Current T cell epitope prediction tools are a valuable resource in designing targeted immunogenicity experiments. They typically focus on, and are able to, accurately predict peptide binding and presentation by major histocompatibility complex (MHC) molecules on the surface of antigen-presenting cells. However, recognition of the peptide-MHC complex by a T cell receptor (TCR) is often not included in these tools. We developed a classification approach based on random forest classifiers to predict recognition of a peptide by a T cell receptor and discover patterns that contribute to recognition. We considered two approaches to solve this problem: (1) distinguishing between two sets of TCRs that each bind to a known peptide and (2) retrieving TCRs that bind to a given peptide from a large pool of TCRs. Evaluation of the models on two HIV-1, B*08-restricted epitopes reveals good performance and hints towards structural CDR3 features that can determine peptide immunogenicity. These results are of particular importance as they show that prediction of T cell epitope and T cell epitope recognition based on sequence data is a feasible approach. In addition, the validity of our models not only serves as a proof of concept for the prediction of immunogenic T cell epitopes but also paves the way for more general and high-performing models.
Despite the increasing importance of non-targeted metabolomics to answer various life science questions, extracting biochemically relevant information from metabolomics spectral data is still an incompletely solved problem. Most computational tools to identify tandem mass spectra focus on a limited set of molecules of interest. However, such tools are typically constrained by the availability of reference spectra or molecular databases, limiting their applicability of generating structural hypotheses for unknown metabolites. In contrast, recent advances in the field illustrate the possibility to expose the underlying biochemistry without relying on metabolite identification, in particular via substructure prediction. We describe an automated method for substructure recommendation motivated by association rule mining. Our framework captures potential relationships between spectral features and substructures learned from public spectral libraries. These associations are used to recommend substructures for any unknown mass spectrum. Our method does not require any predefined metabolite candidates, and therefore it can be used for the hypothesis generation or partial identification of unknown unknowns. The method is called MESSAR (MEtabolite Sub-Structure Auto-Recommender) and is implemented in a free online web service available at messar.biodatamining.be. OPEN ACCESS Citation: Liu Y, Mrzic A, Meysman P, De Vijlder T, Romijn EP, Valkenborg D, et al. (2020) MESSAR: Automated recommendation of metabolite substructures from tandem mass spectra. PLoS ONE 15(1): e0226770. https://doi.
Abstract:28 Current T-cell epitope prediction tools are a valuable resource in designing targeted immunogenicity 29 experiments. They typically focus on, and are able to, accurately predict peptide binding and presentation by 30 major histocompatibility complex (MHC) molecules on the surface of antigen-presenting cells. However, 31 recognition of the peptide-MHC complex by a T-cell receptor is often not included in these tools. We developed 32 a classification approach based on random forest classifiers to predict recognition of a peptide by a T-cell and 33 discover patterns that contribute to recognition. We considered two approaches to solve this problem: (1) 34 distinguishing between two sets of T-cell receptors that each bind to a known peptide and (2) retrieving T-cell 35 receptors that bind to a given peptide from a large pool of T-cell receptors. Evaluation of the models on two 36 HIV-1, B*08-restricted epitopes reveals good performance and hints towards structural CDR3 features that can 37 determine peptide immunogenicity. These results are of particularly importance as they show that prediction of 38 T-cell epitope and T-cell epitope recognition based on sequence data is a feasible approach. In addition, the 39 validity of our models not only serves as a proof of concept for the prediction of immunogenic T-cell epitopes 40 but also paves the way for more general and high performing models.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.