Cryo-EM as latent structural landscape microscopy

Abstract A protein populates a landscape of structural states, abundant snapshots of which are sampled in every cryo-EM experiment. Current analysis averages these snapshots into single density maps or partitions them into discrete classes, discarding the continuous dynamics encoded in the data. Continuous latent-space methods offer a promising alternative, yet whether their learned representations are physically grounded remains unresolved. Here, we realize cryo-EM as structural landscape microscopy, in which latent density directly reflects the probabilistic distribution of molecular states. A central question is whether such a landscape reflects physical reality. For integrin αvβ8, the learned landscape shows strong agreement with independently derived molecular dynamics simulations, supporting its physical plausibility. We then apply the landscape to structural states that conventional cryo-EM cannot resolve. For LIS1-mediated dynein activation, the landscape reveals a spectrum of states from dominant conformations to low-population intermediates defined by distinct binding modes, including a previously unresolved state. For the KCTD5/CUL3NTD/Gβγ complex, the landscape resolves continuous conformational pathways directly from experimental data. Probability-guided particle selection further improves reconstruction quality. Competing Interest Statement The authors have declared no competing interest. Footnotes Contributing authors: daihzh2023{at}shanghaitech.edu.cn; chenqh2024{at}shanghaitech.edu.cn; lilq2023{at}shanghaitech.edu.cn; skrry_12138{at}sjtu.edu.cn; xuzhy12024{at}shanghaitech.edu.cn; limzh2022{at}shanghaitech.edu.cn; xieyf2022{at}shanghaitech.edu.cn; zhengjj1{at}shanghaitech.edu.cn; liuzhj{at}shanghaitech.edu.cn; Author ordering are well decided and updated.