Abstract
In current computational biology, docking is a popular tool used to find the best fit of one ligand relative to its molecular receptor in forming a complex. However, the most of the tools do not take into account the flexibility of the receptor due to computational cost. As a result, the conformational changes caused by the induced fit are ignored in exploratory docking experiments. In this context and to improve the predictive capacity of docking, a good strategy is to simulate the flexibility of the receptor with the use of key conformations that can be obtained by mixing crystallography and computer simulations, a technique known as ensemble docking. Here, we present MDR SurFlexDock, a web tool that improves the docking experiments by computing a discrete, but representative, ensemble of contact surfaces of the receptor through clustering of molecular simulation trajectories in order to simulate the intrinsic flexibility of the ligand-contacting surface. The results of the interaction of each receptor-compound complex are presented in a concise tabular format to allow rapid analysis of compounds when classifying them by inhibition constant (Ki). MDR SurFlexDock can be valuable in cases of docking for new receptors obtained by homology modelling, in extensive analysis of different chemotypes on proteins with low structural information and for fast characterization of binding capacities on contact surfaces with poor structural information or only optimized for a specific ligand. MDR SurFlexDock is freely available as a web service at http://biocomp.uenf.br:81.
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Funding
This work has been supported by the Conselho Nacional de Desenvol-vimento Científico e Tecnológico (CNPq) doctoral grant [141917/2015-6] for J.L.A.F. and ProAP-CAPES support from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), which are gratefully acknowledged.
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de Almeida Filho, J.L., Fernandez, J.H. (2020). MDR SurFlexDock: A Semi-automatic Webserver for Discrete Receptor-Ensemble Docking. In: Kowada, L., de Oliveira, D. (eds) Advances in Bioinformatics and Computational Biology. BSB 2019. Lecture Notes in Computer Science(), vol 11347. Springer, Cham. https://doi.org/10.1007/978-3-030-46417-2_4
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DOI: https://doi.org/10.1007/978-3-030-46417-2_4
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