Phosphate binding sites identification in protein structures

Nearly half of known protein structures interact with phosphate-containing ligands, such as nucleotides and other cofactors. Many methods have been developed for the identification of metal ions-binding sites and some for bigger ligands such as carbohydrates, but none is yet available for the prediction of phosphate-binding sites. Here we describe Pfinder, a method that predicts binding sites for phosphate groups, both in the form of ions or as parts of other non-peptide ligands, in proteins of known structure. Pfinder uses the Query3D local structural comparison algorithm to scan a protein structure for the presence of a number of structural motifs identified for their ability to bind the phosphate chemical group. Pfinder has been tested on a data set of 52 proteins for which both the apo and holo forms were available. We obtained at least one correct prediction in 63% of the holo structures and in 62% of the apo. The ability of Pfinder to recognize a phosphate-binding site in unbound protein structures makes it an ideal tool for functional annotation and for complementing docking and drug design methods. The Pfinder program is available at http://pdbfun.uniroma2.it/pfinder.     

Analysis of zinc binding sites in protein crystal structures

The geometrical properties of zinc binding sites in a dataset of high quality protein crystal structures deposited in the Protein Data Bank have been examined to identify important differences between zinc sites that are directly involved in catalysis and those that play a structural role. Coordination angles in the zinc primary coordination sphere are compared with ideal values for each coordination geometry, and zinc coordination distances are compared with those in small zinc complexes from the Cambridge Structural Database as a guide of expected trends. We find that distances and angles in the primary coordination sphere are in general close to the expected (or ideal) values. Deviations occur primarily for oxygen coordinating atoms and are found to be mainly due to H-bonding of the oxygen coordinating ligand to protein residues, bidentate binding arrangements, and multi-zinc sites. We find that H-bonding of oxygen containing residues (or water) to zinc bound histidines is almost universal in our dataset and defines the elec-His-Zn motif. Analysis of the stereochemistry shows that carboxyl elec-His-Zn motifs are geometrically rigid, while water elec-His-Zn motifs show the most geometrical variation. As catalytic motifs have a higher proportion of carboxyl elec atoms than structural motifs, they provide a more rigid framework for zinc binding. This is understood biologically, as a small distortion in the zinc position in an enzyme can have serious consequences on the enzymatic reaction. We also analyze the sequence pattern of the zinc ligands and residues that provide elecs, and identify conserved hydrophobic residues in the endopeptidases that also appear to contribute to stabilizing the catalytic zinc site. A zinc binding template in protein crystal structures is derived from these observations.     

Automated identification of binding sites for phosphorylated ligands in protein structures

Phosphorylation is a crucial step in many cellular processes, ranging from metabolic reactions involved in energy transformation to signaling cascades. In many instances, protein domains specifically recognize the phosphogroup. Knowledge of the binding site provides insights into the interaction, and it can also be exploited for therapeutic purposes. Previous studies have shown that proteins interacting with phosphogroups are highly heterogeneous, and no single property can be used to reliably identify the binding site. Here we present an energy‐based computational procedure that exploits the protein three‐dimensional structure to identify binding sites involved in the recognition of phosphogroups. The procedure is validated on three datasets containing more than 200 proteins binding to ATP, phosphopeptides, and phosphosugars. A comparison against other three generic binding site identification approaches shows higher accuracy values for our method, with a correct identification rate in the 80–90% range for the top three predicted sites. Addition of conservation information further improves the performance. The method presented here can be used as a first step in functional annotation or to guide mutagenesis experiments and further studies such as molecular docking. Proteins 2012;. © 2012 Wiley Periodicals, Inc.     

Prediction of protein binding sites in protein structures using hidden Markov support vector machine

Background  

Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. Recent research on protein binding site prediction has been mainly based on widely known machine learning techniques, such as artificial neural networks, support vector machines, conditional random field, etc. However, the prediction performance is still too low to be used in practice. It is necessary to explore new algorithms, theories and features to further improve the performance.     

PocketMatch: A new algorithm to compare binding sites in protein structures

Background  

Recognizing similarities and deriving relationships among protein molecules is a fundamental requirement in present-day biology. Similarities can be present at various levels which can be detected through comparison of protein sequences or their structural folds. In some cases similarities obscure at these levels could be present merely in the substructures at their binding sites. Inferring functional similarities between protein molecules by comparing their binding sites is still largely exploratory and not as yet a routine protocol. One of the main reasons for this is the limitation in the choice of appropriate analytical tools that can compare binding sites with high sensitivity. To benefit from the enormous amount of structural data that is being rapidly accumulated, it is essential to have high throughput tools that enable large scale binding site comparison.     

