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. 2022 Oct 28:13:930590.
doi: 10.3389/fimmu.2022.930590. eCollection 2022.

MatchTope: A tool to predict the cross reactivity of peptides complexed with Major Histocompatibility Complex I

Marcus Fabiano de Almeida Mendes  1 , Marcelo de Souza Bragatte  1 , Priscila Vianna  1 , Martiela Vaz de Freitas  1 , Ina Pöhner  2 , Stefan Richter  2 , Rebecca C Wade  2   3 , Francisco Mauro Salzano  1 , Gustavo Fioravanti Vieira  1   4
Affiliations

MatchTope: A tool to predict the cross reactivity of peptides complexed with Major Histocompatibility Complex I

Marcus Fabiano de Almeida Mendes et al. Front Immunol. .

Abstract

The therapeutic targeting of the immune system, for example in vaccinology and cancer treatment, is a challenging task and the subject of active research. Several in silico tools used for predicting immunogenicity are based on the analysis of peptide sequences binding to the Major Histocompatibility Complex (pMHC). However, few of these bioinformatics tools take into account the pMHC three-dimensional structure. Here, we describe a new bioinformatics tool, MatchTope, developed for predicting peptide similarity, which can trigger cross-reactivity events, by computing and analyzing the electrostatic potentials of pMHC complexes. We validated MatchTope by using previously published data from in vitro assays. We thereby demonstrate the strength of MatchTope for similarity prediction between targets derived from several pathogens as well as for indicating possible cross responses between self and tumor peptides. Our results suggest that MatchTope can enhance and speed up future studies in the fields of vaccinology and cancer immunotherapy.

Keywords: T-cell; T-cell response; cross reactivity; cross reactivity prediction; immunoinformatic.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
MatchTope flowchart showing the analysis process from the first step of inputting pdb files to the final step of generating results. Each step is described in greater detail in the Methods section.
Figure 2
Figure 2
Two different views of a pMHC showing the exact region and size of the cylindrical region used by PIPSA to compare MEPs between different pMHCs. The pMHC is shown in the representation with the alpha chain in light blue color, the beta chain in dark blue color, the epitope in orange with amino acid side chains in stick representation. The cylindrical region used for calculation is shown by a gray semi-transparent surface. The pMHC is shown from the top (A) and from the side (B). The pMHC depicted was modeled with the DockTope (28) tool using a dengue virus epitope as input.
Figure 3
Figure 3
The final result of MatchTope analysis: Hierarchical clustering is represented in a tree-based format. Pvclust provides two types of p-values: AU (Approximately Unbiased, red color) p-value and BP (Bootstrap Probability, green color) value. Branches inside red squares have an AU p-value higher than 95%, indicating a significant similarity between pMHCs clustered in that branch. There are three different clusters matching the different data sets used for MatchTope validation. (A) is related to the Hepatitis E Virus (HEV)-Specific T Cell Receptor study, (B) is the cluster from the throat cancer study, and (C) combines the clustering from the HCV and Dengue studies. Groups 1, 3, 5, 7, and 8 clustered epitopes that have cross-reactivity confirmed by in vitro data. Supplementary Table 1 presents the epitope list and which epitopes similarly trigger cross reactivity.
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