关键词: 多级酶功能预测、多语义深度特征学习、大模型词嵌入、多视图特征融合

Abstract: Proteins play a crucial role in biological activities, with enzymes, as significant proteins, being widely utilized across various fields due to their catalytic functions. However, verifying enzyme functions through biochemical experiments is both time-consuming and expensive. Traditional methods for enzyme function annotation primarily rely on sequence similarity, which proves ineffective when the target enzyme sequence significantly differs from known enzymes. Recently, researchers have begun to explore deep learning-based methods, but these approaches are constrained by traditional enzyme sequence encoding methods and typically utilize only a single perspective or level of information. Consequently, these models face limitations when addressing enzymes with complex structures or diverse functions. To overcome these challenges, this paper proposes a novel full-domain cross-semantic fusion multi-level enzyme function prediction method (GCMEFP). Specifically, the proposed method employs two state-of-the-art protein macrolanguage models for sequence word embedding learning. Additionally, it constructs a multi-semantic deep feature learning module that utilizes a convolutional neural network to build a semantic pyramid, enabling the extraction of semantic information at various levels. Furthermore, the method introduces a full-domain cross-view semantic fusion module to explore hidden interaction information between different views and eliminate redundant information, thereby enhancing the model's generalization. Experimental results demonstrate that the proposed GCMEFP achieves 89.6% accuracy on the benchmark dataset, which is 4.8% higher than the existing optimal method, and 55% accuracy on the independent test set New-379, which is 14% higher than the existing optimal method.

Key words: multi-level enzyme function prediction, multi-view deep feature learning, large language model word embedding, multi-view feature aggregation