Localization of binding sites in protein structures by optimization of a composite scoring function

The rise in the number of functionally uncharacterized protein structures is increasing the demand for structure-based methods for functional annotation. Here, we describe a method for predicting the location of a binding site of a given type on a target protein structure. The method begins by constructing a scoring function, followed by a Monte Carlo optimization, to find a good scoring patch on the protein surface. The scoring function is a weighted linear combination of the z-scores of various properties of protein structure and sequence, including amino acid residue conservation, compactness, protrusion, convexity, rigidity, hydrophobicity, and charge density; the weights are calculated from a set of previously identified instances of the binding-site type on known protein structures. The scoring function can easily incorporate different types of information useful in localization, thus increasing the applicability and accuracy of the approach. To test the method, 1008 known protein structures were split into 20 different groups according to the type of the bound ligand. For nonsugar ligands, such as various nucleotides, binding sites were correctly identified in 55%-73% of the cases. The method is completely automated (http://salilab.org/patcher) and can be applied on a large scale in a structural genomics setting.     

Novel metal sites in protein structures

Recently, the three-dimensional structures of several novel metalloenzymes have been solved. Of special interest are those containing uncommon and/or not well characterized metals such as molybdenum, tungsten, nickel, vanadium and cobalt. Modulated by the protein environment, the specific properties of these metals and of special metal-binding cofactors such as siroheme and topa quinone are used to catalyze a vast array of fascinating reactions.     

FTSite: high accuracy detection of ligand binding sites on unbound protein structures

MOTIVATION: Binding site identification is a classical problem that is important for a range of applications, including the structure-based prediction of function, the elucidation of functional relationships among proteins, protein engineering and drug design. We describe an accurate method of binding site identification, namely FTSite. This method is based on experimental evidence that ligand binding sites also bind small organic molecules of various shapes and polarity. The FTSite algorithm does not rely on any evolutionary or statistical information, but achieves near experimental accuracy: it is capable of identifying the binding sites in over 94% of apo proteins from established test sets that have been used to evaluate many other binding site prediction methods. AVAILABILITY: FTSite is freely available as a web-based server at http://ftsite.bu.edu.     

Recognition of functional sites in protein structures

Recognition of regions on the surface of one protein, that are similar to a binding site of another is crucial for the prediction of molecular interactions and for functional classifications. We first describe a novel method, SiteEngine, that assumes no sequence or fold similarities and is able to recognize proteins that have similar binding sites and may perform similar functions. We achieve high efficiency and speed by introducing a low-resolution surface representation via chemically important surface points, by hashing triangles of physico-chemical properties and by application of hierarchical scoring schemes for a thorough exploration of global and local similarities. We proceed to rigorously apply this method to functional site recognition in three possible ways: first, we search a given functional site on a large set of complete protein structures. Second, a potential functional site on a protein of interest is compared with known binding sites, to recognize similar features. Third, a complete protein structure is searched for the presence of an a priori unknown functional site, similar to known sites. Our method is robust and efficient enough to allow computationally demanding applications such as the first and the third. From the biological standpoint, the first application may identify secondary binding sites of drugs that may lead to side-effects. The third application finds new potential sites on the protein that may provide targets for drug design. Each of the three applications may aid in assigning a function and in classification of binding patterns. We highlight the advantages and disadvantages of each type of search, provide examples of large-scale searches of the entire Protein Data Base and make functional predictions.     

Searching for functional sites in protein structures

An ability to assign protein function from protein structure is important for structural genomics consortia. The complex relationship between protein fold and function highlights the necessity of looking beyond the global fold of a protein to specific functional sites. Many computational methods have been developed that address this issue. These include evolutionary trace methods, methods that involve the calculation and assessment of maximal superpositions, methods based on graph theory, and methods that apply machine learning techniques. Such function prediction techniques have been applied to the identification of enzyme catalytic triads and DNA-binding motifs.     

