network model: precise prediction of residue fluctuations and application to binding problems

The single-parameter Gamma matrix of force constants proposed by the Gaussian Network Model (GNM) is iteratively modified to yield native state fluctuations that agree exactly with experimentally observed values. The resulting optimized Gamma matrix contains residue-specific force constants that may be used for an accurate analysis of ligand binding to single or multiple sites on proteins. Bovine Pancreatic Trypsin Inhibitor (BPTI) is used as an example. The calculated off-diagonal elements of the Gamma matrix, i.e., the optimized spring constants, obey a Lorentzian distribution. The mean value of the spring constants is approximately -0.1, a value much weaker than -1 of the GNM. Few of the spring constants are positive, indicating repulsion between residues. Residue pairs with large number of neighbors have spring constants around the mean, -0.1. Large negative spring constants are between highly correlated pairs of residues. The fluctuations of the distance between anticorrelated pairs of residues are subject to smaller spring constants. The importance of the number of neighbors of residue pairs in determining the elements of the Gamma matrix is pointed out. Allosteric effects of binding on a single or multiple residues of BPTI are illustrated and discussed. Comparison of the predictions of the present model with those of the standard GNM shows that the two models agree at lower modes, i.e., those relating to global motions, but they disagree at higher modes. In the higher modes, the present model points to the important contributions from specific residues whereas the standard GNM fails to do so.     

Distinct ligand specificity of the Tiam1 and Tiam2 PDZ domains

Guanine nucleotide exchange factor proteins of the Tiam family are activators of the Rho GTPase Rac1 and critical for cell morphology, adhesion, migration, and polarity. These proteins are modular and contain a variety of interaction domains, including a single post-synaptic density-95/discs large/zonula occludens-1 (PDZ) domain. Previous studies suggest that the specificities of the Tiam1 and Tiam2 PDZ domains are distinct. Here, we sought to conclusively define these specificities and determine their molecular origin. Using a combinatorial peptide library, we identified a consensus binding sequence for each PDZ domain. Analysis of these consensus sequences and binding assays with peptides derived from native proteins indicated that these two PDZ domains have overlapping but distinct specificities. We also identified residues in two regions (S(0) and S(-2) pockets) of the Tiam1 PDZ domain that are important determinants of ligand specificity. Site-directed mutagenesis of four nonconserved residues in these two regions along with peptide binding analyses confirmed that these residues are crucial for ligand affinity and specificity. Furthermore, double mutant cycle analysis of each region revealed energetic couplings that were dependent on the ligand being investigated. Remarkably, a Tiam1 PDZ domain quadruple mutant had the same specificity as the Tiam2 PDZ domain. Finally, analysis of Tiam family PDZ domain sequences indicated that the PDZ domains segregate into four distinct families based on the residues studied here. Collectively, our data suggest that Tiam family proteins have highly evolved PDZ domain-ligand interfaces with distinct specificities and that they have disparate PDZ domain-dependent biological functions.     

C‐H…O hydrogen bonds in FK506‐binding protein–ligand interactions

Hydrogen bonds are important interaction forces observed in protein structures. They can be classified as stronger or weaker depending on their energy, thereby reflecting on the type of donor. The contribution of weak hydrogen bonds is deemed as an important factor toward structure stability along with the stronger bonds. One such bond, the C‐H…O type hydrogen bond, is shown to make a contribution in maintaining three dimensional structures of proteins. Apart from their presence within protein structures, the role of these bonds in protein–ligand interactions is also noteworthy. In this study, we present a statistical analysis on the presence of C‐H…O hydrogen bonds observed between FKBPs and their cognate ligands. The FK506‐binding proteins (FKBPs) carry peptidyl cis–trans isomerase activity apart from the immunosuppressive property by binding to the immunosuppressive drugs FK506 or rapamycin. Because the active site of FKBPs is lined up by many hydrophobic residues, we speculated that the prevalence of C‐H…O hydrogen bonds will be considerable. In a total of 25 structures analyzed, a higher frequency of C‐H…O hydrogen bonds is observed in comparison with the stronger hydrogen bonds. These C‐H…O hydrogen bonds are dominated by a highly conserved donor, the C^α/β of Val55 and an acceptor, the backbone oxygen of Glu54. Both these residues are positioned in the β4‐α1 loop, whereas the other residues Tyr26, Phe36 and Phe99 with higher frequencies are lined up at the opposite face of the active site. These preferences could be implicated in FKBP pharmacophore models toward enhancing the ligand affinity. This study could be a prelude to studying other proteins with hydrophobic pockets to gain better insights into ligand recognition. Copyright © 2013 John Wiley & Sons, Ltd.     

