Aligning sequence and structure representations leveraging protein domains for function prediction

Abstract

Protein function prediction is traditionally approached through sequence or structural modeling, often neglecting the effective fusion of diverse data sources. Protein domains, as functionally independent building blocks, determine a protein’s biological function, yet their potential has not been fully exploited in function prediction tasks. To address this, we introduce a modality-fused neural network leveraging function-aware domain embeddings as a bridge. We pre-train these embeddings by aligning domain semantics with Gene Ontology (GO) terms and textual descriptions. Additionally, we partition proteins into sub-views based on continuous domain regions for contrastive learning, supervised by a novel triplet InfoNCE loss. Our method outperforms state-of-the-art approaches across various benchmarks, and clearly differentiates proteins carrying distinct functions compared to the competitor.