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A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
Published on: March 25, 2014
In Silico Perspectives on the Prediction of the PLP's Epitopes involved in Multiple Sclerosis
Zahra Zamanzadeh1, Mitra Ataei1, Seyed Massood Nabavi2
1Department of medical biotechnology. Institute of Medical Genetic, National Institute of Genetics Engineering and Biotechnology (NIGEB), Tehran, 14965/161 Iran.
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.
Insights
This study predicts myelin proteolipid protein (PLP) epitopes in multiple sclerosis (MS) using bioinformatics. Findings suggest infections may trigger MS via molecular mimicry, offering a faster, cheaper diagnostic approach.
Area of Science:
- Neuroimmunology
- Computational Biology
- Autoimmune Diseases
Background:
- Multiple sclerosis (MS) is a common autoimmune disease of the central nervous system (CNS).
- The exact cause of MS remains unknown, but molecular mimicry theory suggests infections can activate T cells against brain auto-antigens, initiating the disease.
- Myelin proteolipid protein (PLP) is a key auto-antigen in MS.
Purpose of the Study:
- To predict the auto-antigen potency of myelin proteolipid protein (PLP) in the context of multiple sclerosis (MS).
- To identify potential immunogenic epitopes of PLP relevant to MS pathogenesis.
Main Methods:
- Modeled the tertiary structure of PLP using prediction servers due to lack of available data in the Protein Data Bank (PDB).
- Employed a meta-prediction approach with multiple T cell epitope prediction web servers to identify PLP epitopes against Human Leukocyte Antigens (HLA).
- Selected overlapping regions predicted by multiple servers as immunogenic epitopes and performed BLASTP against microorganisms.
Main Results:
- Identified three immunodominant regions in PLP: AA58-74, AA161-177, and AA238-254.
- Found significant similarity between the PLP epitope AA58-74 and peptides from bacteria (Clostridium, Mycobacterium) and viral spike proteins (Alphacoronavirus 1, Canine coronavirus, Feline coronavirus).
- These findings support the molecular mimicry hypothesis, suggesting cross-reactivity with microbial antigens may contribute to MS progression.
Conclusions:
- Bioinformatic prediction of consensus epitopes eliminates the need for extensive experimental synthesis and in vitro testing of all PLP fragments.
- This study successfully predicted key encephalitogenic antigens using bioinformatics tools.
- The in silico approach offers a faster and more cost-effective method for MS research, accelerating the identification of critical antigens.
