The machine learning techniques are playing a major role in the field of immunoinformatics for DNA-binding domain analysis. Functional analysis of the binding ability of DNA-binding domain protein antigen peptides to major histocompatibility complex (MHC) class molecules is important in vaccine development. The variable length of each binding peptide complicates this prediction. Such predictions can be used to select epitopes for use in rational vaccine design and to increase the understanding of roles of the immune system in infectious diseases. Antigenic epitopes of DNA-binding domain protein form Human papilloma virus-31 are important determinant for protection of many host form viral infection. This study shows active part in host immune reactions and involvement of MHC class-I and MHC II in response to almost all antigens. We used PSSM and SVM algorithms for antigen design, which represented predicted binders as MHCII-IAb, MHCII-IAd, MHCII-IAg7, and MHCII- RT1.B nonamers from viral DNA-binding domain crystal structure. These peptide nonamers are from a set of aligned peptides 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.

Phosphoproteomics is the global analysis of protein phosphorylation, holds great promise for the discovery of cell signaling events that link changes in dynamics of protein phosphorylation to the progression of various diseases, particularly cancer and diabetes. Proteomic research first came for research with the introduction of two-dimensional gel electrophoresis. Proteomics has been increasingly applied to oncology research with the widespread introduction of mass spectrometry and protein-chip. Applying proteomics to foster an improved understanding of cancer pathogenesis develop new tumor biomarkers for diagnosis, and early detection using proteomic portrait of samples. The study of Phospho-onco-proteomics provides a better understanding of cancer diagnosis.