科技科学

Breaking timescales with generative sampling of conformational transitions

Nature正文已收录本站

References

  1. Dror, R. O., Dirks, R. M., Grossman, J. P., Xu, H. & Shaw, D. E. Biomolecular simulation: a computational microscope for molecular biology. Annu. Rev. Biophys. 41, 429–452 (2012).

    Article  CAS  PubMed  Google Scholar 

  2. Chipot, C. Free energy methods for the description of molecular processes. Annu. Rev. Biophys. 52, 113–138 (2023).

    Article  CAS  PubMed  Google Scholar 

  3. Kang, C. et al. Convergence is not correctness: context-dependent performance of enhanced-sampling methods across biological complexity. Nat. Commun. 17, 6245 (2026).

  4. Ho, J., Jain, A. N. & Abbeel, P. Denoising diffusion probabilistic models. In Proc. Advances in Neural Information Processing Systems Vol. 33 (eds Larochelle, H. et al.) 6840–6851 (Curran Associates, 2020).

  5. Roux, B. Transition rate theory, spectral analysis, and reactive paths. J. Chem. Phys. 156, 134111 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  6. Lindorff-Larsen, K., Piana, S., Dror, R. O. & Shaw, D. E. How fast-folding proteins fold. Science 334, 517–520 (2011).

    Article  ADS  CAS  PubMed  Google Scholar 

  7. Jung, H. et al. Machine-guided path sampling to discover mechanisms of molecular self-organization. Nat. Comput. Sci. 3, 334–345 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Shaw, D. E. et al. Anton 3: twenty microseconds of molecular dynamics simulation before lunch. In Proc. International Conference for High Performance Computing, Networking, Storage and Analysis (eds de Supinski, B. R., Hall, M. W. & Gamblin, T.) 1:1–1:11 (Association for Computing Machinery, 2021).

  9. Ren, W. & Vanden-Eijnden, E. Finite temperature string method for the study of rare events. J. Phys. Chem. B 109, 6688–6693 (2005).

    Article  PubMed  Google Scholar 

  10. Pan, A. C., Sezer, D. & Roux, B. Finding transition pathways using the string method with swarms of trajectories. J. Phys. Chem. B 112, 3432–3440 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Miao, M. et al. Avoiding non-equilibrium effects in adaptive biasing force calculations. Mol. Simul. 47, 390–394 (2021).

    Article  CAS  Google Scholar 

  12. Vanden-Eijnden, E. & Tal, F. A. Transition state theory: variational formulation, dynamical corrections, and error estimates. J. Chem. Phys. 123, 184103 (2005).

    Article  ADS  PubMed  Google Scholar 

  13. He, Z., Chipot, C. & Roux, B. Committor-consistent variational string method. J. Phys. Chem. Lett. 13, 9263–9271 (2022).

    Article  CAS  PubMed  Google Scholar 

  14. Horvath, F. et al. STIM1 transmembrane helix dimerization captured by AI-guided transition path sampling. Proc. Natl Acad. Sci. USA 122, e2506516122 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Chen, H., Roux, B. & Chipot, C. Discovering reaction pathways, slow variables, and committor probabilities with machine learning. J. Chem. Theory Comput. 19, 4414–4426 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Megías, A. et al. Iterative variational learning of committor-consistent transition pathways using artificial neural networks. Nat. Comput. Sci. 5, 592–602 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Giuseppe Chen, C. et al. Following the committor flow: a data-driven discovery of transition pathways. J. Chem. Theory Comput. 22, 1258–1265 (2026).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Contreras Arredondo, S. et al. Learning the committor without collective variables. Nat. Comput. Sci. 6, 350–357 (2026).

    Article  PubMed  Google Scholar 

  19. Noé, F., Olsson, S., Köhler, J. & Wu, H. Boltzmann generators: sampling equilibrium states of many-body systems with deep learning. Science 365, eaaw1147 (2019).

