Development of Predictive Model for Helper T Lymphocyte Epitope Binding to HLADRB1* 01:01

Abstract

Epitopes are essential peptides for immune system stimulation, such as governing helper T lymphocyte (HTL) activation via antigen presentation and recognition. Current predictive models for epitope selection mainly rely on the antigen presentation, although HTLs only recognize 50% of the presented peptides. Thus, we developed a HTL epitope predictor which involves the antigen recognition step. The predictor is specific for epitopes presented by Human Leukocyte Allele (HLA)-DRB1*01:01, which is protective against developing multiple sclerosis and association with autoimmune diseases. As the data set, we used binding register of immunogenic and non-immunogenic HTL peptides related to HLA-DRB1*01:01. The binding registers were obtained from consensus results of two current HLA-binder predictors. Amino acid descriptors were extracted from the binding registers and subjected to random forest algorithm. A threshold optimization were applied to overcome data set imbalance class. In addition, descriptors were screened by using a recursive feature elimination to enhance the model performance. The obtained model shows that the hydrophobicity, steric, and electrostatic properties of epitopes, mainly at center of binding registers, are important for the TCR recognition as well as the HTL epitopes predictive model. The model complements current HLA-DRB1*01:01-binder prediction methods to screen immunogenic HTL epitopes.