Enhancing Intrinsically Disordered Region Identification in Proteins: A BERT-Based Deep Learning Approach

Intrinsically Disordered Regions (IDRs) are pivotal to understanding protein functionality in cellular processes, with significant implications in drug discovery and structural biology. These regions are recognized for their roles in Amino acids Relations, PTMs and phase separations. However, traditional experimental methods for identifying IDRs are time-consuming and resource-intensive, while current machine-learning approaches often need to improve with scalability and precision across diverse and extensive datasets. In response to this challenge, a novel deep learning framework is introduced, leveraging pre-trained BERT to predict the location of IDRs within protein sequences accurately. Leveraging advanced language models tailored for amino acid sequence complexity, the proposed model enhances prediction accuracy and efficiency. The approach is benchmarked against existing deep learning methodologies shown 0.2965 of MCC and 0.7291 of AUC for a comprehensive evaluation. The results highlight the model's superiority in identifying IDRs with high reliability, establishing a new standard in computational protein analysis. The research propels the computational identification of IDRs toward the potential development of novel therapeutic interventions.