DMPPred: a tool for identification of antigenic regions responsible for inducing type 1 diabetes mellitus.
Nishant KumarSumeet PatiyalShubham ChoudhuryRitu TomerAnjali DhallGajendra Pal Singh RaghavaPublished in: Briefings in bioinformatics (2022)
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 analysed 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. First, we developed a similarity/alignment based method using Basic Local Alignment Search Tool and achieved a high probability of correct hits with poor coverage. Second, we developed an alignment-free method using machine learning techniques and got a maximum AUROC of 0.89 using dipeptide composition. Finally, we developed a hybrid method that combines the strength of both alignment free and alignment-based methods and achieves maximum area under the receiver operating characteristic of 0.95 with Matthew's correlation coefficient of 0.81 on an independent dataset. We developed a web server 'DMPPred' and stand-alone server for predicting, designing and scanning T1DM associated peptides (https://webs.iiitd.edu.in/raghava/dmppred/).
Keyphrases
- glycemic control
- amino acid
- type diabetes
- high resolution
- induced apoptosis
- magnetic resonance imaging
- cardiovascular disease
- multiple sclerosis
- dendritic cells
- oxidative stress
- metabolic syndrome
- adipose tissue
- cardiovascular risk factors
- mass spectrometry
- weight loss
- cell cycle arrest
- skeletal muscle
- health insurance