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.     

Coupling dynamics and evolutionary information with structure to identify protein regulatory and functional binding sites

Binding sites in proteins can be either specifically functional binding sites (active sites) that bind specific substrates with high affinity or regulatory binding sites (allosteric sites), that modulate the activity of functional binding sites through effector molecules. Owing to their significance in determining protein function, the identification of protein functional and regulatory binding sites is widely acknowledged as an important biological problem. In this work, we present a novel binding site prediction method, Active and Regulatory site Prediction (AR-Pred), which supplements protein geometry, evolutionary, and physicochemical features with information about protein dynamics to predict putative active and allosteric site residues. As the intrinsic dynamics of globular proteins plays an essential role in controlling binding events, we find it to be an important feature for the identification of protein binding sites. We train and validate our predictive models on multiple balanced training and validation sets with random forest machine learning and obtain an ensemble of discrete models for each prediction type. Our models for active site prediction yield a median area under the curve (AUC) of 91% and Matthews correlation coefficient (MCC) of 0.68, whereas the less well-defined allosteric sites are predicted at a lower level with a median AUC of 80% and MCC of 0.48. When tested on an independent set of proteins, our models for active site prediction show comparable performance to two existing methods and gains compared to two others, while the allosteric site models show gains when tested against three existing prediction methods. AR-Pred is available as a free downloadable package at https://github.com/sambitmishra0628/AR-PRED_source .     

Characterization of single-stranded DNA-binding proteins from Mycobacteria. The carboxyl-terminal of domain of SSB is essential for stable association with its cognate RecA protein

Single-stranded DNA-binding proteins (SSB) play an important role in most aspects of DNA metabolism including DNA replication, repair, and recombination. We report here the identification and characterization of SSB proteins of Mycobacterium smegmatis and Mycobacterium tuberculosis. Sequence comparison of M. smegmatis SSB revealed that it is homologous to M. tuberculosis SSB, except for a small spacer connecting the larger amino-terminal domain with the extreme carboxyl-terminal tail. The purified SSB proteins of mycobacteria bound single-stranded DNA with high affinity, and the association and dissociation constants were similar to that of the prototype SSB. The proteolytic signatures of free and bound forms of SSB proteins disclosed that DNA binding was associated with structural changes at the carboxyl-terminal domain. Significantly, SSB proteins from mycobacteria displayed high affinity for cognate RecA, whereas Escherichia coli SSB did not under comparable experimental conditions. Accordingly, SSB and RecA were coimmunoprecipitated from cell lysates, further supporting an interaction between these proteins in vivo. The carboxyl-terminal domain of M. smegmatis SSB, which is not essential for interaction with ssDNA, is the site of binding of its cognate RecA. These studies provide the first evidence for stable association of eubacterial SSB proteins with their cognate RecA, suggesting that these two proteins might function together during DNA repair and/or recombination.     

Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design.

Identification and size characterization of surface pockets and occluded cavities are initial steps in protein structure-based ligand design. A new program, CAST, for automatically locating and measuring protein pockets and cavities, is based on precise computational geometry methods, including alpha shape and discrete flow theory. CAST identifies and measures pockets and pocket mouth openings, as well as cavities. The program specifies the atoms lining pockets, pocket openings, and buried cavities; the volume and area of pockets and cavities; and the area and circumference of mouth openings. CAST analysis of over 100 proteins has been carried out; proteins examined include a set of 51 monomeric enzyme-ligand structures, several elastase-inhibitor complexes, the FK506 binding protein, 30 HIV-1 protease-inhibitor complexes, and a number of small and large protein inhibitors. Medium-sized globular proteins typically have 10-20 pockets/cavities. Most often, binding sites are pockets with 1-2 mouth openings; much less frequently they are cavities. Ligand binding pockets vary widely in size, most within the range 10(2)-10(3)A3. Statistical analysis reveals that the number of pockets and cavities is correlated with protein size, but there is no correlation between the size of the protein and the size of binding sites. Most frequently, the largest pocket/cavity is the active site, but there are a number of instructive exceptions. Ligand volume and binding site volume are somewhat correlated when binding site volume is < or =700 A3, but the ligand seldom occupies the entire site. Auxiliary pockets near the active site have been suggested as additional binding surface for designed ligands (Mattos C et al., 1994, Nat Struct Biol 1:55-58). Analysis of elastase-inhibitor complexes suggests that CAST can identify ancillary pockets suitable for recruitment in ligand design strategies. Analysis of the FK506 binding protein, and of compounds developed in SAR by NMR (Shuker SB et al., 1996, Science 274:1531-1534), indicates that CAST pocket computation may provide a priori identification of target proteins for linked-fragment design. CAST analysis of 30 HIV-1 protease-inhibitor complexes shows that the flexible active site pocket can vary over a range of 853-1,566 A3, and that there are two pockets near or adjoining the active site that may be recruited for ligand design.     

