用噪声空间加速蛋白构象采样，兼具速度与泛化能力
JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensembles
- 在噪声空间中进行分子动力学，结合跳跃采样框架
- 小肽构象生成速度比传统MD快10倍以上
- 保留物理先验，支持跨数据集泛化，长于训练长度的肽也能处理
蛋白质构象集合对理解蛋白功能及新型药物发现（如隐匿口袋）至关重要。现有采样方法如分子动力学（MD）计算效率低，而多数机器学习方法难以迁移至训练数据之外的系统。本文提出JAMUN，通过在全原子3D构象的平滑噪声空间中执行分子动力学，采用走-跳采样框架，实现小肽构象集合的高效生成。相比传统分子动力学，速度提升一个数量级。模型所含物理先验使其具备良好迁移能力，可应用于训练数据外的系统，甚至超过训练长度的肽链。代码与权重已开源：https://github.com/prescient-design/jamun。
原文摘要 · Abstract (English)
Conformational ensembles of protein structures are immensely important both for understanding protein function and drug discovery in novel modalities such as cryptic pockets. Current techniques for sampling ensembles such as molecular dynamics (MD) are computationally inefficient, while many recent machine learning methods do not transfer to systems outside their training data. We propose JAMUN which performs MD in a smoothed, noised space of all-atom 3D conformations of molecules by utilizing the framework of walk-jump sampling. JAMUN enables ensemble generation for small peptides at rates of an order of magnitude faster than traditional molecular dynamics. The physical priors in JAMUN enables transferability to systems outside of its training data, even to peptides that are longer than those originally trained on. Our model, code and weights are available at https://github.com/prescient-design/jamun.
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