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Exploring transition states of protein conformational changes via out-of-distribution detection in the hyperspherical latent space

Author

Listed:
  • Bojun Liu

    (University of Wisconsin-Madison
    University of Wisconsin-Madison)

  • Jordan G. Boysen

    (University of Wisconsin-Madison)

  • Ilona Christy Unarta

    (University of Wisconsin-Madison
    University of Wisconsin-Madison)

  • Xuefeng Du

    (University of Wisconsin-Madison)

  • Yixuan Li

    (University of Wisconsin-Madison
    University of Wisconsin-Madison)

  • Xuhui Huang

    (University of Wisconsin-Madison
    University of Wisconsin-Madison)

Abstract

Identifying transitional states is crucial for understanding protein conformational changes that underlie numerous biological processes. Markov state models (MSMs), built from Molecular Dynamics (MD) simulations, capture these dynamics through transitions among metastable conformational states, and have demonstrated success in studying protein conformational changes. However, MSMs face challenges in identifying transition states, as they partition MD conformations into discrete metastable states (or free energy minima), lacking description of transition states located at the free energy barriers. Here, we introduce Transition State identification via Dispersion and vAriational principle Regularized neural networks (TS-DAR), a deep learning framework inspired by out-of-distribution (OOD) detection in trustworthy artificial intelligence (AI). TS-DAR offers an end-to-end pipeline that can simultaneously detect all transition states between multiple free minima from MD simulations using the regularized hyperspherical embeddings in latent space. The key insight of TS-DAR lies in treating transition state structures as OOD data, recognizing that they are sparsely populated and exhibit a distributional shift from metastable states. We demonstrate the power of TS-DAR by applying it to a 2D potential, alanine dipeptide, and the translocation of a DNA motor protein on DNA, where it outperforms previous methods in identifying transition states.

Suggested Citation

  • Bojun Liu & Jordan G. Boysen & Ilona Christy Unarta & Xuefeng Du & Yixuan Li & Xuhui Huang, 2025. "Exploring transition states of protein conformational changes via out-of-distribution detection in the hyperspherical latent space," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-55228-4
    DOI: 10.1038/s41467-024-55228-4
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    References listed on IDEAS

    as
    1. Daniel-Adriano Silva & Gregory R Bowman & Alejandro Sosa-Peinado & Xuhui Huang, 2011. "A Role for Both Conformational Selection and Induced Fit in Ligand Binding by the LAO Protein," PLOS Computational Biology, Public Library of Science, vol. 7(5), pages 1-11, May.
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