Deep learning approach for secondary structure prediction of Pseudomonas aeruginosa –Quorum sensing repressor

Accurate prediction of Protein Secondary Structure (PSS) plays a crucial role in understanding the functional mechanisms of proteins. This study focuses on predicting the secondary structure of the quorum-sensing control repressor protein (QscR) using a UNet based deep learning model. The UNet architecture, known for its exceptional performance is adapted to predict structural features of proteins by learning from sequence based data. The proposed model was trained and validated using benchmark protein datasets to ensure generalizability and accuracy. Comparative analysis with traditional approaches demonstrated that the UNet model achieved superior performance in terms of prediction accuracy and computational efficiency. The findings suggest that the UNet model is a robust tool for SS prediction and can provide deeper insights into quorum-sensing mechanisms, aiding in the design of novel antibacterial strategies.