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Role of peptide processing predictions in T cell epitope identification: contribution of different prediction programs

Proteolysis is the general term to describe the process of protein degradation into peptides. Proteasomes are the main actors in cellular proteolysis, and their activity can be measured in in vitro digestion experiments. However, in vivo proteolysis can be different than what is measured in these ex...

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Autores principales: Calis, Jorg J. A., Reinink, Peter, Keller, Christin, Kloetzel, Peter M., Keşmir, Can
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4297296/
https://www.ncbi.nlm.nih.gov/pubmed/25475908
http://dx.doi.org/10.1007/s00251-014-0815-0
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author Calis, Jorg J. A.
Reinink, Peter
Keller, Christin
Kloetzel, Peter M.
Keşmir, Can
author_facet Calis, Jorg J. A.
Reinink, Peter
Keller, Christin
Kloetzel, Peter M.
Keşmir, Can
author_sort Calis, Jorg J. A.
collection PubMed
description Proteolysis is the general term to describe the process of protein degradation into peptides. Proteasomes are the main actors in cellular proteolysis, and their activity can be measured in in vitro digestion experiments. However, in vivo proteolysis can be different than what is measured in these experiments if other proteases participate or if proteasomal activity is different in vivo. The in vivo proteolysis can be measured only indirectly, by the analysis of peptides presented on MHC-I molecules. MHC-I presented peptides are protected from further degradation, thus enabling an indirect view on the underlying in vivo proteolysis. The ligands presented on different MHC-I molecules enable different views on this process; in combination, they might give a complete picture. Based on in vitro proteasome-only digestions and MHC-I ligand data, different proteolysis predictors have been developed. With new in vitro digestion and MHC-I ligand data sets, we benchmarked how well these predictors capture in vitro proteasome-only activity and in vivo whole-cell proteolysis, respectively. Even though the in vitro proteasome digestion patterns were best captured by methods trained on such data (ProteaSMM and NetChop 20S), the in vivo whole-cell proteolysis was best predicted by a method trained on MHC-I ligand data (NetChop Cterm). Follow-up analysis showed that the likely source of this difference is the activity from proteases other than the proteasome, such as TPPII. This non-proteasomal in vivo activity is captured by NetChop Cterm and should be taken into account in MHC-I ligand predictions. Electronic supplementary material The online version of this article (doi:10.1007/s00251-014-0815-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-42972962015-01-21 Role of peptide processing predictions in T cell epitope identification: contribution of different prediction programs Calis, Jorg J. A. Reinink, Peter Keller, Christin Kloetzel, Peter M. Keşmir, Can Immunogenetics Original Paper Proteolysis is the general term to describe the process of protein degradation into peptides. Proteasomes are the main actors in cellular proteolysis, and their activity can be measured in in vitro digestion experiments. However, in vivo proteolysis can be different than what is measured in these experiments if other proteases participate or if proteasomal activity is different in vivo. The in vivo proteolysis can be measured only indirectly, by the analysis of peptides presented on MHC-I molecules. MHC-I presented peptides are protected from further degradation, thus enabling an indirect view on the underlying in vivo proteolysis. The ligands presented on different MHC-I molecules enable different views on this process; in combination, they might give a complete picture. Based on in vitro proteasome-only digestions and MHC-I ligand data, different proteolysis predictors have been developed. With new in vitro digestion and MHC-I ligand data sets, we benchmarked how well these predictors capture in vitro proteasome-only activity and in vivo whole-cell proteolysis, respectively. Even though the in vitro proteasome digestion patterns were best captured by methods trained on such data (ProteaSMM and NetChop 20S), the in vivo whole-cell proteolysis was best predicted by a method trained on MHC-I ligand data (NetChop Cterm). Follow-up analysis showed that the likely source of this difference is the activity from proteases other than the proteasome, such as TPPII. This non-proteasomal in vivo activity is captured by NetChop Cterm and should be taken into account in MHC-I ligand predictions. Electronic supplementary material The online version of this article (doi:10.1007/s00251-014-0815-0) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2014-12-06 2015 /pmc/articles/PMC4297296/ /pubmed/25475908 http://dx.doi.org/10.1007/s00251-014-0815-0 Text en © The Author(s) 2014
spellingShingle Original Paper
Calis, Jorg J. A.
Reinink, Peter
Keller, Christin
Kloetzel, Peter M.
Keşmir, Can
Role of peptide processing predictions in T cell epitope identification: contribution of different prediction programs
title Role of peptide processing predictions in T cell epitope identification: contribution of different prediction programs
title_full Role of peptide processing predictions in T cell epitope identification: contribution of different prediction programs
title_fullStr Role of peptide processing predictions in T cell epitope identification: contribution of different prediction programs
title_full_unstemmed Role of peptide processing predictions in T cell epitope identification: contribution of different prediction programs
title_short Role of peptide processing predictions in T cell epitope identification: contribution of different prediction programs
title_sort role of peptide processing predictions in t cell epitope identification: contribution of different prediction programs
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4297296/
https://www.ncbi.nlm.nih.gov/pubmed/25475908
http://dx.doi.org/10.1007/s00251-014-0815-0
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