ProtFAD: Introducing function-aware domains as implicit modality towards protein function perception

• URL: http://arxiv.org/abs/2405.15158v1
• Date: Fri, 24 May 2024 02:26:45 GMT
• Title: ProtFAD: Introducing function-aware domains as implicit modality towards protein function perception
• Authors: Mingqing Wang, Zhiwei Nie, Yonghong He, Zhixiang Ren,
• Abstract summary: We propose a function-aware domain representation and a domain-joint contrastive learning strategy to distinguish different protein functions. Our approach significantly and comprehensively outperforms the state-of-the-art methods on various benchmarks.
• Score: 0.3928425951824076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 associate domains with the GO terms as function priors 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.

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