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Structure-based prediction of protein–protein interactions on a genome-wide scale

Author

Listed:
  • Qiangfeng Cliff Zhang

    (Howard Hughes Medical Institute, Columbia University
    Columbia University
    Center for Computational Biology and Bioinformatics, Columbia Initiative in Systems Biology, Columbia University)

  • Donald Petrey

    (Howard Hughes Medical Institute, Columbia University
    Columbia University
    Center for Computational Biology and Bioinformatics, Columbia Initiative in Systems Biology, Columbia University)

  • Lei Deng

    (Columbia University
    Center for Computational Biology and Bioinformatics, Columbia Initiative in Systems Biology, Columbia University
    Tongji University)

  • Li Qiang

    (Naomi Berrie Diabetes Center, College of Physicians & Surgeons of Columbia University)

  • Yu Shi

    (Molecular and Cell Biology Laboratory, The Salk Institute for Biological Studies)

  • Chan Aye Thu

    (Columbia University)

  • Brygida Bisikirska

    (Center for Computational Biology and Bioinformatics, Columbia Initiative in Systems Biology, Columbia University)

  • Celine Lefebvre

    (Center for Computational Biology and Bioinformatics, Columbia Initiative in Systems Biology, Columbia University
    Institute of Cancer Genetics, Columbia University)

  • Domenico Accili

    (Naomi Berrie Diabetes Center, College of Physicians & Surgeons of Columbia University)

  • Tony Hunter

    (Molecular and Cell Biology Laboratory, The Salk Institute for Biological Studies)

  • Tom Maniatis

    (Columbia University)

  • Andrea Califano

    (Columbia University
    Center for Computational Biology and Bioinformatics, Columbia Initiative in Systems Biology, Columbia University
    Institute of Cancer Genetics, Columbia University
    Columbia University)

  • Barry Honig

    (Howard Hughes Medical Institute, Columbia University
    Columbia University
    Center for Computational Biology and Bioinformatics, Columbia Initiative in Systems Biology, Columbia University)

Abstract

Protein–protein interactions, essential for understanding how a cell functions, are predicted using a new method that combines protein structure with other computationally and experimentally derived clues.

Suggested Citation

  • Qiangfeng Cliff Zhang & Donald Petrey & Lei Deng & Li Qiang & Yu Shi & Chan Aye Thu & Brygida Bisikirska & Celine Lefebvre & Domenico Accili & Tony Hunter & Tom Maniatis & Andrea Califano & Barry Honi, 2012. "Structure-based prediction of protein–protein interactions on a genome-wide scale," Nature, Nature, vol. 490(7421), pages 556-560, October.
    • Handle: RePEc:nat:nature:v:490:y:2012:i:7421:d:10.1038_nature11503
    DOI: 10.1038/nature11503
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    Cited by:

    1. Patrick Bryant & Gabriele Pozzati & Arne Elofsson, 2022. "Improved prediction of protein-protein interactions using AlphaFold2," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Zemin Zhang & Yuanqing Li & Jie Yang & Jiacheng Li & Xiongqiang Lin & Ting Liu & Shiling Yang & Jin Lin & Shengyu Xue & Jiamin Yu & Cailing Tang & Ziteng Li & Liping Liu & Zhengzheng Ye & Yanan Deng &, 2024. "Dual-site molecular glues for enhancing protein-protein interactions of the CDK12-DDB1 complex," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    3. Jiahua Rao & Jiancong Xie & Qianmu Yuan & Deqin Liu & Zhen Wang & Yutong Lu & Shuangjia Zheng & Yuedong Yang, 2024. "A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    4. Lucien F. Krapp & Luciano A. Abriata & Fabio Cortés Rodriguez & Matteo Dal Peraro, 2023. "PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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