Predicting the Functional Changes in Protein Mutations Through the Application of BiLSTM and the Self-Attention Mechanism

Abstract

In the field of bioinformatics, changes in protein functionality are mainly influenced by protein mutations. Accurately predicting these functional changes can enhance our understanding of evolutionary mechanisms, promote developments in protein engineering-related fields, and accelerate progress in medical research. In this study, we introduced two different models: one based on bidirectional long short-term memory (BiLSTM), and the other based on self-attention. These models were integrated using a weighted fusion method to predict protein functional changes associated with mutation sites. The findings indicate that the model's predictive precision matches that of the current model, along with its capacity for generalization. Furthermore, the ensemble model surpasses the performance of the single models, highlighting the value of utilizing their synergistic capabilities. This finding may improve the accuracy of predicting protein functional changes associated with mutations and has potential applications in protein engineering and drug research. We evaluated the efficacy of our models under different scenarios by comparing the predicted results of protein functional changes across various numbers of mutation sites. As the number of mutation sites increases, the prediction accuracy decreases significantly, highlighting the inherent limitations of these models in handling cases involving more mutation sites.