In Silico Perspectives on the Prediction of the PLP's Epitopes involved in Multiple Sclerosis.

BACKGROUND: Multiple sclerosis (MS) is the most common autoimmune disease of the central nervous system (CNS). The main cause of the MS is yet to be revealed, but the most probable theory is based on the molecular mimicry that concludes some infections in the activation of T cells against brain auto-antigens that initiate the disease cascade.

OBJECTIVES: The Purpose of this research is the prediction of the auto-antigen potency of the myelin proteolipid protein (PLP) in multiple sclerosis.

MATERIALS AND METHODS: As there wasn't any tertiary structure of PLP available in the Protein Data Bank (PDB) and in order to characterize the structural properties of the protein, we modeled this protein using prediction servers. Meta prediction method, as a new perspective in silico , was performed to fi nd PLPs epitopes. For this purpose, several T cell epitope prediction web servers were used to predict PLPs epitopes against Human Leukocyte Antigens (HLA). The overlap regions, as were predicted by most web servers were selected as immunogenic epitopes and were subjected to the BLASTP against microorganisms.

RESULTS: Three common regions, AA58-74 , AA161-177 , and AA238-254 were detected as immunodominant regions through meta-prediction. Investigating peptides with more than 50% similarity to that of candidate epitope AA58-74 in bacteria showed a similar peptide in bacteria (mainly consistent with that of clostridium and mycobacterium) and spike protein of Alphacoronavirus 1, Canine coronavirus, and Feline coronavirus. These results suggest that cross reaction of the immune system to PLP may have originated from a bacteria or viral infection, and therefore molecular mimicry might have an important role in the progression of MS.

CONCLUSIONS: Through reliable and accurate prediction of the consensus epitopes, it is not necessary to synthesize all PLP fragments and examine their immunogenicity experimentally ( in vitro ). In this study, the best encephalitogenic antigens were predicted based on bioinformatics tools that may provide reliable results for researches in a shorter time and at a lower cost.