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Computational Site Saturation Mutagenesis of Canonical and Non-Canonical Amino Acids to Probe Protein-Peptide Interactions
Technologies for discovering peptides as potential therapeutics have rapidly advanced in recent years with significant interest from both academic and pharmaceutical labs. These advancements in turn drive the need for new computational tools to design peptides for purposes of advancing lead molecule...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047896/ https://www.ncbi.nlm.nih.gov/pubmed/35495632 http://dx.doi.org/10.3389/fmolb.2022.848689 |
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author | Holden, Jeffrey K. Pavlovicz, Ryan Gobbi, Alberto Song, Yifan Cunningham, Christian N. |
author_facet | Holden, Jeffrey K. Pavlovicz, Ryan Gobbi, Alberto Song, Yifan Cunningham, Christian N. |
author_sort | Holden, Jeffrey K. |
collection | PubMed |
description | Technologies for discovering peptides as potential therapeutics have rapidly advanced in recent years with significant interest from both academic and pharmaceutical labs. These advancements in turn drive the need for new computational tools to design peptides for purposes of advancing lead molecules into the clinic. Here we report the development and application of a new automated tool, AutoRotLib, for parameterizing a diverse set of non-canonical amino acids (NCAAs), N-methyl, or peptoid residues for use with the computational design program Rosetta. In addition, we developed a protocol for designing thioether-cyclized macrocycles within Rosetta, due to their common application in mRNA display using the RaPID platform. To evaluate the utility of these new computational tools, we screened a library of canonical and NCAAs on both a linear peptide and a thioether macrocycle, allowing us to quickly identify mutations that affect peptide binding and subsequently measure our results against previously published data. We anticipate in silico screening of peptides against a diverse chemical space will be a fundamental component for peptide design and optimization, as more amino acids can be explored in a single in silico screen than an in vitro screen. As such, these tools will enable maturation of peptide affinity for protein targets of interest and optimization of peptide pharmacokinetics for therapeutic applications. |
format | Online Article Text |
id | pubmed-9047896 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90478962022-04-29 Computational Site Saturation Mutagenesis of Canonical and Non-Canonical Amino Acids to Probe Protein-Peptide Interactions Holden, Jeffrey K. Pavlovicz, Ryan Gobbi, Alberto Song, Yifan Cunningham, Christian N. Front Mol Biosci Molecular Biosciences Technologies for discovering peptides as potential therapeutics have rapidly advanced in recent years with significant interest from both academic and pharmaceutical labs. These advancements in turn drive the need for new computational tools to design peptides for purposes of advancing lead molecules into the clinic. Here we report the development and application of a new automated tool, AutoRotLib, for parameterizing a diverse set of non-canonical amino acids (NCAAs), N-methyl, or peptoid residues for use with the computational design program Rosetta. In addition, we developed a protocol for designing thioether-cyclized macrocycles within Rosetta, due to their common application in mRNA display using the RaPID platform. To evaluate the utility of these new computational tools, we screened a library of canonical and NCAAs on both a linear peptide and a thioether macrocycle, allowing us to quickly identify mutations that affect peptide binding and subsequently measure our results against previously published data. We anticipate in silico screening of peptides against a diverse chemical space will be a fundamental component for peptide design and optimization, as more amino acids can be explored in a single in silico screen than an in vitro screen. As such, these tools will enable maturation of peptide affinity for protein targets of interest and optimization of peptide pharmacokinetics for therapeutic applications. Frontiers Media S.A. 2022-04-14 /pmc/articles/PMC9047896/ /pubmed/35495632 http://dx.doi.org/10.3389/fmolb.2022.848689 Text en Copyright © 2022 Holden, Pavlovicz, Gobbi, Song and Cunningham. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Biosciences Holden, Jeffrey K. Pavlovicz, Ryan Gobbi, Alberto Song, Yifan Cunningham, Christian N. Computational Site Saturation Mutagenesis of Canonical and Non-Canonical Amino Acids to Probe Protein-Peptide Interactions |
title | Computational Site Saturation Mutagenesis of Canonical and Non-Canonical Amino Acids to Probe Protein-Peptide Interactions |
title_full | Computational Site Saturation Mutagenesis of Canonical and Non-Canonical Amino Acids to Probe Protein-Peptide Interactions |
title_fullStr | Computational Site Saturation Mutagenesis of Canonical and Non-Canonical Amino Acids to Probe Protein-Peptide Interactions |
title_full_unstemmed | Computational Site Saturation Mutagenesis of Canonical and Non-Canonical Amino Acids to Probe Protein-Peptide Interactions |
title_short | Computational Site Saturation Mutagenesis of Canonical and Non-Canonical Amino Acids to Probe Protein-Peptide Interactions |
title_sort | computational site saturation mutagenesis of canonical and non-canonical amino acids to probe protein-peptide interactions |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047896/ https://www.ncbi.nlm.nih.gov/pubmed/35495632 http://dx.doi.org/10.3389/fmolb.2022.848689 |
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