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Two-Step Covalent Docking with Attracting Cavities.

Mathilde Goullieux1, Vincent Zoete1,2, Ute F Röhrig1

  • 1SIB Swiss Institute of Bioinformatics, Molecular Modeling Group, CH-1015 Lausanne, Switzerland.

Journal of Chemical Information and Modeling
|December 4, 2023
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Summary
This summary is machine-generated.

A new covalent docking procedure, Attracting Cavities (AC), accurately predicts drug interactions. AC outperforms existing methods like GOLD and AutoDock in modeling covalent drug binding mechanisms.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Structural biology

Background:

  • Covalent drugs offer advantages, increasing interest in their development.
  • Accurate prediction of drug-target interactions is crucial for computer-aided drug design.
  • Existing docking algorithms require refinement for covalent ligand binding.

Purpose of the Study:

  • To develop and validate a novel covalent docking procedure for the Attracting Cavities (AC) code.
  • To mimic the two-step binding mechanism of covalent ligands.
  • To assess the performance of AC against established docking tools.

Main Methods:

  • Developed a covalent docking procedure for the AC code, simulating nonbonded interactions followed by covalent bond formation.
  • Created a benchmark set of 304 covalent complexes for re-docking and cross-docking validation.
  • Compared AC's success rates with GOLD and AutoDock using RMSD criteria of ≤ 2 Å and ≤ 1.5 Å.

Main Results:

  • AC achieved a 78% success rate (RMSD ≤ 2 Å) on the benchmark set, outperforming GOLD (66%) and AutoDock (35%).
  • Using a stricter criterion (RMSD ≤ 1.5 Å), AC reached 71% success, compared to 55% for GOLD and 26% for AD.
  • On SARS-CoV-2 main protease complexes, AC showed 58% re-docking and 28% cross-docking success rates.

Conclusions:

  • The AC covalent docking procedure accurately models covalent ligand binding.
  • AC demonstrates superior performance compared to GOLD and AutoDock for covalent docking.
  • This method is a valuable tool for advancing computer-aided drug design of covalent inhibitors.