Sequence to Location: Protein Subcellular Localization Driven by Deep Pretrained Language Model

Proteins serve as the essential executors of cellular activities, and their mislocalization often resulting in diverse diseases. Traditional methods for determining protein subcellular localization are noted for their time-consuming, labor-intensive, and complex nature. To address these challenges, this study introduces SubLoc, a deep learning-based algorithm for predicting protein subcellular localization. The methodology comprises three key steps: Firstly, leveraging the deep pretrained protein language model ProtT5 to derive protein embedding vectors, thereby capturing intricate patterns and biological functionalities of protein sequences. Secondly, constructing a 3D protein structure graph model using amino acid residue contact relationships within the sequence, which is subsequently processed by a graph convolutional network to effectively manage spatial structural information. Lastly, employing a bidirectional gated recurrent unit and multi-head attention mechanism to analyze sequence features, integrating both structural and sequence data for enhanced subcellular localization prediction. Experimental results demonstrate that SubLoc exhibits exceptional performance in localizing proteins across 10 subcellular compartments, outperforming all comparative methods in terms of precision, recall, and MCC average values. Notably, SubLoc achieves particularly notable results in identifying Cytoplasm and Mitochondrion locations.