KEGCL: Knowledge-Enhanced Graph Contrastive Learning for Protein Complex Identification.

Abstract

Protein complexes play essential roles in cellular functions, and accurate identification of these complexes is critical for understanding biological processes and disease mechanisms. Existing methods frequently compromise the global topology of protein-protein interaction (PPI) networks when incorporating biological resources. Moreover, they fail to adequately address the intrinsic sparsity of PPI data and the widespread occurrence of false positives and false negatives. These approaches also struggle to capture the diverse neighborhood dependencies necessary to represent distinct functional roles of proteins within complexes. To address these limitations, we propose a knowledge-enhanced graph contrastive learning (KEGCL) framework for protein complex identification. KEGCL constructs a knowledge-enhanced PPI network by integrating external biological priors. A perturbation strategy guided by spatiotemporal constraints is then applied to selectively reintroduce functionally relevant interactions, thereby enhancing semantic diversity in the generated graph views. Based on this, graph convolutional encoders with randomized propagation depths are used to capture protein interaction patterns at multiple structural levels, enhancing the model's ability to represent both densely connected cores and loosely associated attachments within protein complexes. Extensive experiments on multiple real-world PPI datasets show that KEGCL achieves competitive performance compared with state-of-the-art methods, and enrichment analyses confirm the biological relevance of the identified complexes.