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High-throughput prediction of protein conformational distributions with subsampled AlphaFold2

Author

Listed:
  • Gabriel Monteiro da Silva

    (Brown University Department of Molecular and Cell Biology and Biochemistry)

  • Jennifer Y. Cui

    (Brown University Department of Molecular and Cell Biology and Biochemistry)

  • David C. Dalgarno

    (Dalgarno Scientific LLC)

  • George P. Lisi

    (Brown University Department of Molecular and Cell Biology and Biochemistry
    Brown University Department of Chemistry)

  • Brenda M. Rubenstein

    (Brown University Department of Molecular and Cell Biology and Biochemistry
    Brown University Department of Chemistry)

Abstract

This paper presents an innovative approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins’ ground state conformations and is limited in its ability to predict conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different protein conformations by subsampling multiple sequence alignments. We tested our method against nuclear magnetic resonance experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted changes in their relative state populations with more than 80% accuracy. Our subsampling approach worked best when used to qualitatively predict the effects of mutations or evolution on the conformational landscape and well-populated states of proteins. It thus offers a fast and cost-effective way to predict the relative populations of protein conformations at even single-point mutation resolution, making it a useful tool for pharmacology, analysis of experimental results, and predicting evolution.

Suggested Citation

  • Gabriel Monteiro da Silva & Jennifer Y. Cui & David C. Dalgarno & George P. Lisi & Brenda M. Rubenstein, 2024. "High-throughput prediction of protein conformational distributions with subsampled AlphaFold2," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46715-9
    DOI: 10.1038/s41467-024-46715-9
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    Cited by:

    1. Devlina Chakravarty & Joseph W. Schafer & Ethan A. Chen & Joseph F. Thole & Leslie A. Ronish & Myeongsang Lee & Lauren L. Porter, 2024. "AlphaFold predictions of fold-switched conformations are driven by structure memorization," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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