MoDock: A multi-objective strategy improves the accuracy for molecular dockingby Junfeng Gu, Xu Yang, Ling Kang, Jinying Wu, Xicheng Wang

Algorithms Mol Biol

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MoDock: A multi-objective o he l s on in f im h t f ov programs and SFs [4-22]. However, previous compara- quantum chemical calculation [25,26], application of modGu et al. Algorithms for Molecular Biology (2015) 10:8

DOI 10.1186/s13015-015-0034-8developing, deviations from the real binding energy are unavoidable. Based on this consideration, consensus scoring

Dalian 116023, China

Full list of author information is available at the end of the articletive studies have revealed that none of the docking programs and SFs truly outperforms the others, and a universally accurate docking method is still out of reach. ern computation technique and computational intelligence [27,28], etc. Despite many achievements have been obtained, the development of an ideal SF still has a long way to go. Therefore, how to improve the docking accuracy with available SFs is a practical and urgent task. Most docking methods are based on one single objective, i.e., a

SF. However, due to the approximation adopted in the SF * Correspondence: jfgu@dlut.edu.cn 1State Key Laboratory of Structural Analysis for Industrial Equipment,

Department of Engineering Mechanics, Dalian University of Technology,evaluate the relative performance of the most popularfunctions. Meanwhile, a 70% ratio of the good docking solutions with the RMSD value below 1.0 Å outperforms other 6 commonly used docking programs, even with a flexible receptor docking program included.

Conclusions: The results show MoDock is an effective strategy to overcome the deviations brought by single scoring function, and improves the prediction power of molecular docking.

Keywords: Multi-objective, Molecular docking, Scoring function, Optimization

Background

Structure-Based Virtual Screening (SBVS) has become a routine tool in both pharmaceutical companies and academic groups for early-stage drug discovery [1]. As a main method of SBVS, molecular docking is the most widely used in practice, and there have reported a number of successful examples [2]. As a result, the docking method has received increasing interest in recent times.

To date, over 60 docking programs and 30 scoring functions (SFs) have been disclosed [3]. For comparing their efficiency, there have been many comparative studies to

It is fundamentally an optimization problem of docking a ligand into the binding site of a receptor. As the objects during the optimization process, SFs estimate binding affinities between small ligands and proteins, and rank the compounds, playing an essential role in molecular docking.

The non-ideal efficacy of SFs is thought as the biggest barrier which hinders the improvement of the molecular docking method. The conflict between the accuracy and speed of SF is a difficult problem need to make great efforts in. More recently, many techniques have been applied to further improve the efficacy of SF, such as including thermodynamic data [23,24], including data derived fromaccuracy for molecular d

Junfeng Gu1*, Xu Yang1, Ling Kang2, Jinying Wu1 and Xic

Abstract

Background: As a main method of structure-based virtua practice. However, the non-ideal efficacy of scoring functi improvement of the molecular docking method.

Results: A new multi-objective strategy for molecular dock docking accuracy with available scoring functions. Instead o weight factors, an aggregate function is adopted to approx multi-constraint problem, which will simultaneously smoot

Then, method of centers and genetic algorithm are used to

GOLD test data set reveal the multi-objective strategy impr© 2015 Gu et al.; licensee BioMed Central. Thi

Attribution License (http://creativecommons.o reproduction in any medium, provided the or

Dedication waiver (http://creativecommons.or unless otherwise stated.Open Access strategy improves the cking ng Wang1 creening, molecular docking is the most widely used in s is thought as the biggest barrier which hinders the g, named as MoDock, is presented to further improve the simple combination of multiple objectives with fixed ate the real solution of the original multi-objective and he energy surface of the combined scoring functions. ind the optimal solution. Tests of MoDock against the es the docking accuracy over the individual scorings is an Open Access article distributed under the terms of the Creative Commons rg/licenses/by/2.0), which permits unrestricted use, distribution, and iginal work is properly credited. The Creative Commons Public Domain g/publicdomain/zero/1.0/) applies to the data made available in this article,

Gu et al. Algorithms for Molecular Biology (2015) 10:8 Page 2 of 9was developed by combining multiple SFs to reduce the deviations brought by individual SFs as possible. The critical step in consensus scoring is the design of an appropriate consensus scoring strategy of individual scores so that the true modes/binders can be discriminated from others accordingly. However, classic consensus strategy like linear combination is strongly dependent on the initial parameters, and simple combination of multiple SFs will make the energy curves discontinuous and non-smooth, and make the optimization problem more difficult to solve.

The application of multiple SFs makes docking become a multi-objective optimization problem. How to choose and combine the SFs, and design relevant optimization strategy to the multi-objective problem are crucial for improving the docking efficiency with consensus scoring. In this work, a multi-objective docking strategy MoDock is proposed to further improve the pose prediction with available SFs. The SFs used in consensus scoring are preferred to be not correlated, so that errors can be diminished. The available scoring functions can generally be divided into the following three types: force-field-based, empirical-based and knowledge-based SFs. They focus on diverse aspects of ligand binding, and are derived from different principles. Therefore, three representative scoring functions from these three types are introduced as the objectives, and then a multi-objective optimization method is designed to optimize these three objectives simultaneously. The publicly available GOLD test set containing 134 protein-ligand complexes is applied to evaluate the reliability of MoDock. The results indicate that the multiobjective strategy can enhance the pose prediction power of docking with the available SFs.