动态蛋白质词汇的蛋白质设计
Protein Design with Dynamic Protein Vocabulary
AI总结：
针对生成模型在蛋白质设计中结构合理性不足的问题，提出ProDVa方法，动态整合天然蛋白质片段，在极少量训练数据下实现功能对齐并显著提升折叠质量。
摘要：
蛋白质设计是生物技术中的一个基本挑战，旨在广阔的可能蛋白质空间中设计具有特定功能的新序列。近期深度生成模型的进展使得基于文本描述的功能性蛋白质设计成为可能，但此类模型在结构合理性方面仍存在困难。受利用天然蛋白质结构的经典蛋白质设计方法的启发，我们探讨了将天然蛋白质片段纳入生成模型是否能增强其可折叠性。我们的实证结果表明，即使是随机纳入片段也能提高可折叠性。基于这一发现，我们提出了ProDVa，一种新颖的蛋白质设计方法，它集成了用于功能描述文本编码器、用于设计蛋白质的蛋白质语言模型，以及一个根据文本功能描述动态检索蛋白质片段的片段编码器。实验结果表明，我们的方法能有效设计出既功能对齐又结构合理的蛋白质序列。与最先进的模型相比，ProDVa在仅使用不到0.04%训练数据的情况下实现了相当的功能对齐，同时设计出显著更多折叠良好的蛋白质，其中pLDDT高于70的蛋白质比例增加了7.38%，PAE低于10的蛋白质比例增加了9.6%。
英文摘要：
Protein design is a fundamental challenge in biotechnology, aiming to design novel sequences with specific functions within the vast space of possible proteins. Recent advances in deep generative models have enabled function-based protein design from textual descriptions, yet struggle with structural plausibility. Inspired by classical protein design methods that leverage natural protein structures, we explore whether incorporating fragments from natural proteins can enhance foldability in generative models. Our empirical results show that even random incorporation of fragments improves foldability. Building on this insight, we introduce ProDVa, a novel protein design approach that integrates a text encoder for functional descriptions, a protein language model for designing proteins, and a fragment encoder to dynamically retrieve protein fragments based on textual functional descriptions. Experimental results demonstrate that our approach effectively designs protein sequences that are both functionally aligned and structurally plausible. Compared to state-of-the-art models, ProDVa achieves comparable function alignment using less than 0.04% of the training data, while designing significantly more well-folded proteins, with the proportion of proteins having pLDDT above 70 increasing by 7.38% and those with PAE below 10 increasing by 9.6%.
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