ClathPLM: Deep multi-view feature extraction with CNN and attention enhances clathrin protein identification

Clathrin is a key structural protein in intracellular vesicle transport, mainly mediating clathrin-mediated endocytosis (CME) through trimeric assembly. Its functional abnormalities are closely associated with various diseases, including neurodegenerative disorders, tumor metastasis, and immune system dysregulation. Traditional experimental methods for identifying the presence of Clathrin have limitations such as high cost and time consumption. Therefore, it is particularly urgent to develop efficient and reliable computational methods to assist in Clathrin recognition. In this study, we propose a model named ClathPLM, which integrates sequence embeddings from three pre-trained protein language models (PPLMs), i.e., ProtT5, ProtBert, and ESM-3, and performs deep representation learning on each feature through an independent branch composed of a convolutional neural network (CNN) and a multi-head Attention (MHA) mechanism, finally fusing the representations of the three views to accomplish the classification task. To validate the effectiveness of this design, we further examined variants of the fusion strategy and attention mechanism. Evaluation results show that ClathPLM demonstrates excellent overall classification performance and robustness, surpassing current state-of-the-art methods. Moreover, the model performs strongly on an additional case-study dataset and shows good scalability on an extra vesicular transport proteins (VTPs) dataset. We anticipate that ClathPLM may contribute to a deeper understanding of the role of Clathrin in cellular regulation and disease mechanisms, and facilitate future biological studies as well as potential clinical applications.