Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

Virtual screening for NS5B inhibitors of Hepatitis C virus

Hepatitis C Virus (HCV) infection is a serious cause of chronic liver disease worldwide with more than 170 million infected individuals at a risk of developing significant morbidity and mortality. To date there is no effective drug for the treatment or vaccine to prevent this infection. The present study aims to discover novel inhibitors which target an allosteric binding site of RNA dependent RNA polymerase enzyme of HCV. A structure based virtual screening of Zinc database by computational docking and the post docking analysis of energy calculations and interactions followed by ADMET studies were conducted. Our study revealed 10 compounds which has more potential than the existing inhibitor to be considered as lead compounds.
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Study of the Differential Activity of Thrombin Inhibitors Using Docking, QSAR, Molecular Dynamics, and MM-GBSA

Indexación: Web of Science; Scopus.Non-peptidic thrombin inhibitors (TIs; 177 compounds) with diverse groups at motifs P1 (such as oxyguanidine, amidinohydrazone, amidine, amidinopiperidine), P2 (such as cyano-fluorophenylacetamide, 2-(2-chloro-6-fluorophenyl)acetamide), and P3 (such as phenylethyl, arylsulfonate groups) were studied using molecular modeling to analyze their interactions with S1, S2, and S3 subsites of the thrombin binding site. Firstly, a protocol combining docking and three dimensional quantitative structure-activity relationship was performed. We described the orientations and preferred active conformations of the studied inhibitors, and derived a predictive CoMSIA model including steric, donor hydrogen bond, and acceptor hydrogen bond fields. Secondly, the dynamic behaviors of some selected TIs (compounds 26, 133, 147, 149, 162, and 177 in this manuscript) that contain different molecular features and different activities were analyzed by creating the solvated models and using molecular dynamics (MD) simulations.We used the conformational structures derived from MD to accomplish binding free energetic calculations using MM-GBSA. With this analysis, we theorized about the effect of van der Waals contacts, electrostatic interactions and solvation in the potency of TIs. In general, the contents reported in this article help to understand the physical and chemical characteristics of thrombin-inhibitor complexes. © 2015 Mena-Ulecia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.http://journals.plos.org/plosone/article?id=10.1371/journal.pone.014277

Propofol inhibits the voltage-gated sodium channel NaChBac at multiple sites.

Voltage-gated sodium (NaV) channels are important targets of general anesthetics, including the intravenous anesthetic propofol. Electrophysiology studies on the prokaryotic NaV channel NaChBac have demonstrated that propofol promotes channel activation and accelerates activation-coupled inactivation, but the molecular mechanisms of these effects are unclear. Here, guided by computational docking and molecular dynamics simulations, we predict several propofol-binding sites in NaChBac. We then strategically place small fluorinated probes at these putative binding sites and experimentally quantify the interaction strengths with a fluorinated propofol analogue, 4-fluoropropofol. In vitro and in vivo measurements show that 4-fluoropropofol and propofol have similar effects on NaChBac function and nearly identical anesthetizing effects on tadpole mobility. Using quantitative analysis by 19F-NMR saturation transfer difference spectroscopy, we reveal strong intermolecular cross-relaxation rate constants between 4-fluoropropofol and four different regions of NaChBac, including the activation gate and selectivity filter in the pore, the voltage sensing domain, and the S4-S5 linker. Unlike volatile anesthetics, 4-fluoropropofol does not bind to the extracellular interface of the pore domain. Collectively, our results show that propofol inhibits NaChBac at multiple sites, likely with distinct modes of action. This study provides a molecular basis for understanding the net inhibitory action of propofol on NaV channels. © 2018 Wang et al

Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose.

We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. The method is closely related to the QMOD approach, substituting a learned scoring field for a pocket constructed of molecular fragments. The problem of mutual ligand alignment is addressed in a general way, and optimal model parameters and ligand poses are identified through multiple-instance machine learning. We provide algorithmic details along with performance results on sixteen structure-activity data sets covering many pharmaceutically relevant targets. In particular, we show how models initially induced from small data sets can extrapolatively identify potent new ligands with novel underlying scaffolds with very high specificity. Further, we show that combining predictions from QuanSA models with those from physics-based simulation approaches is synergistic. QuanSA predictions yield binding affinities, explicit estimates of ligand strain, associated ligand pose families, and estimates of structural novelty and confidence. The method is applicable for fine-grained lead optimization as well as potent new lead identification

New insights on repellent recognition by <i>Anopheles gambiae</i> odorant-binding protein 1

It is generally recognized that insect odorant binding proteins (OBPs) mediate the solubilisation and transport of hydrophobic odorant molecules and contribute to the sensitivity of the insect olfactory system. However, the exact mechanism by which OBPs deliver odorants to olfactory receptors and their role, if any, as selectivity filters for specific odorants, are still a matter of debate. In the case of Anopheles gambiae recent studies indicate that ligand discrimination is effected through the formation of heterodimers such as AgamOBP1 and AgamOBP4 (odorant binding proteins 1 and 4 from Anopheles gambiae). Furthermore, AgamOBPs have been reported to be promiscuous in binding more than one ligand simultaneously and repellents such as DEET (N,N-diethyl-3-toluamide) and 6-MH (6-methyl-5-hepten-2-one) interact directly with mosquito OBPs and/or compete for the binding of attractive odorants thus disrupting OBP heterodimerisation. In this paper, we propose mechanisms of action of DEET and 6-MH. We also predict that ligand binding can occur in several locations of AgamOBP1 with partial occupancies and propose structural features appropriate for repellent pharmacophores