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.     

Heme proteins—Diversity in structural characteristics,function, and folding

The characteristics of heme prosthetic groups and their binding sites have been analyzed in detail in a data set of nonhomologous heme proteins. Variations in the shape, volume, and chemical composition of the binding site, in the mode of heme binding and in the number and nature of heme–protein interactions are found to result in significantly different heme environments in proteins with different functions in biology. Differences are also seen in the properties of the apo states of the proteins. The apo states of proteins that bind heme permanently in their functional form show some disorder, ranging from local unfolding in the heme binding pocket to complete unfolding to give a random coil. In contrast, proteins that bind heme transiently are fully folded in their apo and holo states, presumably allowing both apo and holo forms to remain biologically active resisting aggregation or proteolysis. The principles identified here provide a framework for the design of de novo proteins that will exhibit tight heme ligand binding and for the identification of the function of structural genomic target proteins with heme ligands. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Combining genetic and biochemical approaches to identify functional molecular contact points

Protein-protein interactions are required for many viral and cellular functions and are potential targets for novel therapies. Here we detail a series of genetic and biochemical techniques used in combination to find an essential molecular contact point on the duck hepatitis B virus polymerase. These techniques include differential immunoprecipitation, mutagenesis and peptide competition. The strength of these techniques is their ability to identify contact points on intact proteins or protein complexes employing functional assays. This approach can be used to aid identification of putative binding sites on proteins and protein complexes which are resistant to characterization by other methods.     

DynaBiS: A hierarchical sampling algorithm to identify flexible binding sites for large ligands and peptides

Knowing the ligand or peptide binding site in proteins is highly important to guide drug discovery, but experimental elucidation of the binding site is difficult. Therefore, various computational approaches have been developed to identify potential binding sites in protein structures. However, protein and ligand flexibility are often neglected in these methods due to efficiency considerations despite the recognition that protein–ligand interactions can be strongly affected by mutual structural adaptations. This is particularly true if the binding site is unknown, as the screening will typically be performed based on an unbound protein structure. Herein we present DynaBiS, a hierarchical sampling algorithm to identify flexible binding sites for a target ligand with explicit consideration of protein and ligand flexibility, inspired by our previously presented flexible docking algorithm DynaDock. DynaBiS applies soft-core potentials between the ligand and the protein, thereby allowing a certain protein–ligand overlap resulting in efficient sampling of conformational adaptation effects. We evaluated DynaBiS and other commonly used binding site identification algorithms against a diverse evaluation set consisting of 26 proteins featuring peptide as well as small ligand binding sites. We show that DynaBiS outperforms the other evaluated methods for the identification of protein binding sites for large and highly flexible ligands such as peptides, both with a holo or apo structure used as input.     

Characterization of the photoproducts of protoporphyrin IX bound to human serum albumin and immunoglobulin G

Clinically useful photosensitisers (PSs) are likely bound to subcellular and molecular targets during phototherapy. Binding to a macromolecule has the potential to change the photophysical and photochemical characteristics of the PSs that are crucial for their phototoxicity and cell-killing activity. We investigated the effects of binding of a specific PS (protoporphyrin IX or PPIX) to two proteins, human serum albumin (HSA) and a commercially available immunoglobulin (IgG). These two proteins provide two different environments for PPIX. The albumin binds PPIX in hydrophobic binding sites located in subdomain IIA and IIIA, conversely IgG leaves PPIX exposed to the solvent. We show that photophysical parameters such as emission maxima and fluorescence lifetime depend on the binding site. Our results indicate that the different binding site yields very different rates of formation of photoproducts (more than three times higher for PPIX bound to HSA than to IgG) and that different mechanisms of formation may be occurring. Our characterization shows the relevance of protein binding for the photochemistry and ultimately the phototoxicity of PSs.     

