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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...

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Autores principales: Bloodworth, Nathaniel, Barbaro, Natália Ruggeri, Moretti, Rocco, Harrison, David G., Meiler, Jens
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
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.
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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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