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AF2Complex predicts direct physical interactions in multimeric proteins with deep learning

Author

Listed:
  • Mu Gao

    (Center for the Study of Systems Biology, School of Biological Sciences)

  • Davi Nakajima An

    (School of Computer Science, Georgia Institute of Technology)

  • Jerry M. Parks

    (Biosciences Division, Oak Ridge National Laboratory)

  • Jeffrey Skolnick

    (Center for the Study of Systems Biology, School of Biological Sciences)

Abstract

Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Remarkably accurate atomic structures have been recently computed for individual proteins by AlphaFold2 (AF2). Here, we demonstrate that the same neural network models from AF2 developed for single protein sequences can be adapted to predict the structures of multimeric protein complexes without retraining. In contrast to common approaches, our method, AF2Complex, does not require paired multiple sequence alignments. It achieves higher accuracy than some complex protein-protein docking strategies and provides a significant improvement over AF-Multimer, a development of AlphaFold for multimeric proteins. Moreover, we introduce metrics for predicting direct protein-protein interactions between arbitrary protein pairs and validate AF2Complex on some challenging benchmark sets and the E. coli proteome. Lastly, using the cytochrome c biogenesis system I as an example, we present high-confidence models of three sought-after assemblies formed by eight members of this system.

Suggested Citation

  • Mu Gao & Davi Nakajima An & Jerry M. Parks & Jeffrey Skolnick, 2022. "AF2Complex predicts direct physical interactions in multimeric proteins with deep learning," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29394-2
    DOI: 10.1038/s41467-022-29394-2
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    2. Zhiye Guo & Jian Liu & Jeffrey Skolnick & Jianlin Cheng, 2022. "Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Lorena Ilcu & Lukas Denkhaus & Anton Brausemann & Lin Zhang & Oliver Einsle, 2023. "Architecture of the Heme-translocating CcmABCD/E complex required for Cytochrome c maturation," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    4. Hélène Bret & Jinmei Gao & Diego Javier Zea & Jessica Andreani & Raphaël Guerois, 2024. "From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    5. Andreas Lackner & Michael Müller & Magdalena Gamperl & Delyana Stoeva & Olivia Langmann & Henrieta Papuchova & Elisabeth Roitinger & Gerhard Dürnberger & Richard Imre & Karl Mechtler & Paulina A. Lato, 2023. "The Fgf/Erf/NCoR1/2 repressive axis controls trophoblast cell fate," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    6. Jiao Li & Wan Zheng & Ming Gu & Long Han & Yanmei Luo & Koukou Yu & Mengxin Sun & Yuliang Zong & Xiuxiu Ma & Bing Liu & Ethan P. Lowder & Deanna L. Mendez & Robert G. Kranz & Kai Zhang & Jiapeng Zhu, 2022. "Structures of the CcmABCD heme release complex at multiple states," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    7. Lijun Shi & Chenguang Yang & Mingyuan Zhang & Kangning Li & Keying Wang & Li Jiao & Ruming Liu & Yunyun Wang & Ming Li & Yong Wang & Lu Ma & Shuxin Hu & Xin Bian, 2024. "Dissecting the mechanism of atlastin-mediated homotypic membrane fusion at the single-molecule level," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    8. Felix Homma & Jie Huang & Renier A. L. van der Hoorn, 2023. "AlphaFold-Multimer predicts cross-kingdom interactions at the plant-pathogen interface," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    9. Yitang Zhang & Maofei Chen & Xudong Chen & Minghui Zhang & Jian Yin & Zi Yang & Xin Gao & Sensen Zhang & Maojun Yang, 2024. "Molecular architecture of the mammalian 2-oxoglutarate dehydrogenase complex," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    10. Christoph Buhlheller & Theo Sagmeister & Christoph Grininger & Nina Gubensäk & Uwe B. Sleytr & Isabel Usón & Tea Pavkov-Keller, 2024. "SymProFold: Structural prediction of symmetrical biological assemblies," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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