Literature DB >> 31887035

Autodock Vina Adopts More Accurate Binding Poses but Autodock4 Forms Better Binding Affinity.

Nguyen Thanh Nguyen1, Trung Hai Nguyen2,3, T Ngoc Han Pham4, Nguyen Truong Huy4, Mai Van Bay5, Minh Quan Pham6, Pham Cam Nam5, Van V Vu7, Son Tung Ngo2,3.   

Abstract

The binding pose and affinity between a ligand and enzyme are very important pieces of information for computer-aided drug design. In the initial stage of a drug discovery project, this information is often obtained by using molecular docking methods. Autodock4 and Autodock Vina are two commonly used open-source and free software tools to perform this task, and each has been cited more than 6000 times in the last ten years. It is of great interest to compare the success rate of the two docking software programs for a large and diverse set of protein-ligand complexes. In this study, we selected 800 protein-ligand complexes for which both PDB structures and experimental binding affinity are available. Docking calculations were performed for these complexes using both Autodock4 and Autodock Vina with different docking options related to computing resource consumption and accuracy. Our calculation results are in good agreement with a previous study that the Vina approach converges much faster than AD4 one. However, interestingly, AD4 shows a better performance than Vina over 21 considered targets, whereas the Vina protocol is better than the AD4 package for 10 other targets. There are 16 complexes for which both the AD4 and Vina protocols fail to produce a reasonable correlation with respected experiments so both are not suitable to use to estimate binding free energies for these cases. In addition, the best docking option for performing the AD4 approach is the long option. However, the short option is the best solution for carrying out Vina docking. The obtained results probably will be useful for future docking studies in deciding which program to use.

Entities:  

Year:  2020        PMID: 31887035     DOI: 10.1021/acs.jcim.9b00778

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  52 in total

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Journal:  Chem Rev       Date:  2021-02-05       Impact factor: 60.622

7.  Accelerating AutoDock4 with GPUs and Gradient-Based Local Search.

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8.  Molecular Dockings and Molecular Dynamics Simulations Reveal the Potency of Different Inhibitors against Xanthine Oxidase.

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9.  GNINA 1.0: molecular docking with deep learning.

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10.  Study on the potential active components and molecular mechanism of Xiao Huoluo Pills in the treatment of cartilage degeneration of knee osteoarthritis based on bioinformatics analysis and molecular docking technology.

Authors:  Weijian Chen; Tianye Lin; Qi He; Peng Yang; Gangyu Zhang; Fayi Huang; Zihao Wang; Hao Peng; Baolin Li; Du Liang; Haibin Wang
Journal:  J Orthop Surg Res       Date:  2021-07-17       Impact factor: 2.359

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