54,402 research outputs found

    Characterizing Hydration Properties Based on the Orientational Structure of Interfacial Water Molecules

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    In this manuscript, we present a general computational method for characterizing the molecular structure of liquid water interfaces as sampled from atomistic simulations. With this method, the interfacial structure is quantified based on the statistical analysis of the orientational configurations of interfacial water molecules. The method can be applied to generate position dependent maps of the hydration properties of heterogeneous surfaces. We present an application to the characterization of surface hydrophobicity, which we use to analyze simulations of a hydrated protein. We demonstrate that this approach is capable of revealing microscopic details of the collective dynamics of a protein hydration shell.Comment: 13 pages, 6 figure

    Unique Physicochemical Patterns of Residues in Protein-Protein Interfaces

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    Protein-protein interactions can be characterized by high-resolution structures of complexes, from which diverse features of the interfaces can be derived. For the majority of protein-protein interactions identified, however, there is no information on the structure of the complex or the interface involved in the interaction. Understanding what surface properties drive certain interactions is crucial in the functional evaluation of protein complexes. Here we show that the local patterning of the physicochemical properties of amino acids within surface patches is characteristic of interfaces. To describe this feature in a quantitative manner, we have defined a statistical potential, iPat, as a measure of surface patterning. iPat, which does not take evolutionary conservation or knowledge of the interaction partner into consideration, represents a function principally different from algorithms that consider intermolecular contacts. We assess its suitability for characterizing protein and peptide interfaces, and we demonstrate that iPat is uniquely descriptive for interfaces of proteins that undergo large conformational changes or that are involved in the binding of intrinsically disordered protein (IDP) partners. We suggest that as a stand-alone propensity or in combination with other features, iPat represents a new feature in analyzing the functional binding specificity of protein-protein interactions that has better predictive potential than other simple 1D features, such as hydrophobicity or stickiness

    Specialized dynamical properties of promiscuous residues revealed by simulated conformational ensembles

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    The ability to interact with different partners is one of the most important features in proteins. Proteins that bind a large number of partners (hubs) have been often associated with intrinsic disorder. However, many examples exist of hubs with an ordered structure, and evidence of a general mechanism promoting promiscuity in ordered proteins is still elusive. An intriguing hypothesis is that promiscuous binding sites have specific dynamical properties, distinct from the rest of the interface and pre-existing in the protein isolated state. Here, we present the first comprehensive study of the intrinsic dynamics of promiscuous residues in a large protein data set. Different computational methods, from coarse-grained elastic models to geometry-based sampling methods and to full-atom Molecular Dynamics simulations, were used to generate conformational ensembles for the isolated proteins. The flexibility and dynamic correlations of interface residues with a different degree of binding promiscuity were calculated and compared considering side chain and backbone motions, the latter both on a local and on a global scale. The study revealed that (a) promiscuous residues tend to be more flexible than nonpromiscuous ones, (b) this additional flexibility has a higher degree of organization, and (c) evolutionary conservation and binding promiscuity have opposite effects on intrinsic dynamics. Findings on simulated ensembles were also validated on ensembles of experimental structures extracted from the Protein Data Bank (PDB). Additionally, the low occurrence of single nucleotide polymorphisms observed for promiscuous residues indicated a tendency to preserve binding diversity at these positions. A case study on two ubiquitin-like proteins exemplifies how binding promiscuity in evolutionary related proteins can be modulated by the fine-tuning of the interface dynamics. The interplay between promiscuity and flexibility highlighted here can inspire new directions in protein-protein interaction prediction and design methods. © 2013 American Chemical Society

    Hot-spot analysis for drug discovery targeting protein-protein interactions

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    Introduction: Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.This work has been funded by grants BIO2016-79930-R and SEV-2015-0493 from the Spanish Ministry of Economy, Industry and Competitiveness, and grant EFA086/15 from EU Interreg V POCTEFA. M Rosell is supported by an FPI fellowship from the Severo Ochoa program. The authors are grateful for the support of the the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

    Exploring the potential of 3D Zernike descriptors and SVM for protein\u2013protein interface prediction

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    Abstract Background The correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task. Results In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI). Conclusions The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class
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