eModel-BDB: a database of comparative structure models of drug-target interactions from the Binding Database

Background: The structural information on proteins in their ligand-bound conformational state is invaluable for protein function studies and rational drug design. Compared to the number of available sequences, not only is the repertoire of the experimentally determined structures of holo-proteins limited, these structures do not always include pharmacologically relevant compounds at their binding sites. In addition, binding affinity databases provide vast quantities of information on interactions between drug-like molecules and their targets, however, often lacking structural data. On that account, there is a need for computational methods to complement existing repositories by constructing the atomic-level models of drug-protein assemblies that will not be determined experimentally in the near future. Results: We created eModel-BDB, a database of  200,005 comparative models of drug-bound proteins based on   1,391,403 interaction data obtained from the Binding Database and the PDB library of 31 January 2017. Complex models in eModel-BDB were generated with a collection of the state-of-the-art techniques, including protein meta-threading, template-based structure modeling, refinement and binding site detection, and ligand similarity-based docking. In addition to a rigorous quality control maintained during dataset generation, a subset of weakly homologous models was selected for the retrospective validation against experimental structural data recently deposited to the Protein Data Bank. Validation results indicate that eModel-BDB contains models that are accurate not only at the global protein structure level but also with respect to the atomic details of bound ligands. Conclusions: Freely available eModel-BDB can be used to support structure-based drug discovery and repositioning, drug target identification, and protein structure determination

eRepo-ORP: Exploring the Opportunity Space to Combat Orphan Diseases with Existing Drugs

© 2017 About 7000 rare, or orphan, diseases affect more than 350 million people worldwide. Although these conditions collectively pose significant health care problems, drug companies seldom develop drugs for orphan diseases due to extremely limited individual markets. Consequently, developing new treatments for often life-threatening orphan diseases is primarily contingent on financial incentives from governments, special research grants, and private philanthropy. Computer-aided drug repositioning is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Here, we present eRepo-ORP, a comprehensive resource constructed by a large-scale repositioning of existing drugs to orphan diseases with a collection of structural bioinformatics tools, including eThread, eFindSite, and eMatchSite. Specifically, a systematic exploration of 320,856 possible links between known drugs in DrugBank and orphan proteins obtained from Orphanet reveals as many as 18,145 candidates for repurposing. In order to illustrate how potential therapeutics for rare diseases can be identified with eRepo-ORP, we discuss the repositioning of a kinase inhibitor for Ras-associated autoimmune leukoproliferative disease. The eRepo-ORP data set is available through the Open Science Framework at https://osf.io/qdjup/

Comparative Analysis of Species-Specific Ligand Recognition in Toll-Like Receptor 8 Signaling: A Hypothesis

Toll-like receptors (TLRs) play a central role in the innate immune response by recognizing conserved structural patterns in a variety of microbes. TLRs are classified into six families, of which TLR7 family members include TLR7, 8, and 9, which are localized to endolysosomal compartments recognizing viral infection in the form of foreign nucleic acids. In our current study, we focused on TLR8, which has been shown to recognize different types of ligands such as viral or bacterial ssRNA as well as small synthetic molecules. The primary sequences of rodent and non-rodent TLR8s are similar, but the antiviral compound (R848) that activates the TLR8 pathway is species-specific. Moreover, the factors underlying the receptor's species-specificity remain unknown. To this end, comparative homology modeling, molecular dynamics simulations refinement, automated docking and computational mutagenesis studies were employed to probe the intermolecular interactions between this anti-viral compound and TLR8. Furthermore, comparative analyses of modeled TLR8 (rodent and non-rodent) structures have shown that the variation mainly occurs at LRR14-15 (undefined region); hence, we hypothesized that this variation may be the primary reason for the exhibited species-specificity. Our hypothesis was further bolstered by our docking studies, which clearly showed that this undefined region was in close proximity to the ligand-binding site and thus may play a key role in ligand recognition. In addition, the interface between the ligand and TLR8s varied depending upon the amino acid charges, free energy of binding, and interaction surface. Therefore, our current work provides a hypothesis for previous in vivo studies in the context of TLR signaling

Large-scale computational drug repositioning to find treatments for rare diseases

