MMPred: a tool to predict peptide mimicry events in MHC class II recognition

Area of Science:

• Computational biology
• Immunoinformatics
• Autoimmune disease research

Background:

• Molecular mimicry is a key mechanism in autoimmune diseases, where microbial antigens resemble self-antigens, triggering immune responses.
• Identifying molecular mimicry events is crucial for understanding autoimmune disease pathogenesis and developing targeted therapies.

Purpose of the Study:

• To introduce MMPred, a novel software tool designed to streamline the computational analysis of molecular mimicry.
• To facilitate the generation, investigation, and testing of mimicry hypotheses involving human and microbial peptide sequences.
• To provide epitope predictions for human leukocyte antigen (HLA) class II alleles, crucial in autoimmunity.

Main Methods:

• MMPred integrates epitope prediction and sequence alignment algorithms.
• The tool predicts epitopes for MHC class II alleles and is extendable to MHC class I using pre-trained models.
• A comprehensive assessment involved predicting autoepitope-microbial peptide mimicry, systems biology interpretation, and case studies (e.g., SARS-CoV-2).

Main Results:

• MMPred successfully predicted associations between known HLA class II autoepitopes and microbial peptides.
• The tool's predictions were interpreted within a systems biology framework, identifying potential functional links to autoimmune pathways.
• Illustrative case studies, including SARS-CoV-2 infection, demonstrated MMPred's utility in analyzing mimicry events.

Conclusions:

• MMPred offers a streamlined approach for computational analysis of molecular mimicry in autoimmune diseases.
• The software provides valuable insights into potential triggers of autoimmunity, aiding in hypothesis generation and testing.
• MMPred is freely available, promoting further research in the field of molecular mimicry and autoimmune diseases.