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Quantitative prediction of protein–protein binding affinity with a potential of mean force considering volume correction

Authors

  • Yu Su,

    1. MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing 100084, China
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  • Ao Zhou,

    1. Department of Physics, Nanjing University, Nanjing 210093, China
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  • Xuefeng Xia,

    1. MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing 100084, China
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  • Wen Li,

    1. MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing 100084, China
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  • Zhirong Sun

    Corresponding author
    1. MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing 100084, China
    • MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing 100084, China
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Abstract

Quantitative prediction of protein–protein binding affinity is essential for understanding protein–protein interactions. In this article, an atomic level potential of mean force (PMF) considering volume correction is presented for the prediction of protein–protein binding affinity. The potential is obtained by statistically analyzing X-ray structures of protein–protein complexes in the Protein Data Bank. This approach circumvents the complicated steps of the volume correction process and is very easy to implement in practice. It can obtain more reasonable pair potential compared with traditional PMF and shows a classic picture of nonbonded atom pair interaction as Lennard-Jones potential. To evaluate the prediction ability for protein–protein binding affinity, six test sets are examined. Sets 1–5 were used as test set in five published studies, respectively, and set 6 was the union set of sets 1–5, with a total of 86 protein–protein complexes. The correlation coefficient (R) and standard deviation (SD) of fitting predicted affinity to experimental data were calculated to compare the performance of ours with that in literature. Our predictions on sets 1–5 were as good as the best prediction reported in the published studies, and for union set 6, R = 0.76, SD = 2.24 kcal/mol. Furthermore, we found that the volume correction can significantly improve the prediction ability. This approach can also promote the research on docking and protein structure prediction.

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