Abstract

The immune system reacts to foreign molecules in a highly specialized manner. T cells play a central role in the cell-mediated immunity. T cells scrutinize small peptide fragments presented in complex with major histocompatibility complexes (MHCs) on the surface of most cells in the host. Identifying which peptides will be presented in complex with a given MHC molecule therefore is of pivotal importance for understanding cellular immunity. B cells generate antibodies reactive to specific patches (epitopes) on the surface of antigenic proteins. The understanding of what structural and sequential properties of an antigenic surface patch dictates its immunogenicity has large impacts for our ability to identify and design novel biologicals.

Each MHC molecule is highly specific, binding only a minor fraction of the set of possible peptides. Due to the high selectivity of the MHC molecules, major efforts have been dedicated to characterize their binding specificity and several in-silico methods have been developed to predict this event. However, other factors including antigen processing, peptide: MHC binding stability, peptide similarity to self, etc. have been claimed to impact peptide T cell immunogenicity.

In my lectures, I will give an overview of the advances during the last decade in prediction methods for rational epitope discovery. I will demonstrate how these advances combined with high throughput and accurate data generation has allowed us to arrive at very simple yet highly accurate models for prediction of T cell epitopes and argue why in my view no factor other than MHC binding is critical when predicting cytotoxic peptide T cell immunogenicity. I will also demonstrate how in-silico tools server as critical guide when interpreting immunological patient data.

Likewise, I will describe recent improvements in the prediction of B cell epitopes driven mostly by the advances in the local structural protein property predictions achieved by the use of deep machine learning methods.

In my lectures, I will discuss limitations of the state-of-the-art tools, and show recent results demonstrating how information of the T/B cell receptor sequences and/or structure can be used to boost the predictive performance potentially allowing a targeted approach identifying the cognate target of a given T or B cell receptor.

References

  1.  Jurtz V, Paul S, Andreatta M, Marcatili P, Peters B, Nielsen M. NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data. J Immunol. 2017 Oct 4;
  2. Andreatta M, Alvarez B, Nielsen M. GibbsCluster: unsupervised clustering and alignment of peptide sequences. Nucleic Acids Res. 2017 Apr 12;
  3. Alvarez B, Barra C, Nielsen M, Andreatta M. Computational Tools for the Identification and Interpretation of Sequence Motifs in Immunopeptidomes. Proteomics. 2018 Jan 12;
  4. Jespersen MC, Peters B, Nielsen M, Marcatili P. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res. 2017 May 2;
  5. Lanzarotti E, Marcatili P, Nielsen M. Identification of the cognate peptide-MHC target of T cell receptors using molecular modeling and force field scoring. Mol Immunol. 2017 Dec 27;94