MKFGO: integrating multi-source knowledge fusion with pretrained language model for high-accuracy protein function prediction.

Accurately identifying protein functions is essential to understand life mechanisms and thus advance drug discovery. Although biochemical experiments are the gold standard for determining protein functions, they are often time-consuming and labor-intensive. Here, we proposed a novel composite deep-learning method, Multi-source Knowledge Fusion for Gene Ontology prediction (MKFGO), to infer Gene Ontology (GO) attributes through integrating five complementary pipelines built on multi-source biological data. MKFGO was rigorously benchmarked on 1522 nonredundant proteins, demonstrating superior performance over 12 state-of-the-art function prediction methods. Comprehensive data analyses revealed that the major advantage of MKFGO lies in its two deep-learning components, handcrafted feature representation-based GO prediction (HFRGO) and protein large language model (PLM)-based GO prediction (PLMGO), which derive handcrafted features and PLM-based features, respectively, from protein sequences in different biological views, with effective knowledge fusion at the decision-level. HFRGO leverages a long short-term memory (LSTM)-attention network embedded with handcrafted features, in which the triplet loss-based guilt-by-association strategy is designed to enhance the correlation between feature similarity and function similarity. PLMGO employs the PLM to capture feature embeddings with discriminative functional patterns from sequences. Meanwhile, another three components provide complementary insights for further improving prediction accuracy, driven by protein-protein interaction, GO term probability, and protein-coding gene sequence, respectively. The source codes and models of MKFGO are freely available at https://github.com/yiheng-zhu/MKFGO.