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

Renata De Paris1, Christian Vahl Quevedo1, Duncan D Ruiz2

  • 1Business Intelligence and Machine Learning Research Group-GPIN, School of Technology, PUCRS, Av. Ipiranga, 6681, Building 32, Room 628, Porto Alegre, RS, Brazil.

BMC Bioinformatics
|June 23, 2018
PubMed
Summary
This summary is machine-generated.

This study presents a novel method to optimize ensemble docking by reducing receptor model size, significantly cutting computational costs and time while maintaining high accuracy for drug discovery. The approach enhances ligand binding predictions and identifies new binding modes.

Keywords:
Cloud computingFully-Flexible receptor modelMolecular dockingScientific workflow

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Area of Science:

  • Computational chemistry
  • Molecular modeling
  • Drug discovery

Background:

  • Ensemble conformations from molecular dynamics are vital for rational drug design.
  • Fully-Flexible Receptor (FFR) models improve binding energy prediction and selectivity.
  • High computational costs of explicit protein flexibility in docking are a challenge.

Purpose of the Study:

  • To introduce a novel method for optimizing ensemble docking experiments.
  • To reduce the size of InhA FFR models at docking runtime.
  • To scale docking workflows on cloud virtual machines for efficiency.

Main Methods:

  • Evaluated docking workflow performance across different Azure virtual machine configurations.
  • Validated the Reduced Fully-Flexible Receptor (RFFR) model quality using AutoDock4.2.
  • Tested the novel method with AutoDock Vina to assess cross-software applicability.

Main Results:

  • Reduced model size by ~50% while retaining >=86% of top docking results for 74 ligands.
  • Demonstrated positive accuracy of RFFR models with AutoDock Vina.
  • Showcased optimized ensemble docking applicable to different software.

Conclusions:

  • The selective RFFR method optimizes ensemble docking experiments efficiently.
  • RFFR models accurately represent diverse ligands and reduce experiment time.
  • The method enables detection of novel binding modes missed by rigid models.