用条件生成模型设计能自组装成特定形态的肽类材料
Morphology-Aware Peptide Discovery via Masked Conditional Generative Modeling
- 基于掩码条件生成模型，根据肽的物理化学特征生成目标形态序列
- 在粗粒度分子模拟中验证，83%生成序列成功实现目标形貌
- 适合需要定向设计生物相容性材料的研究者
肽自组装预测为设计可大规模合成、生物相容且低毒性的材料提供了自下而上的强大策略，广泛应用于生物医学与能源领域。然而，在庞大的序列空间中筛选并分类聚集形貌仍极具挑战。我们提出PepMorph，一个端到端的肽发现流程，能够生成不仅易聚集，且其自组装被引导为纤维状或球状结构的新型肽序列，通过孤立肽的描述符（作为形貌代理）进行条件控制。为此，我们整合现有聚集倾向数据集，并提取几何与理化描述符构建新数据集。该数据集用于训练基于Transformer的条件变分自编码器（含掩码机制），可在任意条件下生成新肽序列。经设计规范过滤及粗粒度分子动力学（CG-MD）模拟验证，PepMorph在目标类别中达到83%的成功率，展现出面向应用的肽类材料设计潜力。
原文摘要 · Abstract (English)
Peptide self-assembly prediction offers a powerful bottom-up strategy for designing biocompatible, low-toxicity materials for large-scale synthesis in a broad range of biomedical and energy applications. However, screening the vast sequence space for categorization of aggregate morphology remains intractable. We introduce PepMorph, an end-to-end peptide discovery pipeline that generates novel sequences that are not only prone to aggregate but whose self-assembly is steered toward fibrillar or spherical morphologies by conditioning on isolated peptide descriptors that serve as morphology proxies. To this end, we compiled a new dataset by leveraging existing aggregation propensity datasets and extracting geometric and physicochemical descriptors. This dataset is then used to train a Transformer-based Conditional Variational Autoencoder with a masking mechanism, which generates novel peptides under arbitrary conditioning. After filtering to ensure design specifications and validation of generated sequences through coarse-grained molecular dynamics (CG-MD) simulations, PepMorph yielded 83% success rate under our CG-MD validation protocol and morphology criterion for the targeted class, showcasing its promise as a framework for application-driven peptide discovery.
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