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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Evaluation of AlphaFold2 structures as docking targets.

Matthew Holcomb1, Ya-Ting Chang1, David S Goodsell1,2,3,4

  • 1Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA.

Protein Science : a Publication of the Protein Society
|December 8, 2022
PubMed
Summary
This summary is machine-generated.

AlphaFold2 protein structure predictions show high backbone accuracy but require refinement for effective molecular docking. Adjusting low-confidence regions and side-chain flexibility improves docking performance.

Keywords:
AlphaFold2AutoDock protein structure predictioncomputational dockingcomputer-aided drug designdrug design and developmentvirtual screening

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Area of Science:

  • Structural Biology
  • Computational Chemistry
  • Drug Discovery

Background:

  • AlphaFold2 accurately predicts protein structures, achieving low RMSD compared to experimental data.
  • The utility of AlphaFold2 models as targets for molecular docking remains largely unexplored.

Purpose of the Study:

  • To evaluate the effectiveness of AlphaFold2 predicted protein structures as targets for molecular docking.
  • To identify strategies for improving docking performance using AlphaFold2 models.

Main Methods:

  • Ligand docking was performed using AutoDock-GPU against experimental structures and AlphaFold2 models from the PDBbind dataset.
  • The quality measure from AlphaFold2 structure prediction was assessed for its correlation with docking success.
  • Modifications to AlphaFold2 models, including removal of low-confidence regions and side-chain flexibility, were tested.

Main Results:

  • AlphaFold2's internal quality measure did not reliably predict docking performance.
  • Docking outcomes were significantly improved by removing low-confidence regions in AlphaFold2 models.
  • Allowing side chains to be flexible during docking also enhanced results.

Conclusions:

  • While AlphaFold2 provides high-quality backbone predictions, fine structural details can impede direct use in molecular docking.
  • Optimizing AlphaFold2 models by addressing low-confidence areas and side-chain flexibility is crucial for successful docking applications.