Parallel multi-swarm cooperative particle swarm optimization for protein–ligand docking and virtual screening

Chao Li, Jinxing Li, Jun Sun, Li Mao, Vasile Palade, Bilal Ahmad

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Abstract

Background:
A high-quality docking method tends to yield multifold gains with half pains for the new drug development. Over the past few decades, great efforts have been made for the development of novel docking programs with great efficiency and intriguing accuracy. AutoDock Vina (Vina) is one of these achievements with improved speed and accuracy compared to AutoDock4. Since it was proposed, some of its variants, such as PSOVina and GWOVina, have also been developed. However, for all these docking programs, there is still large room for performance improvement.

Results:
In this work, we propose a parallel multi-swarm cooperative particle swarm model, in which one master swarm and several slave swarms mutually cooperate and co-evolve. Our experiments show that multi-swarm programs possess better docking robustness than PSOVina. Moreover, the multi-swarm program based on random drift PSO can achieve the best highest accuracy of protein–ligand docking, an outstanding enrichment effect for drug-like activate compounds, and the second best AUC screening accuracy among all the compared docking programs, but with less computation consumption than most of the other docking programs.

Conclusion:
The proposed multi-swarm cooperative model is a novel algorithmic modeling suitable for protein–ligand docking and virtual screening. Owing to the existing coevolution between the master and the slave swarms, this model in parallel generates remarkable docking performance. The source code can be freely downloaded from https://github.com/li-jin-xing/MPSOVina.
Original languageEnglish
Article number201
Number of pages17
JournalBMC Bioinformatics
Volume23
Issue number1
DOIs
Publication statusPublished - 30 May 2022

Bibliographical note

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Funder

This work was supported in part by the National Natural Science Foundation of China (Projects Numbers: 61673194, 61672263, 61672265) and in part by the national first-class discipline program of Light Industry Technology and Engineering (Project Number: LITE2018-25). The funding agencies did not have any role in the design, collection, analysis or interpretation of the data or writing of the manuscript

Keywords

  • Autodock Vina
  • Protein–ligand docking
  • Random drift particle swarm optimization
  • Virtual screening

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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