Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins

Ligand–protein interactions are essential for biological processes, and precise characterization of protein binding sites is crucial to understand protein functions. MED‐SuMo is a powerful technology to localize similar local regions on protein surfaces. Its heuristic is based on a 3D representation of macromolecules using specific surface chemical features associating chemical characteristics with geometrical properties. MED‐SMA is an automated and fast method to classify binding sites. It is based on MED‐SuMo technology, which builds a similarity graph, and it uses the Markov Clustering algorithm. Purine binding sites are well studied as drug targets. Here, purine binding sites of the Protein DataBank (PDB) are classified. Proteins potentially inhibited or activated through the same mechanism are gathered. Results are analyzed according to PROSITE annotations and to carefully refined functional annotations extracted from the PDB. As expected, binding sites associated with related mechanisms are gathered, for example, the Small GTPases. Nevertheless, protein kinases from different Kinome families are also found together, for example, Aurora‐A and CDK2 proteins which are inhibited by the same drugs. Representative examples of different clusters are presented. The effectiveness of the MED‐SMA approach is demonstrated as it gathers binding sites of proteins with similar structure‐activity relationships. Moreover, an efficient new protocol associates structures absent of cocrystallized ligands to the purine clusters enabling those structures to be associated with a specific binding mechanism. Applications of this classification by binding mode similarity include target‐based drug design and prediction of cross‐reactivity and therefore potential toxic side effects.     

Hidden partners: Using cross‐docking calculations to predict binding sites for proteins with multiple interactions

Protein‐protein interactions control a large range of biological processes and their identification is essential to understand the underlying biological mechanisms. To complement experimental approaches, in silico methods are available to investigate protein‐protein interactions. Cross‐docking methods, in particular, can be used to predict protein binding sites. However, proteins can interact with numerous partners and can present multiple binding sites on their surface, which may alter the binding site prediction quality. We evaluate the binding site predictions obtained using complete cross‐docking simulations of 358 proteins with 2 different scoring schemes accounting for multiple binding sites. Despite overall good binding site prediction performances, 68 cases were still associated with very low prediction quality, presenting individual area under the specificity‐sensitivity ROC curve (AUC) values below the random AUC threshold of 0.5, since cross‐docking calculations can lead to the identification of alternate protein binding sites (that are different from the reference experimental sites). For the large majority of these proteins, we show that the predicted alternate binding sites correspond to interaction sites with hidden partners, that is, partners not included in the original cross‐docking dataset. Among those new partners, we find proteins, but also nucleic acid molecules. Finally, for proteins with multiple binding sites on their surface, we investigated the structural determinants associated with the binding sites the most targeted by the docking partners.     

The involvement of coordinative interactions in the binding of dihydrolipoamide dehydrogenase to titanium dioxide—Localization of a putative binding site

Titanium (Ti) and its alloys are widely used in orthodontic and orthopedic implants by virtue to their high biocompatibility, mechanical strength, and high resistance to corrosion. Biointegration of the implants with the tissue requires strong interactions, which involve biological molecules, proteins in particular, with metal oxide surfaces. An exocellular high‐affinity titanium dioxide (TiO₂)–binding protein (TiBP), purified from Rhodococcus ruber , has been previously studied in our lab. This protein was shown to be homologous with the orthologous cytoplasmic rhodococcal dihydrolipoamide dehydrogenase (rhDLDH). We have found that rhDLDH and its human homolog (hDLDH) share the TiO₂‐binding capabilities with TiBP. Intrigued by the unique TiO₂‐binding properties of hDLDH, we anticipated that it may serve as a molecular bridge between Ti‐based medical structures and human tissues. The objective of the current study was to locate the region and the amino acids of the protein that mediate the protein‐TiO₂ surface interaction. We demonstrated the role of acidic amino acids in the nonelectrostatic enzyme/dioxide interactions at neutral pH. The observation that the interaction of DLDH with various metal oxides is independent of their isoelectric values strengthens this notion. DLDH does not lose its enzymatic activity upon binding to TiO₂, indicating that neither the enzyme undergoes major conformational changes nor the TiO₂ binding site is blocked. Docking predictions suggest that both rhDLDH and hDLDH bind TiO₂ through similar regions located far from the active site and the dimerization sites. The putative TiO₂‐binding regions of both the bacterial and human enzymes were found to contain a CHED (Cys, His, Glu, Asp) motif, which has been shown to participate in metal‐binding sites in proteins.     

Composite structural motifs of binding sites for delineating biological functions of proteins

Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs that represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures.     

