Continued development of an empirical function for predicting and rationalizing protein-protein binding affinities

被引:8
|
作者
Audie, Joseph [1 ,2 ]
机构
[1] Univ Cattolica Sacro Cuore, Dept Chem, Fairfield, CT 06825 USA
[2] CMD Biosci, Orange, CT USA
关键词
Protein-protein recognition; Binding affinity; Free energy; Alanine scanning mutagenesis; Hot spot; Peptide therapeutics; Biologics; Computational; FREE-ENERGY FUNCTION; PEPTIDE INHIBITOR; COMPLEXES; DESIGN; MUTATIONS; DOCKING;
D O I
10.1016/j.bpc.2009.05.003
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
Here we summarize recent work on the continued development of our fast and simple empirical equation for predicting and structurally rationalizing protein-protein and protein-peptide binding affinities. Our empirical expression consists of six regression-weighted physical descriptors and derives from two key simplifying assumptions: (1) the assumption of rigid-body association and (2) the assumption that all contributions not explicitly considered in the equation make a net contribution to binding of approximate to 0 kcal. Within the strict framework of rigid-body association, we tested relative binding affinity predictions using our empirical equation against the corresponding experimental binding free energy data for 197 interface alanine mutants. Our methodology produced excellent agreement between prediction and experiment for 79% of the mutations considered. These encouraging results further suggest the basic validity of our approach. Further analysis suggests that the majority of the failed predictions can be accounted for in terms of mutation induced violations of assumption (2). In particular, we hypothesize that assumed away charge and aromatic side chain-mediated electrostatic interface interactions play a key role in protein-protein recognition and that such interactions must be explicitly considered for a more generally valid approach to physics-based binding affinity prediction. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:139 / 144
页数:6
相关论文
共 50 条
  • [1] A single empirical expression for predicting protein-protein binding affinities and geometries
    Audie, Joseph
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2009, 237
  • [2] A novel empirical free energy function that explains and predicts protein-protein binding affinities
    Audie, Joseph
    Scarlata, Suzanne
    BIOPHYSICAL CHEMISTRY, 2007, 129 (2-3) : 198 - 211
  • [3] A minimal model of protein-protein binding affinities
    Janin, Joel
    PROTEIN SCIENCE, 2014, 23 (12) : 1813 - 1817
  • [4] Computational prediction of protein-protein binding affinities
    Siebenmorgen, Till
    Zacharias, Martin
    WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE, 2020, 10 (03)
  • [5] Development of an empirical scoring function for calculating protein-ligand binding affinities
    Friesner, Richard A.
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2012, 243
  • [6] Computational Procedure for Predicting Excipient Effects on Protein-Protein Affinities
    Dignon, Gregory L.
    Dill, Ken A.
    JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2024, 20 (03) : 1479 - 1488
  • [7] Development and validation of an empirical free energy function for calculating protein-protein binding free energy surfaces
    Audie, Joseph
    BIOPHYSICAL CHEMISTRY, 2009, 139 (2-3) : 84 - 91
  • [8] A Rigorous Framework for Calculating Protein-Protein Binding Affinities in Membranes
    Blazhynska, Marharyta
    Gumbart, James C.
    Chen, Haochuan
    Tajkhorshid, Emad
    Roux, Benoit
    Chipot, Christophe
    JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2023, 19 (24) : 9077 - 9092
  • [9] Protein interaction score (PI-Score): The derivation and validation of a novel empirical free energy function that explains and predicts protein-protein binding affinities
    Audie, Joseph
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2007, 233 : 177 - 177
  • [10] Assessment of software methods for estimating protein-protein relative binding affinities
    Gonzalez, Tawny R.
    Martin, Kyle P.
    Barnes, Jonathan E.
    Patel, Jagdish Suresh
    Ytreberg, F. Marty
    PLOS ONE, 2020, 15 (12):