Molecular dynamics simulations provide detailed trajectories at the atomic level, but extracting interpretable and robust insights from these high-dimensional data remains challenging. In practice, analyses typically rely on a single representation. Here, we show that representation choice is not neutral: it fundamentally shapes the conformational organization, similarity relationships, and apparent transitions inferred from identical simulation data.
To complement existing representations, we introduce Orientation features, a geometrically grounded, rotation-aware encoding of protein backbone. We compare it against common descriptions across three dynamical regimes: fast-folding proteins, large-scale domain motions, and protein-protein association. Across these systems, we find that different representations emphasize complementary aspects of conformational space, and that no single representation provides a complete picture of the underlying dynamics.
To facilitate systematic comparison, we developed ManiProt, a library for efficient computation and analysis of multiple protein representations. Our results motivate a comparative, representation-aware framework for the interpretation of molecular dynamics simulations.
ConforNets: Latents-Based Conformational Control in OpenFold3
Department of Systems Biology, Columbia University, NY, USA（系统生物学系，哥伦比亚大学，纽约，美国）；Department of Computer Science, Columbia University, NY, USA（计算机科学系，哥伦比亚大学，纽约，美国）；Department of Computer Science, Princeton University, Princeton, NJ, USA（计算机科学系，普林斯顿大学，普林斯顿，新泽西州，美国）
ENSEMBITS: an alphabet of protein conformational ensembles
Department of Computer Science, Vanderbilt University（范德比尔特大学计算机科学系）；Center for AI in Protein Dynamics, Vanderbilt University（蛋白质动力学中的人工智能中心，范德比尔特大学）；Department of Molecular Physiology and Biophysics, Vanderbilt University（分子生理学与生物物理学系，范德比尔特大学）
Yusuf Hamied Department of Chemistry University of Cambridge（尤瑟夫·哈米德化学系剑桥大学）；Department of Computer Science and Technology University of Cambridge（计算机科学与技术系剑桥大学）；Institute for Drug Discovery Leipzig University（药物发现研究所莱比锡大学）；Department of Chemistry Ludwig-Maximilans-University Munich, Germany（化学系路易斯·马克西米利安大学慕尼黑，德国）
Rigidity-Aware Geometric Pretraining for Protein Design and Conformational Ensembles
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Washington（华盛顿大学）；MPI for Intelligent Systems, Tübingen（智能系统马克斯·普朗克研究所，图宾根）；National Research Council of Canada（加拿大国家研究理事会）；University of Ottawa（渥太华大学）；The Chinese University of Hong Kong（香港中文大学）
Learning conformational ensembles of proteins based on backbone geometry
Max Planck Institute for Polymer Research（马克斯·普朗克聚合物研究所）；Heidelberg Institute for Theoretical Studies（海德堡理论研究所）；IWR, Heidelberg University（海德堡大学IWR中心）；SciLifeLab and DBB at Stockholm University（斯德哥尔摩大学SciLifeLab和DBB中心）；Karlsruhe Institute of Technology（卡尔斯鲁厄技术大学）
PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks
Department of Computational, Quantitative, and Synthetic Biology (CQSB), UMR 7238 IBPS, Sorbonne Université, CNRS（索邦大学国家科学研究中心计算、定量与合成生物学系（CQSB），UMR 7238 IBPS）；Univ. Grenoble Alpes, CNRS, Grenoble INP, LJK（格勒诺布尔阿尔卑斯大学国家科学研究中心格勒诺布尔国立理工学院让·库尔实验室（LJK））；Institut universitaire de France (IUF)（法国大学研究院（IUF））
Polyformer: a generative framework for thermodynamic modeling of polymeric molecules
PsiDagger；Department of Physics and Astronomy, University of Pittsburgh（物理与天文学系，匹兹堡大学）；Department of Chemistry and The PULSE Institute, Stanford University（化学系和PULSE研究所，斯坦福大学）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）
