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Rosetta FlexPepDock to predict peptide-MHC binding: An approach for non-canonical amino acids
Computation methods that predict the binding of peptides to MHC-I are important tools for screening and identifying immunogenic antigens and have the potential to accelerate vaccine and drug development. However, most available tools are sequence-based and optimized only for peptides containing the...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9746977/ https://www.ncbi.nlm.nih.gov/pubmed/36512534 http://dx.doi.org/10.1371/journal.pone.0275759 |
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author | Bloodworth, Nathaniel Barbaro, Natália Ruggeri Moretti, Rocco Harrison, David G. Meiler, Jens |
author_facet | Bloodworth, Nathaniel Barbaro, Natália Ruggeri Moretti, Rocco Harrison, David G. Meiler, Jens |
author_sort | Bloodworth, Nathaniel |
collection | PubMed |
description | Computation methods that predict the binding of peptides to MHC-I are important tools for screening and identifying immunogenic antigens and have the potential to accelerate vaccine and drug development. However, most available tools are sequence-based and optimized only for peptides containing the twenty canonical amino acids. This omits a large number of peptides containing non-canonical amino acids (NCAA), or residues that undergo varied post-translational modifications such as glycosylation or phosphorylation. These modifications fundamentally alter peptide immunogenicity. Similarly, existing structure-based methods are biased towards canonical peptide backbone structures, which may or may not be preserved when NCAAs are present. Rosetta FlexPepDock ab-initio is a structure-based computational protocol able to evaluate peptide-receptor interaction where no prior information of the peptide backbone is known. We benchmarked FlexPepDock ab-initio for docking canonical peptides to MHC-I, and illustrate for the first time the method’s ability to accurately model MHC-I bound epitopes containing NCAAs. FlexPepDock ab-initio protocol was able to recapitulate near-native structures (≤1.5Å) in the top lowest-energy models for 20 out of 25 cases in our initial benchmark. Using known experimental binding affinities of twenty peptides derived from an influenza-derived peptide, we showed that FlexPepDock protocol is able to predict relative binding affinity as Rosetta energies correlate well with experimental values (r = 0.59, p = 0.006). ROC analysis revealed 80% true positive and a 40% false positive rate, with a prediction power of 93%. Finally, we demonstrate the protocol’s ability to accurately recapitulate HLA-A*02:01 bound phosphopeptide backbone structures and relative binding affinity changes, the theoretical structure of the lymphocytic choriomeningitis derived glycosylated peptide GP392 bound to MHC-I H-2D(b), and isolevuglandin-adducted peptides. The ability to use non-canonical amino acids in the Rosetta FlexPepDock protocol may provide useful insight into critical amino acid positions where the post-translational modification modulates immunologic responses. |
format | Online Article Text |
id | pubmed-9746977 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-97469772022-12-14 Rosetta FlexPepDock to predict peptide-MHC binding: An approach for non-canonical amino acids Bloodworth, Nathaniel Barbaro, Natália Ruggeri Moretti, Rocco Harrison, David G. Meiler, Jens PLoS One Research Article Computation methods that predict the binding of peptides to MHC-I are important tools for screening and identifying immunogenic antigens and have the potential to accelerate vaccine and drug development. However, most available tools are sequence-based and optimized only for peptides containing the twenty canonical amino acids. This omits a large number of peptides containing non-canonical amino acids (NCAA), or residues that undergo varied post-translational modifications such as glycosylation or phosphorylation. These modifications fundamentally alter peptide immunogenicity. Similarly, existing structure-based methods are biased towards canonical peptide backbone structures, which may or may not be preserved when NCAAs are present. Rosetta FlexPepDock ab-initio is a structure-based computational protocol able to evaluate peptide-receptor interaction where no prior information of the peptide backbone is known. We benchmarked FlexPepDock ab-initio for docking canonical peptides to MHC-I, and illustrate for the first time the method’s ability to accurately model MHC-I bound epitopes containing NCAAs. FlexPepDock ab-initio protocol was able to recapitulate near-native structures (≤1.5Å) in the top lowest-energy models for 20 out of 25 cases in our initial benchmark. Using known experimental binding affinities of twenty peptides derived from an influenza-derived peptide, we showed that FlexPepDock protocol is able to predict relative binding affinity as Rosetta energies correlate well with experimental values (r = 0.59, p = 0.006). ROC analysis revealed 80% true positive and a 40% false positive rate, with a prediction power of 93%. Finally, we demonstrate the protocol’s ability to accurately recapitulate HLA-A*02:01 bound phosphopeptide backbone structures and relative binding affinity changes, the theoretical structure of the lymphocytic choriomeningitis derived glycosylated peptide GP392 bound to MHC-I H-2D(b), and isolevuglandin-adducted peptides. The ability to use non-canonical amino acids in the Rosetta FlexPepDock protocol may provide useful insight into critical amino acid positions where the post-translational modification modulates immunologic responses. Public Library of Science 2022-12-13 /pmc/articles/PMC9746977/ /pubmed/36512534 http://dx.doi.org/10.1371/journal.pone.0275759 Text en © 2022 Bloodworth et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Bloodworth, Nathaniel Barbaro, Natália Ruggeri Moretti, Rocco Harrison, David G. Meiler, Jens Rosetta FlexPepDock to predict peptide-MHC binding: An approach for non-canonical amino acids |
title | Rosetta FlexPepDock to predict peptide-MHC binding: An approach for non-canonical amino acids |
title_full | Rosetta FlexPepDock to predict peptide-MHC binding: An approach for non-canonical amino acids |
title_fullStr | Rosetta FlexPepDock to predict peptide-MHC binding: An approach for non-canonical amino acids |
title_full_unstemmed | Rosetta FlexPepDock to predict peptide-MHC binding: An approach for non-canonical amino acids |
title_short | Rosetta FlexPepDock to predict peptide-MHC binding: An approach for non-canonical amino acids |
title_sort | rosetta flexpepdock to predict peptide-mhc binding: an approach for non-canonical amino acids |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9746977/ https://www.ncbi.nlm.nih.gov/pubmed/36512534 http://dx.doi.org/10.1371/journal.pone.0275759 |
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