Deep learning approaches have spurred substantial advances in the single-state prediction of biomolecular structures. The function of biomolecules is, however, dependent on the range of conformations they can assume. This is especially true for peptides, a highly flexible class of molecules that are involved in numerous biological processes and are of high interest as therapeutics. Here we introduce PepFlow, a transferable generative model that enables direct all-atom sampling from the allowable conformational space of input peptides. We train the model in a diffusion framework and subsequently use an equivalent flow to perform conformational sampling. To overcome the prohibitive cost of generalized all-atom modelling, we modularize the generation process and integrate a hypernetwork to predict sequence-specific network parameters. PepFlow accurately predicts peptide structures and effectively recapitulates experimental peptide ensembles at a fraction of the running time of traditional approaches. PepFlow can also be used to sample conformations that satisfy constraints such as macrocyclization.

深度学习方法极大地促进了生物分子结构单态预测的进步。然而，生物分子的功能取决于它们可以呈现的构象范围。对于肽来说尤其如此，肽是一类高度灵活的分子，参与许多生物过程，并且作为治疗药物受到高度关注。在这里，我们介绍 PepFlow，一种可转移的生成模型，可以从输入肽的允许构象空间直接进行全原子采样。我们在扩散框架中训练模型，然后使用等效流程来执行构象采样。为了克服广义全原子建模的高昂成本，我们将生成过程模块化并集成超网络来预测序列特定的网络参数。 PepFlow 可以准确预测肽结构，并有效地重现实验肽整体，而运行时间仅为传统方法的一小部分。 PepFlow 还可用于对满足大环化等约束的构象进行采样。