33,553 research outputs found

    Computational structure‐based drug design: Predicting target flexibility

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    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

    Inclusion of Enclosed Hydration Effects in the Binding Free Energy Estimation of Dopamine D3 Receptor Complexes

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    Confined hydration and conformational flexibility are some of the challenges encountered for the rational design of selective antagonists of G-protein coupled receptors. We present a set of C3-substituted (-)-stepholidine derivatives as potent binders of the dopamine D3 receptor. The compounds are characterized biochemically, as well as by computer modeling using a novel molecular dynamics-based alchemical binding free energy approach which incorporates the effect of the displacement of enclosed water molecules from the binding site. The free energy of displacement of specific hydration sites is obtained using the Hydration Site Analysis method with explicit solvation. This work underscores the critical role of confined hydration and conformational reorganization in the molecular recognition mechanism of dopamine receptors and illustrates the potential of binding free energy models to represent these key phenomena.Comment: This is the first report of using enclosed hydration in estimating binding free energies of protein-ligand complexes using implicit solvatio

    Identifying Ligand Binding Conformations of the β2-Adrenergic Receptor by Using Its Agonists as Computational Probes

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    Recently available G-protein coupled receptor (GPCR) structures and biophysical studies suggest that the difference between the effects of various agonists and antagonists cannot be explained by single structures alone, but rather that the conformational ensembles of the proteins need to be considered. Here we use an elastic network model-guided molecular dynamics simulation protocol to generate an ensemble of conformers of a prototypical GPCR, β2-adrenergic receptor (β2AR). The resulting conformers are clustered into groups based on the conformations of the ligand binding site, and distinct conformers from each group are assessed for their binding to known agonists of β2AR. We show that the select ligands bind preferentially to different predicted conformers of β2AR, and identify a role of β2AR extracellular region as an allosteric binding site for larger drugs such as salmeterol. Thus, drugs and ligands can be used as "computational probes" to systematically identify protein conformers with likely biological significance. © 2012 Isin et al

    Consensus virtual screening approaches to predict protein ligands

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    In order to exploit the advantages of receptor-based virtual screening, namely time/cost saving and specificity, it is important to rely on algorithms that predict a high number of active ligands at the top ranks of a small molecule database. Towards that goal consensus methods combining the results of several docking algorithms were developed and compared against the individual algorithms. Furthermore, a recently proposed rescoring method based on drug efficiency indices was evaluated. Among AutoDock Vina 1.0, AutoDock 4.2 and GemDock, AutoDock Vina was the best performing single method in predicting high affinity ligands from a database of known ligands and decoys. The rescoring of predicted binding energies with the water/butanol partition coeffcient did not lead to an improvement averaged over all receptor targets. Various consensus algorithms were investigated and a simple combination of AutoDock and AutoDock Vina results gave the most consistent performance that showed early enrichment of known ligands for all receptor targets investigated. In case a number ligands is known for a specific target, every method proposed in this study should be evaluated

    DockPro: A VR-Based Tool for Protein-Protein Docking Problem

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    Proteins are large molecules that are vital for all living organisms and they are essential components of many industrial products. The process of binding a protein to another is called protein-protein docking. Many automated algorithms have been proposed to find docking configurations that might yield promising protein-protein complexes. However, these automated methods are likely to come up with false positives and have high computational costs. Consequently, Virtual Reality has been used to take advantage of user's experience on the problem; and proposed applications can be further improved. Haptic devices have been used for molecular docking problems; but they are inappropriate for protein-protein docking due to their workspace limitations. Instead of haptic rendering of forces, we provide a novel visual feedback for simulating physicochemical forces of proteins. We propose an interactive 3D application, DockPro, which enables domain experts to come up with dockings of protein-protein couples by using magnetic trackers and gloves in front of a large display
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