Identification of specific interactions that drive ligand-induced closure in five enzymes with classic domain movements

In order to better understand ligand-induced closure in domain enzymes, open unliganded X-ray structures and closed liganded X-ray structures have been studied in five enzymes: adenylate kinase, aspartate aminotransferase, citrate synthase, liver alcohol dehydrogenase, and the catalytic subunit of cAMP-dependent protein kinase. A sequential model of ligand binding and domain closure was used to test the hypothesis that the ligand actively drives closure from an open conformation. The analysis supports the assumption that each enzyme has a dedicated binding domain to which the ligand binds first and a closing domain. In every case, a small number of residues are identified to interact with the ligand to initiate and drive domain closure. In all cases except adenylate kinase, the backbone of residues located in an interdomain-bending region (hinge site) is identified to interact with the ligand to aid in driving closure. In adenylate kinase, the side-chain of a residue located directly adjacent to a bending region drives closure. It is thought that by binding near a hinge site the ligand is able to get within interaction range of residues when the enzyme is in the open conformation. Interdomain bending regions not involved in inducing closure are involved in control, helping to determine the location of the hinge axis. Similarities have been discovered between aspartate aminotransferase and citrate synthase that only come to light in the context of their dynamical behaviour in response to binding their substrate. Similarity also exists between liver alcohol dehydrogenase and cAMP-dependent protein kinase whereby groups on NAD and ATP, respectively, mimic the backbone of a single amino acid residue in a process where a three residue segment located at the terminus of a beta-sheet, moves to form hydrogen bonds with the mimic that resemble those found in a parallel beta-sheet. This interaction helps to drive domain closure in a process that has analogy to protein folding.     

Different Types of Interactions Involving Cysteine Sulfhydryl Group in Proteins

Abstract

Various types of interactions involving the sulfhydryl group of free cysteine residues have been analyzed using known protein structures. In a hydrogen bond the -SH group is more amenable to donating its proton to a carbonyl group, rather than acting as a proton acceptor. It rarely interacts with a carboxylate group, and is a poor ligand to bind an anionic substrate. It is quite prone to make contacts that are definitely non-hydrogen bond type. In the S…C=0 interaction the S atom is placed on the face of an amide group (mostly from the main-chain, but there are cases from the side-chain also) close to the C atom. Cases of S…N interaction, where the S atom is on top of the N atom of another residue (both main-, as well as side-chains, including the guanidinium group) are also observed. A considerable number of Cys residues have aromatic residues as neighbors, and here too, the preferred mode of interaction is along the face. The intra-residue S…C=0 interaction constrains the main-chain and side-chain torsion angles (ψ and x₁), whereas the inter-residue interactions are non-local and stabilize the tertiary structure. The S…C=0 interaction may have a role in lowering the pK _avalues of the Cys residues in enzyme active sites.     

Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network

Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.     

Homology modeling of human Toll‐like receptors TLR7, 8, and 9 ligand‐binding domains

Toll‐like receptors (TLRs) play a key role in the innate immune system. The TLR7, 8, and 9 compose a family of intracellularly localized TLRs that signal in response to pathogen‐derived nucleic acids. So far, there are no crystallographic structures for TLR7, 8, and 9. For this reason, their ligand‐binding mechanisms are poorly understood. To enable first predictions of the receptor–ligand interaction sites, we developed three‐dimensional structures for the leucine‐rich repeat ectodomains of human TLR7, 8, and 9 based on homology modeling. To achieve a high sequence similarity between targets and templates, structural segments from all known TLR ectodomain structures (human TLR1/2/3/4 and mouse TLR3/4) were used as candidate templates for the modeling. The resulting models support previously reported essential ligand‐binding residues. They also provide a basis to identify three potential receptor dimerization mechanisms. Additionally, potential ligand‐binding residues are identified using combined procedures. We suggest further investigations of these residues through mutation experiments. Our modeling approach can be extended to other members of the TLR family or other repetitive proteins.     

Structure-wise discrimination of cytosine,thymine, and uracil by proteins in terms of their nonbonded interactions

The molecular recognition and discrimination of very similar ligand moieties by proteins are important subjects in protein–ligand interaction studies. Specificity in the recognition of molecules is determined by the arrangement of protein and ligand atoms in space. The three pyrimidine bases, viz. cytosine, thymine, and uracil, are structurally similar, but the proteins that bind to them are able to discriminate them and form interactions. Since nonbonded interactions are responsible for molecular recognition processes in biological systems, our work attempts to understand some of the underlying principles of such recognition of pyrimidine molecular structures by proteins. The preferences of the amino acid residues to contact the pyrimidine bases in terms of nonbonded interactions; amino acid residue–ligand atom preferences; main chain and side chain atom contributions of amino acid residues; and solvent-accessible surface area of ligand atoms when forming complexes are analyzed. Our analysis shows that the amino acid residues, tyrosine and phenyl alanine, are highly involved in the pyrimidine interactions. Arginine prefers contacts with the cytosine base. The similarities and differences that exist between the interactions of the amino acid residues with each of the three pyrimidine base atoms in our analysis provide insights that can be exploited in designing specific inhibitors competitive to the ligands.     

