Enhanced Protein Secondary Structure Prediction Through Multi-View Multi-Feature Evolutionary Deep Fusion Method

Understanding protein secondary structure is crucial for analyzing their functions and designing drugs. Accurately predicting its eight states is both vital and challenging. Despite numerous proposed methods, research on representing proteins from multiple views and features remains limited. Moreover, effectively selecting and fusing this information remains an unresolved issue, which hampers prediction accuracy. In this paper, the Multi-View Multi-Feature Evolutionary Deep Fusion (MVMF-EDF) method, which consists of two stages, is proposed to address this challenge. First, a multi-view multi-feature (MVMF) strategy is introduced to construct protein representations from various views and features. Notably, MVMF-EDF incorporates semantic information from a pre-trained language model to enhance protein representation. Second, an evolutionary deep fusion (EDF) approach is employed to automatically select and fuse the extracted features. Experimental results on four benchmark datasets show that MVMF-EDF outperforms baseline models in predicting protein secondary structure. This approach leverages deep neural networks for feature extraction and evolutionary algorithms for feature integration, achieving high accuracy without requiring manual feature fusion design.