Accounting for Intraligand Interactions in Flexible Ligand Docking with a PMF-Based Scoring Function

被引:6
|
作者
Lizunov, A. Y. [1 ,2 ]
Gonchar, A. L. [2 ]
Zaitseva, N. I. [3 ]
Zosimov, V. V. [1 ]
机构
[1] Moscow Inst Phys & Technol, Dept Math, Moscow 117303, Russia
[2] Moscow MV Lomonosov State Univ, Fac Fundamental Med, Moscow 119991, Russia
[3] First Moscow State Med Univ, Fac Pharm, Moscow 119991, Russia
基金
俄罗斯科学基金会;
关键词
KNOWLEDGE-BASED POTENTIALS; BINDING-AFFINITY; PROTEIN; VALIDATION; PREDICTION; ALGORITHM; PROGRAMS; DATABASE; HIV-1;
D O I
10.1021/acs.jcim.5b00158
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
We analyzed the frequency with which intra-ligand contacts occurred in a set of 1300 protein ligand complexes [Plewczynski et al. J. Comput. Chem. 2011, 32, 742-755.]. Our analysis showed that flexible figands often form intraligand hydrophobic contacts, while intraligand hydrogen bonds are rare. The test set was also thoroughly investigated and classified. We suggest a universal method for enhancement of a scoring function based on a potential of mean force (PMF-based score) by adding a term accounting for intraligand interactions. The method was implemented via in-house developed program, utilizing an Algo_score scoring function [Ramensky et al. Proteins: Struct., Punct., Genet. 2007, 69, 349-357.] based on the Tarasov-Muryshev PMF [Muryshev et al. J. Comput.-Aided MoL Des. 2003, 17, 597-605.]. The enhancement of the scoring function was shown to significantly improve the docking and scoring quality for flexible ligands in the test set of 1300 protein ligand complexes [Plewczynski et al. J. Comput. Chem. 2011, 32, 742-755.]. We then investigated the correlation of the docking results with two parameters of intraligand interactions estimation. These parameters are the weight of intraligand interactions and the minimum number of bonds between the ligand atoms required to take their interaction into account.
引用
收藏
页码:2121 / 2137
页数:17
相关论文
共 50 条
  • [31] A knowledge-based halogen bonding scoring function for predicting protein-ligand interactions
    Liu, Yingtao
    Xu, Zhijian
    Yang, Zhuo
    Chen, Kaixian
    Zhu, Weiliang
    JOURNAL OF MOLECULAR MODELING, 2013, 19 (11) : 5015 - 5030
  • [32] Fragment-based flexible ligand docking by evolutionary optimization
    Budin, N
    Majeux, N
    Caflisch, A
    BIOLOGICAL CHEMISTRY, 2001, 382 (09) : 1365 - 1372
  • [33] LeScore: a scoring function incorporating hydrogen bonding penalty for protein-ligand docking
    Xie, Aowei
    Zhao, Guangjian
    Liang, Huicong
    Gao, Ting
    Gao, Xinru
    Hou, Ning
    Wei, Fengjiao
    Li, Jiajie
    Zhao, Hongtao
    Xu, Ximing
    JOURNAL OF MOLECULAR MODELING, 2025, 31 (04)
  • [34] Vinardo: A Scoring Function Based on Autodock Vina Improves Scoring, Docking, and Virtual Screening
    Quiroga, Rodrigo
    Villarreal, Marcos A.
    PLOS ONE, 2016, 11 (05):
  • [35] Combining docking, scoring and molecular field analyses to probe influenza neuraminidase-ligand interactions
    Abu Hammad, Areej M.
    Afifi, Fatma U.
    Taha, Mutasem O.
    JOURNAL OF MOLECULAR GRAPHICS & MODELLING, 2007, 26 (02): : 443 - 456
  • [36] Bootstrap-based consensus scoring method for protein-ligand docking
    Fukunishi, Hiroaki
    Teramoto, Reiji
    Takada, Toshikazu
    Shimada, Jiro
    JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2008, 48 (05) : 988 - 996
  • [37] Prediction of ligand binding sites using improved blind docking method with a Machine Learning-Based scoring function
    Che, Xinhao
    Chai, Shiyang
    Zhang, Zhongzhou
    Zhang, Lei
    CHEMICAL ENGINEERING SCIENCE, 2022, 261
  • [38] ITScore: A novel iterative knowledge-based scoring function to predict protein-ligand interactions
    Huang, SY
    Zou, XQ
    BIOPHYSICAL JOURNAL, 2005, 88 (01) : 218A - 218A
  • [39] CONTOURO: A new directional contact based scoring function for capturing protein- ligand binding interactions
    Lindblom, Peter
    Wu, Guosheng
    Liu, Zhijie
    Jim, Kam-Chuen
    Claremon, David
    Gregg, Richard
    Singh, Suresh B.
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2012, 243
  • [40] Improving the Accuracy of Knowledge-Based Scoring Functions for Protein-Ligand Interactions by Accounting for Sparse Data in the Training Set
    Grinter, Sam Z.
    Zou, Xiaoqin
    BIOPHYSICAL JOURNAL, 2012, 102 (03) : 409A - 409A