© 2018, The Author(s). Rare, or orphan, diseases are conditions afflicting a small subset of people in a population. Although these disorders collectively pose significant health care problems, drug companies require government incentives to develop drugs for rare diseases due to extremely limited individual markets. Computer-aided drug repositioning, i.e., finding new indications for existing drugs, is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Structure-based matching of drug-binding pockets is among the most promising computational techniques to inform drug repositioning. In order to find new targets for known drugs ultimately leading to drug repositioning, we recently developed eMatchSite, a new computer program to compare drug-binding sites. In this study, eMatchSite is combined with virtual screening to systematically explore opportunities to reposition known drugs to proteins associated with rare diseases. The effectiveness of this integrated approach is demonstrated for a kinase inhibitor, which is a confirmed candidate for repositioning to synapsin Ia. The resulting dataset comprises 31,142 putative drug-target complexes linked to 980 orphan diseases. The modeling accuracy is evaluated against the structural data recently released for tyrosine-protein kinase HCK. To illustrate how potential therapeutics for rare diseases can be identified, we discuss a possibility to repurpose a steroidal aromatase inhibitor to treat Niemann-Pick disease type C. Overall, the exhaustive exploration of the drug repositioning space exposes new opportunities to combat orphan diseases with existing drugs. DrugBank/Orphanet repositioning data are freely available to research community at https://osf.io/qdjup/

Binding site matching in rational drug design: Algorithms and applications

© 2018 The Author(s) 2018. Published by Oxford University Press. All rights reserved. Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning

Molecular Modeling-Based Evaluation of hTLR10 and Identification of Potential Ligands in Toll-Like Receptor Signaling

Toll-like receptors (TLRs) are pattern recognition receptors that recognize pathogens based on distinct molecular signatures. The human (h)TLR1, 2, 6 and 10 belong to the hTLR1 subfamilies, which are localized in the extracellular regions and activated in response to diverse ligand molecules. Due to the unavailability of the hTLR10 crystal structure, the understanding of its homo and heterodimerization with hTLR2 and hTLR1 and the ligand responsible for its activation is limited. To improve our understanding of the TLR10 receptor-ligand interaction, we used homology modeling to construct a three dimensional (3D) structure of hTLR10 and refined the model through molecular dynamics (MD) simulations. We utilized the optimized structures for the molecular docking in order to identify the potential site of interactions between the homo and heterodimer (hTLR10/2 and hTLR10/1). The docked complexes were then used for interaction with ligands (Pam3CSK4 and PamCysPamSK4) using MOE-Dock and ASEDock. Our docking studies have shown the binding orientations of hTLR10 heterodimer to be similar with other TLR2 family members. However, the binding orientation of hTLR10 homodimer is different from the heterodimer due to the presence of negative charged surfaces at the LRR11-14, thereby providing a specific cavity for ligand binding. Moreover, the multiple protein-ligand docking approach revealed that Pam3CSK4 might be the ligand for the hTLR10/2 complex and PamCysPamSK4, a di-acylated peptide, might activate hTLR10/1 hetero and hTLR10 homodimer. Therefore, the current modeled complexes can be a useful tool for further experimental studies on TLR biology

Comparative assessment of strategies to identify similar ligand-binding pockets in proteins

Abstract Background Detecting similar ligand-binding sites in globally unrelated proteins has a wide range of applications in modern drug discovery, including drug repurposing, the prediction of side effects, and drug-target interactions. Although a number of techniques to compare binding pockets have been developed, this problem still poses significant challenges. Results We evaluate the performance of three algorithms to calculate similarities between ligand-binding sites, APoc, SiteEngine, and G-LoSA. Our assessment considers not only the capabilities to identify similar pockets and to construct accurate local alignments, but also the dependence of these alignments on the sequence order. We point out certain drawbacks of previously compiled datasets, such as the inclusion of structurally similar proteins, leading to an overestimated performance. To address these issues, a rigorous procedure to prepare unbiased, high-quality benchmarking sets is proposed. Further, we conduct a comparative assessment of techniques directly aligning binding pockets to indirect strategies employing structure-based virtual screening with AutoDock Vina and rDock. Conclusions Thorough benchmarks reveal that G-LoSA offers a fairly robust overall performance, whereas the accuracy of APoc and SiteEngine is satisfactory only against easy datasets. Moreover, combining various algorithms into a meta-predictor improves the performance of existing methods to detect similar binding sites in unrelated proteins by 5–10%. All data reported in this paper are freely available at https://osf.io/6ngbs/