Similarity networks of protein binding sites

An increasing attention has been dedicated to the characterization of complex networks within the protein world. This work is reporting how we uncovered networked structures that reflected the structural similarities among protein binding sites. First, a 211 binding sites dataset has been compiled by removing the redundant proteins in the Protein Ligand Database (PLD) (http://www-mitchell.ch.cam.ac.uk/pld/). Using a clique detection algorithm we have performed all-against-all binding site comparisons among the 211 available ones. Within the set of nodes representing each binding site an edge was added whenever a pair of binding sites had a similarity higher than a threshold value. The generated similarity networks revealed that many nodes had few links and only few were highly connected, but due to the limited data available it was not possible to definitively prove a scale-free architecture. Within the same dataset, the binding site similarity networks were compared with the networks of sequence and fold similarity networks. In the protein world, indications were found that structure is better conserved than sequence, but on its own, sequence was better conserved than the subset of functional residues forming the binding site. Because a binding site is strongly linked with protein function, the identification of protein binding site similarity networks could accelerate the functional annotation of newly identified genes. In view of this we have discussed several potential applications of binding site similarity networks, such as the construction of novel binding site classification databases, as well as the implications for protein molecular design in general and computational chemogenomics in particular.     

Beyond structural genomics: computational approaches for the identification of ligand binding sites in protein structures

Structural genomics projects have revealed structures for a large number of proteins of unknown function. Understanding the interactions between these proteins and their ligands would provide an initial step in their functional characterization. Binding site identification methods are a fast and cost-effective way to facilitate the characterization of functionally important protein regions. In this review we describe our recently developed methods for binding site identification in the context of existing methods. The advantage of energy-based approaches is emphasized, since they provide flexibility in the identification and characterization of different types of binding sites.     

Geometric constraints for porphyrin binding in helical protein binding sites

Helical bundles which bind heme and porphyrin cofactors have been popular targets for cofactor-containing de novo protein design. By analyzing a highly nonredundant subset of the protein databank we have determined a rotamer distribution for helical histidines bound to heme cofactors. Analysis of the entire nonredundant database for helical sequence preferences near the ligand histidine demonstrated little preference for amino acid side chain identity, size, or charge. Analysis of the database subdivided by ligand histidine rotamer, however, reveals strong preferences in each case, and computational modeling illuminates the structural basis for some of these findings. The majority of the rotamer distribution matches that predicted by molecular simulation of a single porphyrin-bound histidine residue placed in the center of an all-alanine helix, and the deviations explain two prominent features of natural heme protein binding sites: heme distortion in the case of the cytochromes C in the m166 histidine rotamer, and a highly prevalent glycine residue in the t73 histidine rotamer. These preferences permit derivation of helical consensus sequence templates which predict optimal side chain-cofactor packing interactions for each rotamer. These findings thus promise to guide future design endeavors not only in the creation of higher affinity heme and porphyrin binding sites, but also in the direction of bound cofactor geometry.     

LigBase: a database of families of aligned ligand binding sites in known protein sequences and structures

A database comprising all ligand-binding sites of known structure aligned with all related protein sequences and structures is described. Currently, the database contains approximately 50000 ligand-binding sites for small molecules found in the Protein Data Bank (PDB). The structure-structure alignments are obtained by the Combinatorial Extension (CE) program (Shindyalov and Bourne, Protein Eng., 11, 739-747, 1998) and sequence-structure alignments are extracted from the ModBase database of comparative protein structure models for all known protein sequences (Sanchez et al., Nucleic Acids Res., 28, 250-253, 2000). It is possible to search for binding sites in LigBase by a variety of criteria. LigBase reports summarize ligand data including relevant structural information from the PDB file, such as ligand type and size, and contain links to all related protein sequences in the TrEMBL database. Residues in the binding sites are graphically depicted for comparison with other structurally defined family members. LigBase provides a resource for the analysis of families of related binding sites.     

Structures of in vitro evolved binding sites on neocarzinostatin scaffold reveal unanticipated evolutionary pathways

We have recently applied in vitro evolution methods to create in Neocarzinostatin a new binding site for a target molecule unrelated to its natural ligand. The main objective of this work was to solve the structure of some of the selected binders in complex with the target molecule: testosterone. Three proteins (1a.15, 3.24 and 4.1) were chosen as representative members of sequence families that came out of the selection process within different randomization schemes. In order to evaluate ligand-induced conformational adaptation, we also determined the structure of one of the proteins (3.24) in the free and complexed forms. Surprisingly, all these mutants bind not one but two molecules of testosterone in two very different ways. The 3.24 structure revealed that the protein spontaneously evolved in the system to bind two ligand molecules in one single binding crevice. These two binding sites are formed by substituted as well as by non-variable side-chains. The comparison with the free structure shows that only limited structural changes are observed upon ligand binding. The X-ray structures of the complex formed by 1a.15 and 4.1 Neocarzinostatin mutants revealed that the two variants form very similar dimers. These dimers were observed neither for the uncomplexed variants nor for wild-type Neocarzinostatin but were shown here to be induced by ligand binding. Comparison of the three complexed forms clearly suggests that these unanticipated structural responses resulted from the molecular arrangement used for the selection experiments.     