Characterization of surface binding sites in glycoside hydrolases: A computational study

Structural properties of carbohydrate surface binding sites (SBSs) were investigated with computational methods. Eighty‐five SBSs of 44 enzymes in 119 Protein Data Bank (PDB) files were collected as a dataset. On the basis of SBSs shape, they were divided into 3 categories: flat surfaces, clefts, and cavities (types A, B, and C, respectively). Ligand varieties showed the correlation between shape of SBSs and ligands size. To reduce cut‐off differences in each SBSs with different ligand size, molecular docking were performed. Molecular docking results were used to refine SBSs classification and binding sites cut‐off. Docking results predicted putative ligands positions and displayed dependence of the ligands binding mode to the structural type of SBSs. Physicochemical properties of SBSs were calculated for all docking results with YASARA Structure. The results showed that all SBSs are hydrophilic, while their charges could vary and depended to ligand size and defined cut‐off. Surface binding sites type B had highest average values of solvent accessible surface area. Analysis of interactions showed that hydrophobic interactions occur more than hydrogen bonds, which is related to the presence of aromatic residues and carbohydrates interactions.     

Sequence and structural features of carbohydrate binding in proteins and assessment of predictability using a neural network

Background  

Protein-Carbohydrate interactions are crucial in many biological processes with implications to drug targeting and gene expression. Nature of protein-carbohydrate interactions may be studied at individual residue level by analyzing local sequence and structure environments in binding regions in comparison to non-binding regions, which provide an inherent control for such analyses. With an ultimate aim of predicting binding sites from sequence and structure, overall statistics of binding regions needs to be compiled. Sequence-based predictions of binding sites have been successfully applied to DNA-binding proteins in our earlier works. We aim to apply similar analysis to carbohydrate binding proteins. However, due to a relatively much smaller region of proteins taking part in such interactions, the methodology and results are significantly different. A comparison of protein-carbohydrate complexes has also been made with other protein-ligand complexes.     

The specificity of cross-reactivity: promiscuous antibody binding involves specific hydrogen bonds rather than nonspecific hydrophobic stickiness

Proteins are renowned for their specificity of function. There is, however, accumulating evidence that many proteins, from enzymes to antibodies, are functionally promiscuous. Promiscuity is of considerable physiological importance. In the immune system, cross‐reactive or multispecific antibodies are implicated in autoimmune and allergy conditions. In most cases, however, the mechanism behind promiscuity and the relationship between specific and promiscuous activities are unknown. Are the two contradictory? Or can a protein exhibit several unrelated activities each of which is highly specific? To address these questions, we studied a multispecific IgE antibody (SPE7) elicited against a 2,4‐dinitrophenyl hapten (DNP). SPE7 is able to distinguish between closely related derivatives such as NP (nitrophenol) and DNP, yet it can also bind a number of unrelated ligands. We find that, like DNP, the cross‐reactants are themselves bound specifically—close derivatives of these cross‐reactants show very low or no binding to SPE7. It has been suggested that cross‐reactivity is simply due to “hydrophobic stickiness”, nonspecific interactions between hydrophobic ligands and binding sites. However, partitioning experiments reveal that affinity for SPE7 is unrelated to ligand hydrophobicity. These data, combined with crystal structures of SPE7 in complex with four different ligands, demonstrate that each cross‐reactant is bound specifically, forming different hydrogen bonds dependant upon its particular chemistry and the availability of complementary antibody residues. SPE7 is highly homologous to the germline antinitrophenol (NP) antibody B1–8. By comparing the sequences and binding patterns of SPE7 and B1–8, we address the relationship between affinity maturation, specificity, and cross‐reactivity.     

Non-covalent binding of membrane lipids to membrane proteins

Polar lipids and membrane proteins are major components of biological membranes, both cell membranes and membranes of enveloped viruses. How these two classes of membrane components interact with each other to influence the function of biological membranes is a fundamental question that has attracted intense interest since the origins of the field of membrane studies. One of the most powerful ideas that driven the field is the likelihood that lipids bind to membrane proteins at specific sites, modulating protein structure and function. However only relatively recently has high resolution structure determination of membrane proteins progressed to the point of providing atomic level structure of lipid binding sites on membrane proteins. Analysis of X-ray diffraction, electron crystallography and NMR data over 100 specific lipid binding sites on membrane proteins. These data demonstrate tight lipid binding of both phospholipids and cholesterol to membrane proteins. Membrane lipids bind to membrane proteins by their headgroups, or by their acyl chains, or binding is mediated by the entire lipid molecule. When headgroups bind, binding is stabilized by polar interactions between lipid headgroups and the protein. When acyl chains bind, van der Waals effects dominate as the acyl chains adopt conformations that complement particular sites on the rough protein surface. No generally applicable motifs for binding have yet emerged. Previously published biochemical and biophysical data link this binding with function. This Article is Part of a Special Issue Entitled: Membrane Structure and Function: Relevance in the Cell's Physiology, Pathology and Therapy.     

