Bioinformatic Core, Immunogenetics Laboratory, Genetics Department, Biosciences Institute, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.
Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany.
Front Immunol. 2022 Oct 28;13:930590. doi: 10.3389/fimmu.2022.930590. eCollection 2022.
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 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 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.
例如在疫苗学和癌症治疗中，针对免疫系统的治疗靶向是一项具有挑战性的任务，也是当前研究的热点。有几种用于预测免疫原性的工具是基于对与主要组织相容性复合体（pMHC）结合的肽序列的分析。然而，这些生物信息学工具中很少有考虑 pMHC 三维结构的。在这里，我们描述了一种新的生物信息学工具 MatchTope，它是通过计算和分析 pMHC 复合物的静电势来开发的，用于预测可能引发交叉反应事件的肽相似性。我们使用先前发表的来自测定的实验数据验证了 MatchTope。我们证明了 MatchTope 用于预测来自几种病原体的靶标之间的相似性以及指示自身和肿瘤肽之间可能的交叉反应的能力。我们的结果表明，MatchTope 可以增强和加速疫苗学和癌症免疫治疗领域的未来研究。
