Abstract
BackgroundIn the rational drug design process, an ensemble of conformations obtained from a molecular dynamics simulation plays a crucial role in docking experiments. Some studies have found that Fully-Flexible Receptor (FFR) models predict realistic binding energy accurately and improve scoring to enhance selectiveness. At the same time, methods have been proposed to reduce the high computational costs involved in considering the explicit flexibility of proteins in receptor-ligand docking. This study introduces a novel method to optimize ensemble docking-based experiments by reducing the size of an InhA FFR model at docking runtime and scaling docking workflow invocations on cloud virtual machines.ResultsFirst, in order to find the most affordable cost-benefit pool of virtual machines, we evaluated the performance of the docking workflow invocations in different configurations of Azure instances. Second, we validated the gains obtained by the proposed method based on the quality of the Reduced Fully-Flexible Receptor (RFFR) models produced using AutoDock4.2. The analyses show that the proposed method reduced the model size by approximately 50% while covering at least 86% of the best docking results from the 74 ligands tested. Third, we tested our novel method using AutoDock Vina, a different docking software, and showed the positive accuracy achieved in the resulting RFFR models. Finally, our results demonstrated that the method proposed optimized ensemble docking experiments and is applicable to different docking software. In addition, it detected new binding modes, which would be unreachable if employing only the rigid structure used to generate the InhA FFR model.ConclusionsOur results showed that the selective method is a valuable strategy for optimizing ensemble docking-based experiments using different docking software. The RFFR models produced by discarding non-promising snapshots from the original model are accurately shaped for a larger number of ligands, and the elapsed time spent in the ensemble docking experiments are considerably reduced.
Highlights
In the rational drug design process, an ensemble of conformations obtained from a molecular dynamics simulation plays a crucial role in docking experiments
We show that Reduced Fully-Flexible Receptor (RFFR) models can be generated by identifying promising Molecular Dynamics (MD) conformations to the ligands during the docking experiments without previous assessments about the best free energy of binding or any other evaluation associated with ligand binding quality
This paper describes the implementation of the proposed method in the e-FReDock workflow [35] and evaluates its results by assessing the quality of the RFFR models produced
Summary
In the rational drug design process, an ensemble of conformations obtained from a molecular dynamics simulation plays a crucial role in docking experiments. Methods have been proposed to reduce the high computational costs involved in considering the explicit flexibility of proteins in receptor-ligand docking. De Paris et al BMC Bioinformatics (2018) 19:235 optimize drug candidates because of its ability to quickly screen large libraries of potential leads for identifying native-like poses and filtering out compounds that are likely nonbinders [6, 7]. It has been widely used in pharmaceutical design since structure-based virtual screening has shown to be more economic than experimental screening [7]. Proteins are very versatile and their flexibility cannot be a priori neglected since it plays an essential role in their structure and function [12, 13]
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