Analysis of water patterns in protein kinase binding sites

Deregulation of protein kinases is associated with numerous diseases, making them important targets for drug discovery. The majority of drugs target the catalytic site of these proteins, but due to the high level of similarity within the ATP binding sites of protein kinases, it is often difficult to achieve the required pharmacological selectivity. In this study, we describe the identification and subsequent analysis of water patterns in the ATP binding sites of 171 protein kinase structures, comprising 19 different kinases from various branches of the kinome, and demonstrate that structurally similar binding sites often have significantly different water patterns. We show that the observed variations in water patterns of different, but structurally similar kinases can be exploited in the structure-based design of potent and selective kinase inhibitors.     

Identification of N-methyl-D-aspartic acid (NMDA) receptor subtype-specific binding sites that mediate direct interactions with scaffold protein PSD-95

N-methyl-D-aspartate (NMDA) neurotransmitter receptors and the postsynaptic density-95 (PSD-95) membrane-associated guanylate kinase (MAGUK) family of scaffolding proteins are integral components of post-synaptic macromolecular signaling complexes that serve to propagate glutamate responses intracellularly. Classically, NMDA receptor NR2 subunits associate with PSD-95 MAGUKs via a conserved ES(E/D)V amino acid sequence located at their C termini. We previously challenged this dogma to demonstrate a second non-ES(E/D)V PSD-95-binding site in both NMDA receptor NR2A and NR2B subunits. Here, using a combination of co-immunoprecipitations from transfected mammalian cells, yeast two-hybrid interaction assays, and glutathione S-transferase (GST) pulldown assays, we show that NR2A subunits interact directly with PSD-95 via the C-terminal ESDV motif and additionally via an Src homology 3 domain-binding motif that associates with the Src homology 3 domain of PSD-95. Peptide inhibition of co-immunoprecipitations of NR2A and PSD-95 demonstrates that both the ESDV and non-ESDV sites are required for association in native brain tissue. Furthermore, we refine the non-ESDV site within NR2B to residues 1149-1157. These findings provide a molecular basis for the differential association of NMDA receptor subtypes with PSD-95 MAGUK scaffold proteins. These selective interactions may contribute to the organization, lateral mobility, and ultimately the function of NMDA receptor subtypes at synapses. Furthermore, they provide a more general molecular mechanism by which the scaffold, PSD-95, may discriminate between potential interacting partner proteins.     

Selective prediction of interaction sites in protein structures with THEMATICS

Background  

Methods are now available for the prediction of interaction sites in protein 3D structures. While many of these methods report high success rates for site prediction, often these predictions are not very selective and have low precision. Precision in site prediction is addressed using Theoretical Microscopic Titration Curves (THEMATICS), a simple computational method for the identification of active sites in enzymes. Recall and precision are measured and compared with other methods for the prediction of catalytic sites.     

Evaluating conformational changes in protein structures binding RNA

Many protein-RNA recognition events are known to exhibit conformational changes from qualitative observations of individual complexes. However, a quantitative estimation of conformational changes is required if protein-RNA docking and template-based methods for RNA binding site prediction are to be developed. This study presents the first quantitative evaluation of conformational changes that occur when proteins bind RNA. The analysis of twelve RNA-binding proteins in the bound and unbound states using error-scaled difference distance matrices is presented. The binding site residues are mapped to each structure, and the conformational changes that affect these residues are evaluated. Of the twelve proteins four exhibit greater movements in nonbinding site residues, and a further four show the greatest movements in binding site residues. The remaining four proteins display no significant conformational change. When interface residues are found to be in conformationally variable regions of the protein they are typically seen to move less than 2 A between the bound and unbound conformations. The current data indicate that conformational changes in the binding site residues of RNA binding proteins may not be as significant as previously suggested, but a larger data set is required before wider conclusions may be drawn. The implications of the observed conformational changes for protein function prediction are discussed.