Protriever：用于适应性预测的端到端可微蛋白质同源性搜索
Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction
AI总结：
本文提出端到端可微框架Protriever，将同源蛋白检索与下游适应性预测联合训练，在达到SOTA性能的同时借助向量搜索提速两个数量级。
摘要：
检索同源蛋白质序列对于适应性预测、蛋白质设计、结构建模以及蛋白质-蛋白质相互作用等广泛的蛋白质建模任务至关重要。传统工作流程依赖于两步过程：首先通过多序列比对（MSA）检索同源物，然后在一个或多个此类比对上训练模型。然而，基于MSA的检索计算成本高昂，难以处理高度分化的序列或复杂的插入与缺失模式，并且其运行独立于下游建模目标。我们提出Protriever，这是一种端到端可微框架，能够学习检索相关同源物，同时针对目标任务进行训练。当应用于蛋白质适应性预测时，与依赖基于MSA同源物检索的序列模型相比，Protriever取得了最先进的性能，并通过高效向量搜索将速度提升两个数量级。Protriever既不依赖特定架构，也不依赖特定任务，并能在推理时灵活适应不同的检索策略和蛋白质数据库，为以比对为中心的方法提供了一种可扩展的替代方案。
英文摘要：
Retrieving homologous protein sequences is essential for a broad range of protein modeling tasks such as fitness prediction, protein design, structure modeling, and protein-protein interactions. Traditional workflows have relied on a two-step process: first retrieving homologs via Multiple Sequence Alignments (MSA), then training models on one or more of these alignments. However, MSA-based retrieval is computationally expensive, struggles with highly divergent sequences or complex insertions & deletions patterns, and operates independently of the downstream modeling objective. We introduce Protriever, an end-to-end differentiable framework that learns to retrieve relevant homologs while simultaneously training for the target task. When applied to protein fitness prediction, Protriever achieves state-of-the-art performance compared to sequence-based models that rely on MSA-based homolog retrieval, while being two orders of magnitude faster through efficient vector search. Protriever is both architecture- and task-agnostic, and can flexibly adapt to different retrieval strategies and protein databases at inference time -- offering a scalable alternative to alignment-centric approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
