Machine Learning Techniques: Approach for Mapping of MHC Class Binding Nonamers

The machine learning techniques are playing a major role in the field ofimmunoinformatics for DNA-binding domain analysis. Functional analysis of the binding ability ofDNA-binding domain protein antigen peptides to major histocompatibility complex (MHC) classmolecules is important in vaccine development. The variable length of each binding peptidecomplicates this prediction. Such predictions can be used to select epitopes for use in rationalvaccine design and to increase the understanding of roles of the immune system in infectiousdiseases. Antigenic epitopes of DNA-binding domain protein form Human papilloma virus-31 areimportant determinant for protection of many host form viral infection. This study shows activepart in host immune reactions and involvement of MHC class-I and MHC II in response to almostall antigens. We used PSSM and SVM algorithms for antigen design, which representedpredicted binders as MHCII-IAb, MHCII-IAd, MHCII-IAg7, and MHCII- RT1.B nonamers from viralDNA-binding domain crystal structure. These peptide nonamers are from a set of alignedpeptides known to bind to a given MHC molecule as the predictor of MHC-peptide binding.Analysis shows potential drug targets to identify active sites against diseases.