Identification of the ligand binding sites on the molecular surface of proteins

Identification of protein biochemical functions based on their three-dimensional structures is now required in the post-genome-sequencing era. Ligand binding is one of the major biochemical functions of proteins, and thus the identification of ligands and their binding sites is the starting point for the function identification. Previously we reported our first trial on structure-based function prediction, based on the similarity searches of molecular surfaces against the functional site database. Here we describe the extension of our first trial by expanding the search database to whole heteroatom binding sites appearing within the Protein Data Bank (PDB) with the new analysis protocol. In addition, we have determined the similarity threshold line, by using 10 structure pairs with solved free and complex structures. Finally, we extensively applied our method to newly determined hypothetical proteins, including some without annotations, and evaluated the performance of our methods.     

Contribution of Ena/VASP proteins to intracellular motility of listeria requires phosphorylation and proline-rich core but not F-actin binding or multimerization

The Listeria model system has been essential for the identification and characterization of key regulators of the actin cytoskeleton such as the Arp2/3 complex and Ena/vasodilator-stimulated phosphoprotein (VASP) proteins. Although the role of Ena/VASP proteins in Listeria motility has been extensively studied, little is known about the contributions of their domains and phosphorylation state to bacterial motility. To address these issues, we have generated a panel of Ena/VASP mutants and, upon expression in Ena/VASP-deficient cells, evaluated their contribution to Ena/VASP function in Listeria motility. The proline-rich region, the putative G-actin binding site, and the Ser/Thr phosphorylation of Ena/VASP proteins are all required for efficient Listeria motility. Surprisingly, the interaction of Ena/VASP proteins with F-actin and their potential ability to form multimers are both dispensable for their involvement in this process. Our data suggest that Ena/VASP proteins contribute to Listeria motility by regulating both the nucleation and elongation of actin filaments at the bacterial surface.     

Tools for phospho- and glycoproteomics of plasma membranes

Analysis of plasma membrane proteins and their posttranslational modifications is considered as important for identification of disease markers and targets for drug treatment. Due to their insolubility in water, studying of plasma membrane proteins using mass spectrometry has been difficult for a long time. Recent technological developments in sample preparation together with important improvements in mass spectrometric analysis have facilitated analysis of these proteins and their posttranslational modifications. Now, large scale proteomic analyses allow identification of thousands of membrane proteins from minute amounts of sample. Optimized protocols for affinity enrichment of phosphorylated and glycosylated peptides have set new dimensions in the depth of characterization of these posttranslational modifications of plasma membrane proteins. Here, I summarize recent advances in proteomic technology for the characterization of the cell surface proteins and their modifications. In the focus are approaches allowing large scale mapping rather than analytical methods suitable for studying individual proteins or non-complex mixtures.     

Studies on the inference of protein binding regions across fold space based on structural similarities

The emerging picture of a continuous protein fold space highlights the existence of non obvious structural similarities between proteins with apparent different topologies. The identification of structure resemblances across fold space and the analysis of similar recognition regions may be a valuable source of information towards protein structure-based functional characterization. In this work, we use non-sequential structural alignment methods (ns-SAs) to identify structural similarities between protein pairs independently of their SCOP hierarchy, and we calculate the significance of binding region conservation using the interacting residues overlap in the ns-SA. We cluster the binding inferences for each family to distinguish already known family binding regions from putative new ones. Our methodology exploits the enormous amount of data available in the PDB to identify binding region similarities within protein families and to propose putative binding regions. Our results indicate that there is a plethora of structurally common binding regions among proteins, independently of current fold classifications. We obtain a 6- to 8-fold enrichment of novel binding regions, and identify binding inferences for 728 protein families that so far lack binding information in the PDB. We explore binding mode analogies between ligands from commonly clustered binding regions to investigate the utility of our methodology. A comprehensive analysis of the obtained binding inferences may help in the functional characterization of protein recognition and assist rational engineering. The data obtained in this work is available in the download link at www.scowlp.org.     