    Article  ADS  PubMed  Google Scholar 

  20. Jing, B., Stärk, H., Jaakkola, T. & Berger, B. Generative modeling of molecular dynamics trajectories. In Proc. Advances in Neural Information Processing Systems Vol. 37 (eds Globerson, A. et al.) 40534–40564 (Curran Associates, 2024).

  21. Lewis, S. et al. Scalable emulation of protein equilibrium ensembles with generative deep learning. Science 389, eadv9817 (2025).

    Article  CAS  PubMed  Google Scholar 

  22. Thiemann, F. L. et al. Force-free molecular dynamics through autoregressive equivariant networks. Nat. Mach. Intell. 8, 764–776 (2026).

    Article  Google Scholar 

  23. Seong, K., Park, S., Kim, S., Kim, W. Y. & Ahn, S. Transition path sampling with improved off-policy training of diffusion path samplers. In Proc. International Conference on Learning Representations Vol. 2025 (eds Yue, Y. et al.) 93040–93062 (ICLR, 2025).

  24. Raja, S. et al. Action-minimization meets generative modeling: efficient transition path sampling with the Onsager–Machlup functional. In Proc. 42nd International Conference on Machine Learning Vol. 267 (eds Singh, A. et al.) 50972–51008 (PMLR, 2025).

  25. Kania, S., Webber, R. J., Simpson, G., Aristoff, D. & Zuckerman, D. M. Randomized Iterative trajectory reweighting for steady-state distributions without discretization error. Proc. Natl Acad. Sci. USA 123, e2529246123 (2026).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Bolhuis, P. G., Dellago, C., Geissler, P. L. & Chandler, D. Transition path sampling: throwing ropes over mountains in the dark. J. Phys. Condens. Matter 12, A147–A152 (2000).

    Article  ADS  CAS  Google Scholar 

  27. Juraszek, J. & Bolhuis, P. G. Sampling the multiple folding mechanisms of Trp-cage in explicit solvent. Proc. Natl Acad. Sci. USA 103, 15859–15864 (2006).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  28. Chen, H. & Chipot, C. Chasing collective variables using temporal data-driven strategies. QRB Discov. 4, e2 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Chen, H. et al. A companion guide to the string method with swarms of trajectories: characterization, performance, and pitfalls. J. Chem. Theory Comput. 18, 1406–1422 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Roh, S.-H. et al. Cryo-EM and MD infer water-mediated proton transport and autoinhibition mechanisms of the Vo complex. Sci. Adv. 6, eabb9605 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  31. Blanc, F. E. C. & Hummer, G. Mechanism of proton-powered c-ring rotation in a mitochondrial ATP synthase. Proc. Natl Acad. Sci. USA 121, e2314199121 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhou, R. Trp-cage: folding free energy landscape in explicit water. Proc. Natl Acad. Sci. USA 100, 13280–13285 (2003).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  33. Marinelli, F., Pietrucci, F., Laio, A. & Piana, S. A kinetic model of Trp-cage folding from multiple biased molecular dynamics simulations. PLoS Comput. Biol. 5, e1000452 (2009).

    Article  ADS  MathSciNet  PubMed  PubMed Central  Google Scholar 

  34. Sidky, H., Chen, W. & Ferguson, A. L. High-resolution Markov state models for the dynamics of Trp-cage miniprotein constructed over slow folding modes identified by state-free reversible VAMPnets. J. Phys. Chem. B 123, 7999–8009 (2019).

    Article  CAS  PubMed  Google Scholar 

  35. Klingenberg, M. The ADP and ATP transport in mitochondria and its carrier. Biochim. Biophys. Acta Biomembr. 1778, 1978–2021 (2008).

    Article  CAS  Google Scholar 

  36. Kunji, E. R. & Ruprecht, J. J. The mitochondrial ADP/ATP carrier exists and functions as a monomer. Biochem. Soc. Trans. 48, 1419–1432 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Tamura, K. & Hayashi, S. Atomistic modeling of alternating access of a mitochondrial ADP/ATP membrane transporter with molecular simulations. PLoS One 12, 1–21 (2017).

