Identification of protein-ligand binding site using multi-clustering and support vector machine

© 2016 IEEE. Multi-clustering has been widely used. It acts as a pre-training process for identifying protein-ligand binding in structure-based drug design. Then, the Support Vector Machine (SVM) is employed to classify the sites most likely for binding ligands. Three types of attributes are used, namely geometry-based, energy-based, and sequence conservation. Comparison is made on 198 drug-target protein complexes with LIGSITECSC, SURFNET, Fpocket, Q-SiteFinder, ConCavity, and MetaPocket. The results show an improved success rate of up to 86%

Computational approaches to predict protein functional families and functional sites.

Understanding the mechanisms of protein function is indispensable for many biological applications, such as protein engineering and drug design. However, experimental annotations are sparse, and therefore, theoretical strategies are needed to fill the gap. Here, we present the latest developments in building functional subclassifications of protein superfamilies and using evolutionary conservation to detect functional determinants, for example, catalytic-, binding- and specificity-determining residues important for delineating the functional families. We also briefly review other features exploited for functional site detection and new machine learning strategies for combining multiple features

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

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

Virtual screening for inhibitors of the human TSLP:TSLPR interaction

The pro-inflammatory cytokine thymic stromal lymphopoietin (TSLP) plays a pivotal role in the pathophysiology of various allergy disorders that are mediated by type 2 helper T cell (Th2) responses, such as asthma and atopic dermatitis. TSLP forms a ternary complex with the TSLP receptor (TSLPR) and the interleukin-7-receptor subunit alpha (IL-7Ra), thereby activating a signaling cascade that culminates in the release of pro-inflammatory mediators. In this study, we conducted an in silico characterization of the TSLP: TSLPR complex to investigate the drugability of this complex. Two commercially available fragment libraries were screened computationally for possible inhibitors and a selection of fragments was subsequently tested in vitro. The screening setup consisted of two orthogonal assays measuring TSLP binding to TSLPR: a BLI-based assay and a biochemical assay based on a TSLP: alkaline phosphatase fusion protein. Four fragments pertaining to diverse chemical classes were identified to reduce TSLP: TSLPR complex formation to less than 75% in millimolar concentrations. We have used unbiased molecular dynamics simulations to develop a Markov state model that characterized the binding pathway of the most interesting compound. This work provides a proof-ofprinciple for use of fragments in the inhibition of TSLP: TSLPR complexation

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

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