PDB2Vec: Using 3D Structural Information for Improved Protein Analysis

In recent years, machine learning methods have shown remarkable results in various protein analysis tasks, including protein classification, folding prediction, and protein-to-protein interaction prediction. However, most studies focus only on the 3D structures or sequences for the downstream classification task. Hence analyzing the combination of both 3D structures and sequences remains comparatively unexplored. This study investigates how incorporating protein sequence and 3D structure information influences protein classification performance. We use two well-known datasets, STCRDAB and PDB Bind, for classification tasks to accomplish this. To this end, we propose an embedding method called PDB2Vec to encode both the 3D structure and protein sequence data to improve the predictive performance of the downstream classification task. We performed protein classification using three different experimental settings: only 3D structural embedding (called PDB2Vec), sequence embeddings using alignment-free methods from the biology domain including on k-mers, position weight matrix, minimizers and spaced k-mers, and the combination of both structural and sequence-based embeddings. Our experiments demonstrate the importance of incorporating both three-dimensional structural information and amino acid sequence information for improving the performance of protein classification and show that the combination of structural and sequence information leads to the best performance. We show that both types of information are complementary and essential for classification tasks.