用生成模型预测高分子分子在不同温度下的构象集合。
Polyformer: a generative framework for thermodynamic modeling of polymeric molecules
- 基于序列和温度生成符合热力学分布的分子构象。
- 首次同时解决折叠路径、构象集合及温度依赖变化问题。
- 适用于蛋白结构域，结果与分子动力学模拟高度一致。
经典生物结构范式认为，生物分子（如蛋白质、核酸、脂质等）的序列决定其构象，构象决定功能。AlphaFold 等程序通过预测单一最优构象来应对这一范式。然而，生物分子并非静态，其构象集合才真正决定功能。我们提出 Polyformer——一种用于高分子分子热力学建模的生成框架。给定序列和温度（或其他热力学变量），Polyformer 能生成符合分子真实热力学构象集合的构象。它是首个同时解决三个核心问题的生成模型：分子如何折叠、其构象集合是什么、以及当物理温度变化时构象集合如何演化。以含 50–111 个残基的蛋白结构域为具体案例，模型预测与分子动力学（MD）轨迹表现出良好一致性。
原文摘要 · Abstract (English)
The classic paradigm of structural biology is that the sequence of a biomolecule (protein, nucleic acid, lipid, etc) determines its conformation (shape) which determines its biological function. Protein folding programs like AlphaFold address this paradigm by predicting the single best conformation given a sequence that defines the molecule. However, biomolecules are not static structures, and their conformational ensemble determines their function. We present the Polyformer -- a generative framework for thermodynamic modeling of polymeric molecules. Given the sequence and temperature (or another thermodynamic variable), the Polyformer generates conformations faithful to the molecule's thermodynamic conformational ensemble. It is the first generative model that solves three problems simultaneously: how does a molecule fold, what is its conformational ensemble, and how does the conformational ensemble change as we change physical temperature. As a concrete test case, we apply Polyformer to protein domains with 50-111 residues and report good agreement of model predictions to Molecular Dynamics (MD) trajectories.
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