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pyPept: a python library to generate atomistic 2D and 3D representations of peptides

We present pyPept, a set of executables and underlying python-language classes to easily create, manipulate, and analyze peptide molecules using the FASTA, HELM, or recently-developed BILN notations. The framework enables the analysis of both pure proteinogenic peptides as well as those with non-nat...

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Detalles Bibliográficos
Autores principales: Ochoa, Rodrigo, Brown, J. B., Fox, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10498622/
https://www.ncbi.nlm.nih.gov/pubmed/37700347
http://dx.doi.org/10.1186/s13321-023-00748-2
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author Ochoa, Rodrigo
Brown, J. B.
Fox, Thomas
author_facet Ochoa, Rodrigo
Brown, J. B.
Fox, Thomas
author_sort Ochoa, Rodrigo
collection PubMed
description We present pyPept, a set of executables and underlying python-language classes to easily create, manipulate, and analyze peptide molecules using the FASTA, HELM, or recently-developed BILN notations. The framework enables the analysis of both pure proteinogenic peptides as well as those with non-natural amino acids, including support to assemble a customizable monomer library, without requiring programming. From line notations, a peptide is transformed into a molecular graph for 2D depiction tasks, the calculation of physicochemical properties, and other systematic analyses or processing pipelines. The package includes a module to rapidly generate approximate peptide conformers by incorporating secondary structure restraints either given by the user or predicted via pyPept, and a wrapper tool is also provided to automate the generation and output of 2D and 3D representations of a peptide directly from the line notation. HELM and BILN notations that include circular, branched, or stapled peptides are fully supported, eliminating errors in structure creation that are prone during manual drawing and connecting. The framework and common workflows followed in pyPept are described together with illustrative examples. pyPept has been released at: https://github.com/Boehringer-Ingelheim/pyPept. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-023-00748-2.
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spelling pubmed-104986222023-09-14 pyPept: a python library to generate atomistic 2D and 3D representations of peptides Ochoa, Rodrigo Brown, J. B. Fox, Thomas J Cheminform Software We present pyPept, a set of executables and underlying python-language classes to easily create, manipulate, and analyze peptide molecules using the FASTA, HELM, or recently-developed BILN notations. The framework enables the analysis of both pure proteinogenic peptides as well as those with non-natural amino acids, including support to assemble a customizable monomer library, without requiring programming. From line notations, a peptide is transformed into a molecular graph for 2D depiction tasks, the calculation of physicochemical properties, and other systematic analyses or processing pipelines. The package includes a module to rapidly generate approximate peptide conformers by incorporating secondary structure restraints either given by the user or predicted via pyPept, and a wrapper tool is also provided to automate the generation and output of 2D and 3D representations of a peptide directly from the line notation. HELM and BILN notations that include circular, branched, or stapled peptides are fully supported, eliminating errors in structure creation that are prone during manual drawing and connecting. The framework and common workflows followed in pyPept are described together with illustrative examples. pyPept has been released at: https://github.com/Boehringer-Ingelheim/pyPept. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-023-00748-2. Springer International Publishing 2023-09-12 /pmc/articles/PMC10498622/ /pubmed/37700347 http://dx.doi.org/10.1186/s13321-023-00748-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Ochoa, Rodrigo
Brown, J. B.
Fox, Thomas
pyPept: a python library to generate atomistic 2D and 3D representations of peptides
title pyPept: a python library to generate atomistic 2D and 3D representations of peptides
title_full pyPept: a python library to generate atomistic 2D and 3D representations of peptides
title_fullStr pyPept: a python library to generate atomistic 2D and 3D representations of peptides
title_full_unstemmed pyPept: a python library to generate atomistic 2D and 3D representations of peptides
title_short pyPept: a python library to generate atomistic 2D and 3D representations of peptides
title_sort pypept: a python library to generate atomistic 2d and 3d representations of peptides
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10498622/
https://www.ncbi.nlm.nih.gov/pubmed/37700347
http://dx.doi.org/10.1186/s13321-023-00748-2
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