深度生成式蛋白质设计中的挑战与指南：四个案例研究
Challenges and Guidelines in Deep Generative Protein Design: Four Case Studies
AI总结：
提出基于Score Matching和Flow Matching的深度生成流程用于de novo蛋白质设计，在四个蛋白质家族上验证了其生成结构合理性与功能稳定性，为整合生成模型至蛋白质设计提供实用指南。
摘要：
深度生成模型在de novo蛋白质设计方面展现出潜力，但可靠地生成几何上合理、进化上一致、功能上相关且动态稳定的结构仍具挑战。我们提出了一种基于Score Matching和Flow Matching的深度生成建模流程，用于单体的早期de novo设计。我们将该流程应用于四个不同的蛋白质家族，并采用可适应的评估协议。生成的结构显示出无冲突的逼真构象，并富含家族特异性特征，同时设计的序列保留了关键的功能残基并维持了变异性。分子动力学和结合模拟表明其具有动态稳定性，具备类似野生型的结合口袋，能与家族特异性配体产生良好相互作用。这些结果为将生成模型整合到蛋白质设计工作流程中提供了实用指南。
英文摘要：
Deep generative models show promise for $\textit{de novo}$ protein design, yet reliably producing designs that are geometrically plausible, evolutionarily consistent, functionally relevant, and dynamically stable remains challenging. We present a deep generative modeling pipeline for early $\textit{de novo}$ design of monomeric proteins, based on Score Matching and Flow Matching. We apply this pipeline to four diverse protein families with an adaptable evaluation protocol. Generated structures display realistic, clash-free conformations enriched with family-specific features, while the designed sequences preserve essential functional residues while retaining variability. Molecular dynamics and binding simulations show dynamic stability, with wild-type-like binding pockets that interact favorably with family-specific ligands. These results provide practical guidelines for integrating generative models into protein design workflows.
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