Ligand based structural studies of the CB1 cannabinoid receptor.

The structural characterization of G-protein coupled receptors (GPCRs) is quite important as these proteins represent a vast number of therapeutic targets involved in drug discovery. However, solving the three-dimensional structure of GPCR has been a significant obstacle in structural biology. A variety of reasons, including their large molecular weight, intricate interhelical packing, as well as their membrane-associated topology, has hindered efforts aimed at their purification. In the absence of pure protein, available in the native conformation, classical methods of structural analysis such as X-ray crystallography and nuclear magnetic resonance spectroscopy cannot be utilized successfully. Alternative methods must therefore be explored to facilitate the structural features involved in drug-receptor interactions. The methods described herein detail the use of covalent probes, or affinity labels, capable of binding covalently to a target GPCR at its binding site(s). Our approach involves the incorporation of a number of reactive moieties in different regions of the ligand molecule each of which is expected to react with different amino acid residues. Information obtained from such work coupled with computer modeling and validated by the use of site-directed mutagenesis of GPCRs allows for three-dimensional mapping of the receptor binding site. It also sheds light on the different possible binding motifs for the various classes of agonists and antagonists and identifies amino acid residues involved with GPCR activation or inactivation.     

蛋白质间相互作用技术的研究近况

蛋白质间相互作用技术的研究近况黄翠芬叶棋浓（军事医学科学院生物工程研究所,北京１００８５０关键词：蛋白质,相互作用,技术ＲｅｃｅｎｔＡｄｖａｎｃｅｓｉｎｔｈｅＴｅｃｈｎｉｑｕｅｓｏｆＰｒｏｔｅｉｎ┐ＰｒｏｔｅｉｎＩｎｔｅｒａｃｔｉｏｎｓＨｕａｎｇＣｕ...     

Genome‐wide computational determination of the human metalloproteome

Accurate prediction of protein function in humans is important for understanding biological processes at the molecular level in biomedicine and drug design. Over a third of proteins are commonly held to bind metal, and ～10% of human proteins, to bind zinc. Therefore, an initial step in protein function prediction frequently involves predicting metal ion binding. In recent years, methods have been developed to predict a set of residues in 3D space forming the metal‐ion binding site, often with a high degree of accuracy. Here, using extensions of these methods, we provide an extensive list of human proteins and their putative metal ion binding site residues, using translated gene sequences derived from the complete, resolved human genome. Under conditions of ～90% selectivity, over 900 new human putative metal ion binding proteins are identified. A statistical analysis of resolved metal ion binding sites in the human metalloproteome is furnished and the importance of remote homology analysis is demonstrated. As an example, a novel metal‐ion binding site involving a complex of a botulinum substrate with its inhibitor is presented. On the basis of the location of the predicted site and the interactions of the contacting residues at the complex interface, we postulate that metal ion binding in this region could influence complex formation and, consequently, the functioning of the protein. Thus, this work provides testable hypotheses about novel functions of known proteins. Proteins 2015; 83:931–939. © 2015 Wiley Periodicals, Inc.     

Rapid identification of recombinant Fabs that bind to membrane proteins

Crystallographic studies of membrane proteins have been steadily increasing despite their unique physical properties that hinder crystal formation. Co-crystallization with antibody fragments has emerged as a promising solution to obtain diffraction quality crystals. Antibody binding to the target membrane protein can yield a homogenous population of the protein. Interantibody interactions can also provide additional crystal contacts, which are minimized in membrane proteins due to micelle formation around the transmembrane segments. Rapid identification of antibody fragments that can recognize native protein structure makes phage display a valuable method for crystallographic studies of membrane proteins. Methods that speed the reliable characterization of phage display selected antibody fragments are needed to make the technology more generally applicable. In this report, a phage display biopanning procedure is described to identify Fragments antigen binding (Fabs) for membrane proteins. It is also demonstrated that Fabs can be rapidly grouped based on relative affinities using enzyme linked immunosorbent assay (ELISA) and unpurified Fabs. This procedure greatly speeds the prioritization of candidate binders to membrane proteins and will aid in subsequent structure determinations.