Multi-View Collaboration Feature Fusion for Protein Function Prediction

With the rapid growth of high-throughput sequencing data, many proteins remain uncharacterized, while experimental validation is costly and time-consuming. Automatic Function Prediction (AFP) is thus urgently needed. Protein functions are complex and multilevel, with inherent interactions among features such as sequence, structure, and evolution. Existing methods relying on single-level representations or simple feature aggregation struggle to capture the hierarchical dependencies and semantic collaborative relationships in the Gene Ontology (GO) label system, limiting prediction accuracy and generalization. To overcome these challenges, we propose a Multi-View Collaboration Feature Fusion (MVCFF) framework, which leverages complementary features from multiple sequence perspectives to enhance protein function prediction. In MVCFF, a sequential feature extraction subnetwork is designed to capture view-specific information, incorporating both local patterns and long-range dependencies within amino acid sequences. Building on this, a multi-view collaboration paradigm is employed, enabling interactive learning of key positional information through integrated multi-view features and facilitating synergistic information fusion. The resulting multi-view representations are then fed into downstream label predictors to perform classification tasks. To further boost predictive accuracy, we introduce an extended version, MVCFF+, which combines the original MVCFF framework with sequence-similarity-based prediction methods via a weighted fusion strategy. Extensive experiments demonstrate that our approach substantially improves prediction performance, outperforming existing methods by a clear margin. The source code is publicly available at https://github.com/AGI-FBHC/MVCFF.