Deep Multi-Modal Approach for Protein Function Prediction and Classification

Proteins play a crucial role in various biochemical activities, making accurate protein class and function prediction critical. Accurate protein function prediction is fundamental in computational biology for biological understanding. Relying solely on one modality for protein function prediction limits the model's ability to capture the protein's complete picture, potentially leading to inaccurate predictions. This paper addresses these challenges and presents a machine-learning model for precise protein class prediction, leveraging protein sequence, secondary structure, and interaction information. The model achieves a remarkable 100% accuracy in protein class prediction, highlighting the effectiveness of multimodal data. Addition-ally, GO notation prediction yields high accuracies of 96% for Biological Processes (BP), 97% for Cellular Components (CC), and 98% for Molecular Functions (MF). These results demonstrate the benefits of multimodal learning in capturing the intricate relationships within protein functions, enabling more accurate protein function annotation, and ultimately leading to characterization and downstream applications in biomedical research.