Network representation of protein interactions: Theory of graph description and analysis

A methodological framework is presented for the graph theoretical interpretation of NMR data of protein interactions. The proposed analysis generalizes the idea of network representations of protein structures by expanding it to protein interactions. This approach is based on regularization of residue‐resolved NMR relaxation times and chemical shift data and subsequent construction of an adjacency matrix that represents the underlying protein interaction as a graph or network. The network nodes represent protein residues. Two nodes are connected if two residues are functionally correlated during the protein interaction event. The analysis of the resulting network enables the quantification of the importance of each amino acid of a protein for its interactions. Furthermore, the determination of the pattern of correlations between residues yields insights into the functional architecture of an interaction. This is of special interest for intrinsically disordered proteins, since the structural (three‐dimensional) architecture of these proteins and their complexes is difficult to determine. The power of the proposed methodology is demonstrated at the example of the interaction between the intrinsically disordered protein osteopontin and its natural ligand heparin.     

Searching for protein-protein interaction sites and docking by the methods of molecular dynamics, grid scoring, and the pairwise interaction potential of amino acid residues

In CAPRI Rounds 1 and 2, we assumed that because there are many ionic charges that weaken electrostatic interaction forces in living cells, the hydrophobic interaction force might be important entropically. As a result of Rounds 1 and 2, the predictions for binding sites and geometric centers were acceptable, but those of the binding axes were poor, because only the largest benzene cluster was used for generating the initial docking structures. These were generated by fitting of benzene clusters formed on the surface of receptor and ligand. In CAPRI Rounds 3-5, the grid-scoring sum on the protein-protein interaction surface and the pairwise potential of the amino acid residues, which were indicated as coming easily into the protein-protein interaction regions, were used as the calculation methods, along with the smaller benzene clusters that participated in benzene cluster fitting. Good predicted models were obtained for Targets 11 and 12. When the modeled receptor proteins were superimposed on the experimental structures, the smallest ligand root-mean-square deviation (RMSD) values corresponding to the RMSD between the model and experimental structures were 6.2 A and 7.3 A, respectively.     

Computer-based screening of functional conformers of proteins

A long-standing goal in biology is to establish the link between function, structure, and dynamics of proteins. Considering that protein function at the molecular level is understood by the ability of proteins to bind to other molecules, the limited structural data of proteins in association with other bio-molecules represents a major hurdle to understanding protein function at the structural level. Recent reports show that protein function can be linked to protein structure and dynamics through network centrality analysis, suggesting that the structures of proteins bound to natural ligands may be inferred computationally. In the present work, a new method is described to discriminate protein conformations relevant to the specific recognition of a ligand. The method relies on a scoring system that matches critical residues with central residues in different structures of a given protein. Central residues are the most traversed residues with the same frequency in networks derived from protein structures. We tested our method in a set of 24 different proteins and more than 260,000 structures of these in the absence of a ligand or bound to it. To illustrate the usefulness of our method in the study of the structure/dynamics/function relationship of proteins, we analyzed mutants of the yeast TATA-binding protein with impaired DNA binding. Our results indicate that critical residues for an interaction are preferentially found as central residues of protein structures in complex with a ligand. Thus, our scoring system effectively distinguishes protein conformations relevant to the function of interest.     

The Mitosis and Neurodevelopment Proteins NDE1 and NDEL1 Form Dimers, Tetramers, and Polymers with a Folded Back Structure in Solution

Paralogs NDE1 (nuclear distribution element 1) and NDEL1 (NDE-like 1) are essential for mitosis and neurodevelopment. Both proteins are predicted to have similar structures, based upon high sequence similarity, and they co-complex in mammalian cells. X-ray diffraction studies and homology modeling suggest that their N-terminal regions (residues 8–167) adopt continuous, extended α-helical coiled-coil structures, but no experimentally derived information on the structure of their C-terminal regions or the architecture of the full-length proteins is available. In the case of NDE1, no biophysical data exists. Here we characterize the structural architecture of both full-length proteins utilizing negative stain electron microscopy along with our established paradigm of chemical cross-linking followed by tryptic digestion, mass spectrometry, and database searching, which we enhance using isotope labeling for mixed NDE1-NDEL1. We determined that full-length NDE1 forms needle-like dimers and tetramers in solution, similar to crystal structures of NDEL1, as well as chain-like end-to-end polymers. The C-terminal domain of each protein, required for interaction with key protein partners dynein and DISC1 (disrupted-in-schizophrenia 1), includes a predicted disordered region that allows a bent back structure. This facilitates interaction of the C-terminal region with the N-terminal coiled-coil domain and is in agreement with previous results showing N- and C-terminal regions of NDEL1 and NDE1 cooperating in dynein interaction. It sheds light on recently identified mutations in the NDE1 gene that cause truncation of the encoded protein. Additionally, analysis of mixed NDE1-NDEL1 complexes demonstrates that NDE1 and NDEL1 can interact directly.