  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.
