Combination of scoring functions improves discrimination in protein-protein docking

被引:34
|
作者
Murphy, J [1 ]
Gatchell, DW [1 ]
Prasad, JC [1 ]
Vajda, S [1 ]
机构
[1] Boston Univ, Dept Biomed Engn, Boston, MA 02215 USA
来源
关键词
fast Fourier transform (FFT); rigid-body docking; binding free energy; structure-based potential; ranking docked conformations;
D O I
10.1002/prot.10473
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Two structure-based potentials are used for both filtering (i.e., selecting a subset of conformations generated by rigid-body docking), and rescoring and ranking the selected conformations. ACP (atomic contact potential) is an atom-level extension of the Miyazawa-Jernigan potential parameterized on protein structures, whereas RPScore (residue pair potential score) is a residue-level potential, based on interactions in protein-protein complexes. These potentials are combined with other energy terms and applied to 13 sets of protein decoys, as well as to the results of docking 10 pairs of unbound proteins. For both potentials, the ability to discriminate between near-native and non-native docked structures is substantially improved by refining the structures and by adding a van der Waals energy term. It is observed that ACP and RPScore complement each other in a number of ways (e.g., although RPScore yields more hits than ACP, mainly as a result of its better performance for charged complexes, ACP usually ranks the near-native complexes better). As a general solution to the protein-docking problem, we have found that the best discrimination strategies combine either an RPScore filter with an ACP-based scoring function, or an ACP-based filter with an RPScore-based scoring function. Thus, ACP and RPScore capture complementary structural information, and combining them in a multistage postprocessing protocol provides substantially better discrimination than the use of the same potential for both filtering and ranking the docked conformations. (C) 2003 Wiley-Liss, Inc.
引用
收藏
页码:840 / 854
页数:15
相关论文
共 50 条
  • [21] The scoring of poses in protein-protein docking: current capabilities and future directions
    Iain H Moal
    Mieczyslaw Torchala
    Paul A Bates
    Juan Fernández-Recio
    BMC Bioinformatics, 14
  • [22] The scoring of poses in protein-protein docking: current capabilities and future directions
    Moal, Iain H.
    Torchala, Mieczyslaw
    Bates, Paul A.
    Fernandez-Recio, Juan
    BMC BIOINFORMATICS, 2013, 14
  • [23] An Improved Protein-Protein Docking Technique Using Multilevel Scoring Function
    Sunny, Sharon
    Kataria, Deepesh
    Jayaraj, P. B.
    PROCEEDINGS OF THE 2019 IEEE REGION 10 CONFERENCE (TENCON 2019): TECHNOLOGY, KNOWLEDGE, AND SOCIETY, 2019, : 751 - 756
  • [24] FTDMP: A Framework for Protein-Protein, Protein-DNA, and Protein-RNA Docking and Scoring
    Olechnovic, Kliment
    Banciul, Rita
    Dapkunas, Justas
    Venclovas, Ceslovas
    PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, 2025,
  • [25] Protein-Protein Docking
    Ehrlich, Lutz P.
    Wade, Rebecca C.
    REVIEWS IN COMPUTATIONAL CHEMISTRY, VOL 17, 2001, 17 : 61 - 97
  • [26] Scoring optimisation of unbound protein-protein docking including protein binding site predictions
    Schneider, Sebastian
    Zacharias, Martin
    JOURNAL OF MOLECULAR RECOGNITION, 2012, 25 (01) : 15 - 23
  • [27] Are Scoring Functions in Protein-Protein Docking Ready To Predict Interactomes? Clues from a Novel Binding Affinity Benchmark
    Kastritis, Panagiotis L.
    Bonvin, Alexandre M. J. J.
    JOURNAL OF PROTEOME RESEARCH, 2010, 9 (05) : 2216 - 2225
  • [28] Rigid Docking Based Protein-Protein Interaction Prediction using High Scoring Docking Models
    Matsuzaki, Yuri
    Simm, Jaak
    BIOPHYSICAL JOURNAL, 2016, 110 (03) : 327A - 327A
  • [29] Scoring functions for protein-ligand docking
    Jain, Ajay N.
    CURRENT PROTEIN & PEPTIDE SCIENCE, 2006, 7 (05) : 407 - 420
  • [30] A new protein-protein docking scoring function based on interface residue properties
    Bernauer, J.
    Aze, J.
    Janin, J.
    Poupon, A.
    BIOINFORMATICS, 2007, 23 (05) : 555 - 562