Identification of Antigenic Regions Responsible for inducing Type 1 diabetes mellitus

There are a number of antigens that induce autoimmune response against β-cells, leading to Type 1 diabetes mellitus (T1DM). Recently several antigen-specific immunotherapies have been developed to treat T1DM. Thus identification of T1DM associated peptides with antigenic regions or epitopes is important for peptide based-therapeutics (e.g., immunotherapeutic). In this study, for the first time an attempt has been made to develop a method for predicting, designing and scanning of T1DM associated peptides with high precision. We analyzed 815 T1DM associated peptides and observed that these peptides are not associated with a specific class of HLA alleles. Thus, HLA binder prediction methods are not suitable for predicting T1DM associated peptides. Firstly, we developed a similarity/alignment based method using BLAST and achieved a high probability of correct hits with poor coverage. Secondly, we developed an alignment free method using machine learning techniques and got maximum AUROC 0.89 using dipeptide composition. Finally, we developed a hybrid method that combines the strength of both alignment free and alignment based methods and achieve maximum AUROC 0.95 with MCC 0.81 on independent dataset. We developed a webserver “DMPPred” and standalone server, for predicting, designing and scanning of T1DM associated peptides ( https://webs.iiitd.edu.in/raghava/dmppred/ ). Key Points Prediction of peptides responsible for inducing immune system against β-cells Compilation and analysis of Type 1 diabetes associated HLA binders BLAST based similarity search against Type 1diabetes associated peptides Alignment free method using machine learning techniques and composition A hybrid method using alignment free and alignment based approach Author’s Biography Nishant Kumar is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India Sumeet Patiyal is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India Shubham Choudhury is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India Ritu Tomer is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India Anjali Dhall is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. Gajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.