Differences in signalling by directly and indirectly binding ligands in bacterial chemotaxis

In chemotaxis of Escherichia coli and other bacteria, extracellular stimuli are perceived by transmembrane receptors that bind their ligands either directly, or indirectly through periplasmic‐binding proteins (BPs). As BPs are also involved in ligand uptake, they provide a link between chemotaxis and nutrient utilization by cells. However, signalling by indirectly binding ligands remains much less understood than signalling by directly binding ligands. Here, we compared intracellular responses mediated by both types of ligands and developed a new mathematical model for signalling by indirectly binding ligands. We show that indirect binding allows cells to better control sensitivity to specific ligands in response to their nutrient environment and to coordinate chemotaxis with ligand transport, but at the cost of the dynamic range being much narrower than for directly binding ligands. We further demonstrate that signal integration by the chemosensory complexes does not depend on the type of ligand. Overall, our data suggest that the distinction between signalling by directly and indirectly binding ligands is more physiologically important than the traditional distinction between high‐ and low‐abundance receptors.     

Exhaustive comparison and classification of ligand‐binding surfaces in proteins

Many proteins function by interacting with other small molecules (ligands). Identification of ligand‐binding sites (LBS) in proteins can therefore help to infer their molecular functions. A comprehensive comparison among local structures of LBSs was previously performed, in order to understand their relationships and to classify their structural motifs. However, similar exhaustive comparison among local surfaces of LBSs (patches) has never been performed, due to computational complexity. To enhance our understanding of LBSs, it is worth performing such comparisons among patches and classifying them based on similarities of their surface configurations and electrostatic potentials. In this study, we first developed a rapid method to compare two patches. We then clustered patches corresponding to the same PDB chemical component identifier for a ligand, and selected a representative patch from each cluster. We subsequently exhaustively as compared the representative patches and clustered them using similarity score, PatSim. Finally, the resultant PatSim scores were compared with similarities of atomic structures of the LBSs and those of the ligand‐binding protein sequences and functions. Consequently, we classified the patches into ～2000 well‐characterized clusters. We found that about 63% of these clusters are used in identical protein folds, although about 25% of the clusters are conserved in distantly related proteins and even in proteins with cross‐fold similarity. Furthermore, we showed that patches with higher PatSim score have potential to be involved in similar biological processes.     

Landscape of protein–small ligand binding modes

Elucidating the mechanisms of specific small‐molecule (ligand) recognition by proteins is a long‐standing conundrum. While the structures of these molecules, proteins and ligands, have been extensively studied, protein–ligand interactions, or binding modes, have not been comprehensively analyzed. Although methods for assessing similarities of binding site structures have been extensively developed, the methods for the computational treatment of binding modes have not been well established. Here, we developed a computational method for encoding the information about binding modes as graphs, and assessing their similarities. An all‐against‐all comparison of 20,040 protein–ligand complexes provided the landscape of the protein–ligand binding modes and its relationships with protein‐ and chemical spaces. While similar proteins in the same SCOP Family tend to bind relatively similar ligands with similar binding modes, the correlation between ligand and binding similarities was not very high (R² = 0.443). We found many pairs with novel relationships, in which two evolutionally distant proteins recognize dissimilar ligands by similar binding modes (757,474 pairs out of 200,790,780 pairs were categorized into this relationship, in our dataset). In addition, there were an abundance of pairs of homologous proteins binding to similar ligands with different binding modes (68,217 pairs). Our results showed that many interesting relationships between protein–ligand complexes are still hidden in the structure database, and our new method for assessing binding mode similarities is effective to find them.     

Prediction of carbohydrate binding sites on protein surfaces with 3-dimensional probability density distributions of interacting atoms

Non-covalent protein-carbohydrate interactions mediate molecular targeting in many biological processes. Prediction of non-covalent carbohydrate binding sites on protein surfaces not only provides insights into the functions of the query proteins; information on key carbohydrate-binding residues could suggest site-directed mutagenesis experiments, design therapeutics targeting carbohydrate-binding proteins, and provide guidance in engineering protein-carbohydrate interactions. In this work, we show that non-covalent carbohydrate binding sites on protein surfaces can be predicted with relatively high accuracy when the query protein structures are known. The prediction capabilities were based on a novel encoding scheme of the three-dimensional probability density maps describing the distributions of 36 non-covalent interacting atom types around protein surfaces. One machine learning model was trained for each of the 30 protein atom types. The machine learning algorithms predicted tentative carbohydrate binding sites on query proteins by recognizing the characteristic interacting atom distribution patterns specific for carbohydrate binding sites from known protein structures. The prediction results for all protein atom types were integrated into surface patches as tentative carbohydrate binding sites based on normalized prediction confidence level. The prediction capabilities of the predictors were benchmarked by a 10-fold cross validation on 497 non-redundant proteins with known carbohydrate binding sites. The predictors were further tested on an independent test set with 108 proteins. The residue-based Matthews correlation coefficient (MCC) for the independent test was 0.45, with prediction precision and sensitivity (or recall) of 0.45 and 0.49 respectively. In addition, 111 unbound carbohydrate-binding protein structures for which the structures were determined in the absence of the carbohydrate ligands were predicted with the trained predictors. The overall prediction MCC was 0.49. Independent tests on anti-carbohydrate antibodies showed that the carbohydrate antigen binding sites were predicted with comparable accuracy. These results demonstrate that the predictors are among the best in carbohydrate binding site predictions to date.     

