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Vinardo: A Scoring Function Based on Autodock Vina Improves Scoring, Docking, and Virtual Screening

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  • Rodrigo Quiroga
  • Marcos A Villarreal

Abstract

Autodock Vina is a very popular, and highly cited, open source docking program. Here we present a scoring function which we call Vinardo (Vina RaDii Optimized). Vinardo is based on Vina, and was trained through a novel approach, on state of the art datasets. We show that the traditional approach to train empirical scoring functions, using linear regression to optimize the correlation of predicted and experimental binding affinities, does not result in a function with optimal docking capabilities. On the other hand, a combination of scoring, minimization, and re-docking on carefully curated training datasets allowed us to develop a simplified scoring function with optimum docking performance. This article provides an overview of the development of the Vinardo scoring function, highlights its differences with Vina, and compares the performance of the two scoring functions in scoring, docking and virtual screening applications. Vinardo outperforms Vina in all tests performed, for all datasets analyzed. The Vinardo scoring function is available as an option within Smina, a fork of Vina, which is freely available under the GNU Public License v2.0 from http://smina.sf.net. Precompiled binaries, source code, documentation and a tutorial for using Smina to run the Vinardo scoring function are available at the same address.

Suggested Citation

  • Rodrigo Quiroga & Marcos A Villarreal, 2016. "Vinardo: A Scoring Function Based on Autodock Vina Improves Scoring, Docking, and Virtual Screening," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-18, May.
    • Handle: RePEc:plo:pone00:0155183
    DOI: 10.1371/journal.pone.0155183
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    References listed on IDEAS

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    1. Max W Chang & Christian Ayeni & Sebastian Breuer & Bruce E Torbett, 2010. "Virtual Screening for HIV Protease Inhibitors: A Comparison of AutoDock 4 and Vina," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-9, August.
    2. Hongjian Li & Kwong-Sak Leung & Pedro J Ballester & Man-Hon Wong, 2014. "istar: A Web Platform for Large-Scale Protein-Ligand Docking," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-12, January.
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    Cited by:

    1. Adam Pecina & Jindřich Fanfrlík & Martin Lepšík & Jan Řezáč, 2024. "SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein–ligand binding affinity predictions in minutes," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    2. Ammu Prasanna Kumar & Suryani Lukman, 2018. "Allosteric binding sites in Rab11 for potential drug candidates," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-46, June.

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