    Article  Google Scholar 

  38. Pietropaolo, A., Pierri, C. L., Palmieri, F. & Klingenberg, M. The switching mechanism of the mitochondrial ADP/ATP carrier explored by free-energy landscapes. Biochim. Biophys. Acta Bioenerg. 1857, 772–781 (2016).

    Article  CAS  Google Scholar 

  39. Yao, S. et al. Mechanistic insights into multiple-step transport of mitochondrial ADP/ATP carrier. Comput. Struct. Biotechnol. J. 20, 1829–1840 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Yi, Q. et al. Molecular dynamics simulations on apo ADP/ATP carrier shed new lights on the featured motif of the mitochondrial carriers. Mitochondrion 47, 94–102 (2019).

    Article  CAS  PubMed  Google Scholar 

  41. Springett, R., King, M. S., Crichton, P. G. & Kunji, E. R. Modelling the free energy profile of the mitochondrial ADP/ATP carrier. Biochim. Biophys. Acta Bioenerg. 1858, 906–914 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Okazaki, K.-I. et al. Mechanism of the electroneutral sodium/proton antiporter PaNhaP from transition-path shooting. Nat. Commun. 10, 1742 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  43. Thompson, M. J. et al. Asynchronous subunit transitions prime acetylcholine receptor activation. Science 391, eadw1264 (2025).

    Article  Google Scholar 

  44. Lev, B. et al. String method solution of the gating pathways for a pentameric ligand-gated ion channel. Proc. Natl Acad. Sci. USA 114, E4158–E4167 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Bergh, C., Heusser, S. A., Howard, R. & Lindahl, E. Markov state models of proton- and pore-dependent activation in a pentameric ligand-gated ion channel. eLife 10, e68369 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ajaz, A. et al. Concerted vs stepwise mechanisms in dehydro-Diels-Alder reactions. J. Org. Chem. 76, 9320–9328 (2011).

    Article  CAS  PubMed  Google Scholar 

  47. Biroli, G. & Kurchan, J. Metastable states in glassy systems. Phys. Rev. E 64, 016101 (2001).

    Article  ADS  CAS  Google Scholar 

  48. Yang, S., Banavali, N. K. & Roux, B. Mapping the conformational transition in Src activation by cumulating the information from multiple molecular dynamics trajectories. Proc. Natl Acad. Sci. USA 106, 3776–3781 (2009).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  49. Moradi, M. & Tajkhorshid, E. Mechanistic picture for conformational transition of a membrane transporter at atomic resolution. Proc. Natl Acad. Sci. USA 110, 18916–18921 (2013).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  50. Shaw, D. E. et al. Anton, a special-purpose machine for molecular dynamics simulation. Commun. ACM 51, 91–97 (2008).

    Article  Google Scholar 

  51. Sipka, M., Dietschreit, J. C. B., Grajciar, L. & Gomez-Bombarelli, R. Differentiable simulations for enhanced sampling of rare events. In Proc. 40th International Conference on Machine Learning Vol. 202 (eds Krause, A. et al.) 31990–32007 (PMLR, 2023).

  52. Swenson, D. W. H., Prinz, J.-H., Noé, F., Chodera, J. D. & Bolhuis, P. G. OpenPathSampling: a Python framework for path sampling simulations. 1. Basics. J. Chem. Theory Comput. 15, 813–836 (2019).

    Article  CAS  PubMed  Google Scholar 

  53. Belkacemi, Z., Gkeka, P., Lelièvre, T. & Stoltz, G. Chasing collective variables using autoencoders and biased trajectories. J. Chem. Theory Comput. 18, 59–78 (2022).

    Article  CAS  PubMed  Google Scholar 

  54. Frassek, M., Arjun, A. & Bolhuis, P. G. An extended autoencoder model for reaction coordinate discovery in rare event molecular dynamics datasets. J. Chem. Phys. 155, 064103 (2021).

