DeepCR: predicting cytokine receptor proteins through pretrained language models and deep learning networks.

Abstract

Cytokine receptors play a pivotal role in mediating the immune response and are critical in cytokine storms, which underlie the pathogenesis of conditions such as acute respiratory distress syndrome (ARDS) and autoimmune disorders. Identifying cytokine receptors is essential for understanding their biological functions, exploring therapeutic targets, and guiding clinical interventions. Traditional biochemical methods to identify cytokine receptors are labor-intensive, costly, and time-consuming, prompting the need for more efficient alternatives. Recent advances in computational biology have enabled the use of machine learning to classify cytokine receptor proteins. Most existing approaches focused on homologous features and protein composition to classify cytokine families, but no dedicated studies have been conducted on cytokine receptor proteins. This gap presents an opportunity to develop a method specifically for classifying cytokine receptors among other membrane proteins. In this study, we present a novel classification framework combining pre-trained language models (PLMs) with a multi-window convolutional neural network (mCNN) architecture for the fast and accurate identification of cytokine receptor proteins. PLMs, such as ProtTrans and ESM variants, capture biochemical context directly from raw protein sequences, while mCNN efficiently extracts local and global sequence patterns using convolutional layers with varying window sizes. Our model achieved an AUC of 0.96 in the training as well as 0.97 and 0.93 in two independent tests, demonstrating its effectiveness in distinguishing cytokine receptors from non-cytokine receptor proteins. By eliminating the need for manual feature extraction, this approach offers a robust and scalable solution for protein classification, paving the way for its application in drug discovery and understanding cytokine-mediated diseases.