TEMPO: Temporal Multi-scale Autoregressive Generation of Protein Conformational Ensembles

• URL: http://arxiv.org/abs/2511.05510v1
• Date: Fri, 24 Oct 2025 13:11:47 GMT
• Title: TEMPO: Temporal Multi-scale Autoregressive Generation of Protein Conformational Ensembles
• Authors: Yaoyao Xu, Di Wang, Zihan Zhou, Tianshu Yu, Mingchen Chen,
• Abstract summary: We introduce a novel hierarchical autoregressive framework for modeling protein dynamics.<n>By bridging high-level biophysical principles with state-of-the-art generative modeling, our approach provides an efficient framework for simulating protein dynamics.
• Score: 8.6867067594535
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding the dynamic behavior of proteins is critical to elucidating their functional mechanisms, yet generating realistic, temporally coherent trajectories of protein ensembles remains a significant challenge. In this work, we introduce a novel hierarchical autoregressive framework for modeling protein dynamics that leverages the intrinsic multi-scale organization of molecular motions. Unlike existing methods that focus on generating static conformational ensembles or treat dynamic sampling as an independent process, our approach characterizes protein dynamics as a Markovian process. The framework employs a two-scale architecture: a low-resolution model captures slow, collective motions driving major conformational transitions, while a high-resolution model generates detailed local fluctuations conditioned on these large-scale movements. This hierarchical design ensures that the causal dependencies inherent in protein dynamics are preserved, enabling the generation of temporally coherent and physically realistic trajectories. By bridging high-level biophysical principles with state-of-the-art generative modeling, our approach provides an efficient framework for simulating protein dynamics that balances computational efficiency with physical accuracy.

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