    Article  ADS  CAS  PubMed  Google Scholar 

  55. Zou, Z., Wang, D. & Tiwary, P. A graph neural network-state predictive information bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics. Digit. Discov. 4, 211–221 (2025).

    Article  CAS  Google Scholar 

  56. Strahan, J., Finkel, J., Dinner, A. R. & Weare, J. Predicting rare events using neural networks and short-trajectory data. J. Comput. Phys. 488, 112152 (2023).

    Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

  57. Kang, P., Trizio, E. & Parrinello, M. Computing the committor with the committor to study the transition state ensemble. Nat. Comput. Sci. 4, 451–460 (2024).

    Article  PubMed  Google Scholar 

  58. Costa, A. d. S., Ponnapati, M., Rubin, D., Smidt, T. & Jacobson, J. Accelerating protein molecular dynamics simulation with DeepJump. Preprint at https://doi.org/10.48550/arXiv.2509.13294 (2025).

  59. Song, J., Meng, C. & Ermon, S. Denoising diffusion implicit models. In Proc. International Conference on Learning Representations (ICLR) (eds Oh, A., Murray, N. & Titov, I.) 1–20 (ICLR, 2021).

  60. Schlitter, J., Engels, M. & Krüger, P. Targeted molecular dynamics: a new approach for searching pathways of conformational transitions. J. Mol. Graph. 12, 84–89 (1994).

    Article  CAS  PubMed  Google Scholar 

  61. Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A. & Müller, K.-R. SchNet–a deep learning architecture for molecules and materials. J. Chem. Phys. 148, 241722 (2018).

  62. Vaswani, A. et al. Attention is all you need. In Proc. Advances in Neural Information Processing Systems Vol. 30 (eds Guyon, I. et al.) 5998–6008 (Curran Associates, 2017).

  63. Post, M. & Hummer, G. AI-guided transition path sampling of lipid flip-flop and membrane nanoporation. Nat. Commun. 17, 224 (2026).

    Article  ADS  CAS  Google Scholar 

  64. Breebaart, R. S., Lazzeri, G., Covino, R. & Bolhuis, P. G. Understanding mechanisms of molecular rare events from start to finish. Phys. Rev. Lett. 136, 168001 (2026).

  65. Clevert, D.-A., Unterthiner, T. & Hochreiter, S. Fast and accurate deep network learning by exponential linear units (ELUs). In Proc. 4th International Conference on Learning Representations (ICLR) (eds Bengio, Y. & LeCun, Y.) 1–14 (ICLR, 2016).

  66. Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. In Proc. 3rd International Conference on Learning Representations (ICLR) (eds Bengio, Y. & LeCun, Y.) 1–15 (ICLR, 2015).

  67. Phillips, J. C. et al. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J. Chem. Phys. 153, 044130 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  68. Huang, J. et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat. Methods 14, 71–73 (2017).

    Article  CAS  PubMed  Google Scholar 

  69. Pebay-Peyroula, E. et al. Structure of mitochondrial ADP/ATP carrier in complex with carboxyatractyloside. Nature 426, 39–44 (2003).

    Article  ADS  CAS  PubMed  Google Scholar 

  70. Webb, B. & Sali, A. Comparative protein structure modeling using MODELLER. Curr. Protoc. Bioinformatics 54, 5–6 (2016).

    Article  Google Scholar 

  71. Ruprecht, J. J. et al. The molecular mechanism of transport by the mitochondrial ADP/ATP carrier. Cell 176, 435–447 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Smart, O. S., Neduvelil, J. G., Wang, X., Wallace, B. A. & Sansom, M. S. P. HOLE: a program for the analysis of the pore dimensions of ion channel structural models. J. Mol. Graph. 14, 354–360 (1996).

    Article  CAS  PubMed  Google Scholar 

Download references