PLM-ATG: Identification of Autophagy Proteins by Integrating Protein Language Model Embeddings with PSSM-Based Features.

Autophagy critically regulates cellular development while maintaining pathophysiological homeostasis. Since the autophagic process is tightly regulated by the coordination of autophagy-related proteins (ATGs), precise identification of these proteins is essential. Although current computational approaches have addressed experimental recognition's costly and time-consuming challenges, they still have room for improvement since handcrafted features inadequately capture the intricate patterns and relationships hidden in sequences. In this study, we propose PLM-ATG, a novel computational model that integrates support vector machines with the fusion of protein language model (PLM) embeddings and position-specific scoring matrix (PSSM)-based features for the ATG identification. First, we extracted sequence-based features and PSSM-based features as the inputs of six classifiers to establish baseline models. Among these, the combination of the SVM classifier and the AADP-PSSM feature set achieved the best prediction accuracy. Second, two popular PLM embeddings, i.e., ESM-2 and ProtT5, were fused with the AADP-PSSM features to further improve the prediction of ATGs. Third, we selected the optimal feature subset from the combination of the ESM-2 embeddings and AADP-PSSM features to train the final SVM model. The proposed PLM-ATG achieved an accuracy of 99.5% and an MCC of 0.990, which are nearly 5% and 0.1 higher than those of the state-of-the-art model EnsembleDL-ATG, respectively.