Perpetual complexity: predicting human CD8⁺ T-cell responses to pathogenic peptides.

Area of Science:

• Immunology
• Computational Biology
• Bioinformatics

Background:

• Accurate prediction of CD8+ T-cell epitopes is vital for clinical applications in infection, cancer, and autoimmunity.
• Traditional prediction models primarily focused on peptide affinity to MHC-I binding grooves.
• Increasing evidence highlights the complexity of T-cell recognition, involving multiple factors in the antigen presentation pathway.

Purpose of the Study:

• To review the historical development of peptide prediction algorithms for CD8+ T-cell epitopes.
• To outline current online tools available for predicting CD8+ T-cell epitopes.
• To discuss recent advances impacting epitope prediction accuracy.

Main Methods:

• Review of historical discoveries in T-cell epitope prediction.
• Analysis of advances in next-generation sequencing and immunopeptidomics.
• Survey of current online prediction tools.

Main Results:

• Identification of key discoveries that spurred the development of prediction algorithms.
• Highlighting the impact of improved HLA-I sub-allele classification and immunopeptidomics.
• Cataloging available online tools for CD8+ T-cell epitope prediction.

Conclusions:

• Epitope prediction has evolved beyond simple MHC-I binding affinity.
• Advances in sequencing and immunopeptidomics offer greater precision.
• Current tools leverage these advances for improved prediction of T-cell epitopes.