Proteome-wide prediction of overlapping small molecule and protein binding sites using structure

Small molecules that modulate protein-protein interactions are of great interest for chemical biology and therapeutics. Here I present a structure-based approach to predict 'bi-functional' sites able to bind both small molecule ligands and proteins, in proteins of unknown structure. First, I develop a homology-based annotation method that transfers binding sites of known three-dimensional structure onto protein sequences, predicting residues in ligand and protein binding sites with estimated true positive rates of 98% and 88%, respectively, at 1% false positive rates. Applying this method to the human proteome predicts 8463 proteins with bi-functional residues and correctly recovers the targets of known interaction modulators. Proteins with significantly (p < 0.01) more bi-functional residues than expected were found to be enriched in regulatory and depleted in metabolism functions. Finally, I demonstrate the utility of the method by describing examples of predicted overlap and evidence of their biological and therapeutic relevance. The results suggest that combining the structures of known binding sites with established fold detection algorithms can predict regions of protein-protein interfaces that are amenable to small molecule modulation. Open-source software and the results for several complete proteomes are available at http://pibase.janelia.org/homolobind.     

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 effect of denaturants on protein thermal stability analyzed through a theoretical model considering multiple binding sites

A novel mathematical development applied to protein ligand binding thermodynamics is proposed, which allows the simulation, and therefore the analysis of the effects of multiple and independent binding sites to the Native and/or Unfolded protein conformations, with different binding constant values. Protein stability is affected when it binds to a small number of high affinity ligands or to a high number of low affinity ligands. Differential scanning calorimetry (DSC) measures released or absorbed energy of thermally induced structural transitions of biomolecules. This paper presents the general theoretical development for the analysis of thermograms of proteins obtained for n-ligands bound to the native protein and m-ligands bound to their unfolded form. In particular, the effect of ligands with low affinity and with a high number of binding sites (n and/or m > 50) is analyzed. If the interaction with the native form of the protein is the one that predominates, they are considered stabilizers and if the binding with the unfolded species predominates, it is expected a destabilizing effect. The formalism presented here can be adapted to fitting routines in order to simultaneously obtain the unfolding energy and ligand binding energy of the protein. The effect of guanidinium chloride on bovine serum albumin thermal stability, was successfully analyzed with the model considering low number of middle affinity binding sites to the native state and a high number of weak binding sites to the unfolded state.     

Deciphering common recognition principles of nucleoside mono/di and tri‐phosphates binding in diverse proteins via structural matching of their binding sites

Nucleoside triphosphate (NTP) ligands are of high biological importance and are essential for all life forms. A pre‐requisite for them to participate in diverse biochemical processes is their recognition by diverse proteins. It is thus of great interest to understand the basis for such recognition in different proteins. Towards this, we have used a structural bioinformatics approach and analyze structures of 4677 NTP complexes available in Protein Data Bank (PDB). Binding sites were extracted and compared exhaustively using PocketMatch, a sensitive in‐house site comparison algorithm, which resulted in grouping the entire dataset into 27 site‐types. Each of these site‐types represent a structural motif comprised of two or more residue conservations, derived using another in‐house tool for superposing binding sites, PocketAlign. The 27 site‐types could be grouped further into 9 super‐types by considering partial similarities in the sites, which indicated that the individual site‐types comprise different combinations of one or more site features. A scan across PDB using the 27 structural motifs determined the motifs to be specific to NTP binding sites, and a computational alanine mutagenesis indicated that residues identified to be highly conserved in the motifs are also most contributing to binding. Alternate orientations of the ligand in several site‐types were observed and rationalized, indicating the possibility of some residues serving as anchors for NTP recognition. The presence of multiple site‐types and the grouping of multiple folds into each site‐type is strongly suggestive of convergent evolution. Knowledge of determinants obtained from this study will be useful for detecting function in unknown proteins. Proteins 2017; 85:1699–1712. © 2017 Wiley Periodicals, Inc.