ProtFAD：引入功能感知结构域作为蛋白质功能预测的隐式模态
ProtFAD: Introducing function-aware domains as implicit modality towards protein function prediction
AI总结：
针对蛋白质序列与结构建模瓶颈，本文提出 ProtFAD，利用功能感知结构域作为隐式模态，并通过语义对齐预训练与三元组 InfoNCE 对比学习提升蛋白质功能预测性能。
摘要：
蛋白质功能预测目前通过编码其序列或结构来实现，其中序列到功能的跨越以及高质量结构数据的稀缺导致了明显的性能瓶颈。蛋白质结构域是蛋白质中功能独立的“构建模块”，其组合决定了多样的生物学功能。然而，现有大多数研究尚未充分挖掘蛋白质结构域中包含的复杂功能信息。为填补这一空白，我们提出了一种协同整合方法，用于功能感知的结构域表示，并提出结构域联合对比学习策略，以在对齐模态的同时区分不同的蛋白质功能。具体而言，我们将结构域语义与 GO 术语和文本描述对齐，以预训练结构域嵌入。此外，我们基于连续的联合结构域将蛋白质划分为多个子视图，在一种新颖的三元组 InfoNCE 损失监督下进行对比训练。我们的方法在多个基准上显著且全面地优于最先进方法，并且与竞争方法相比，能够清晰地区分携带不同功能的蛋白质。我们的实现可在 https://github.com/AI-HPC-Research-Team/ProtFAD 获取。
英文摘要：
Protein function prediction is currently achieved by encoding its sequence or structure, where the sequence-to-function transcendence and high-quality structural data scarcity lead to obvious performance bottlenecks. Protein domains are "building blocks" of proteins that are functionally independent, and their combinations determine the diverse biological functions. However, most existing studies have yet to thoroughly explore the intricate functional information contained in the protein domains. To fill this gap, we propose a synergistic integration approach for a function-aware domain representation, and a domain-joint contrastive learning strategy to distinguish different protein functions while aligning the modalities. Specifically, we align the domain semantics with GO terms and text description to pre-train domain embeddings. Furthermore, we partition proteins into multiple sub-views based on continuous joint domains for contrastive training under the supervision of a novel triplet InfoNCE loss. Our approach significantly and comprehensively outperforms the state-of-the-art methods on various benchmarks, and clearly differentiates proteins carrying distinct functions compared to the competitor. Our implementation is available at https://github.com/AI-HPC-Research-Team/ProtFAD.
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。
