A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model.

ABSTRACT:

Background

In 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.

Results

First, in order to find the most affordable cost-benefit pool of vi

SUBMITTER: De Paris R 

PROVIDER: S-EPMC6013854 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature