Project title {A computational approach to identify and design multi-functional therapeutic peptides with optimum cytotoxicity.}
A computational approach to identify and design multi-functional therapeutic peptides with optimum cytotoxicity.Over the last decade, peptide drug approval has steadily increased. Peptides are recognized for high selectivity, better stability, low production cost, low toxicity and good efficacy. Furthermore, it has more potential to act as an inhibitor or activator of protein-protein interactions (PPIs) than small molecules. PPIs are associated with different human diseases. Already, peptide drug has been used to treat cancer, diabetes, HIV infection and so on. Therapeutic peptides possess a wide range of bioactivity, including anti-cancer, anti-bacterial, anti-microbial, cell penetration etc. Furthermore, therapeutic peptides are often multifunctional. However, most existing computational studies have focused on single-functional therapeutic peptides, whereas there is limited study on the multifunctional therapeutic peptide. In this study, we will propose a deep-learning model to identify and design multifunctional therapeutic peptides with optimum cytotoxicity.
Center for Computational Engineering and Networking, Coimbatore
The successful PhD candidate must have a passion for computational research and knowledge in machine learning, deep learning, and python programming. B-Tech or M-Tech in computer science / bioinformatics / computational science / computational engineering / biotechnology is required.