pyMBE: The Python-based molecule builder for ESPResSo

Autores
Beyer, David; Torres, Paola Beatriz; Pineda, Sebastian P.; Narambuena, Claudio Fabian; Grad, Jean Noël; Košovan, Peter; Blanco, Pablo M.
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We present the Python-based Molecule Builder for ESPResSo (pyMBE), an open source software application to design custom coarse-grained (CG) models, as well as pre-defined models of polyelectrolytes, peptides, and globular proteins in the Extensible Simulation Package for Research on Soft Matter (ESPResSo). The Python interface of ESPResSo offers a flexible framework, capable of building custom CG models from scratch. As a downside, building CG models from scratch is prone to mistakes, especially for newcomers in the field of CG modeling, or for molecules with complex architectures. The pyMBE module builds CG models in ESPResSo using a hierarchical bottom-up approach, providing a robust tool to automate the setup of CG models and helping new users prevent common mistakes. ESPResSo features the constant pH (cpH) and grand-reaction (G-RxMC) methods, which have been designed to study chemical reaction equilibria in macromolecular systems with many reactive species. However, setting up these methods for systems, which contain several types of reactive groups, is an error-prone task, especially for beginners. The pyMBE module enables the automatic setup of cpH and G-RxMC simulations in ESPResSo, lowering the barrier for newcomers and opening the door to investigate complex systems not studied with these methods yet. To demonstrate some of the applications of pyMBE, we showcase several case studies where we successfully reproduce previously published simulations of charge-regulating peptides and globular proteins in bulk solution and weak polyelectrolytes in dialysis. The pyMBE module is publicly available as a GitHub repository (https://github.com/pyMBE-dev/pyMBE), which includes its source code and various sample and test scripts, including the ones that we used to generate the data presented in this article.
Fil: Beyer, David. University of Stuttgart; Alemania
Fil: Torres, Paola Beatriz. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - San Luis. Instituto de Fisica Aplicada "dr. Jorge Andres Zgrablich". Grupo Vinculado Bionanotecnologia y Sistemas Complejos | Universidad Nacional de San Luis. Facultad de Cs.fisico Matematicas y Naturales. Instituto de Fisica Aplicada "dr. Jorge Andres Zgrablich". Grupo Vinculado Bionanotecnologia y Sistemas Complejos. - Universidad Tecnologica Nacional. Facultad Reg.san Rafael. Grupo Vinculado Bionanotecnologia y Sistemas Complejos.; Argentina
Fil: Pineda, Sebastian P.. Charles University; República Checa
Fil: Narambuena, Claudio Fabian. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - San Luis. Instituto de Fisica Aplicada "dr. Jorge Andres Zgrablich". Grupo Vinculado Bionanotecnologia y Sistemas Complejos | Universidad Nacional de San Luis. Facultad de Cs.fisico Matematicas y Naturales. Instituto de Fisica Aplicada "dr. Jorge Andres Zgrablich". Grupo Vinculado Bionanotecnologia y Sistemas Complejos. - Universidad Tecnologica Nacional. Facultad Reg.san Rafael. Grupo Vinculado Bionanotecnologia y Sistemas Complejos.; Argentina
Fil: Grad, Jean Noël. University of Stuttgart; Alemania
Fil: Košovan, Peter. Norwegian University of Life Sciences; Noruega
Fil: Blanco, Pablo M.. Norwegian University of Science and Technology; Noruega
Materia
Protein
ESPResSo
Peptide
Simulation
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/262682
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/262682
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling pyMBE: The Python-based molecule builder for ESPResSoBeyer, DavidTorres, Paola BeatrizPineda, Sebastian P.Narambuena, Claudio FabianGrad, Jean NoëlKošovan, PeterBlanco, Pablo M.ProteinESPResSoPeptideSimulationhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1We present the Python-based Molecule Builder for ESPResSo (pyMBE), an open source software application to design custom coarse-grained (CG) models, as well as pre-defined models of polyelectrolytes, peptides, and globular proteins in the Extensible Simulation Package for Research on Soft Matter (ESPResSo). The Python interface of ESPResSo offers a flexible framework, capable of building custom CG models from scratch. As a downside, building CG models from scratch is prone to mistakes, especially for newcomers in the field of CG modeling, or for molecules with complex architectures. The pyMBE module builds CG models in ESPResSo using a hierarchical bottom-up approach, providing a robust tool to automate the setup of CG models and helping new users prevent common mistakes. ESPResSo features the constant pH (cpH) and grand-reaction (G-RxMC) methods, which have been designed to study chemical reaction equilibria in macromolecular systems with many reactive species. However, setting up these methods for systems, which contain several types of reactive groups, is an error-prone task, especially for beginners. The pyMBE module enables the automatic setup of cpH and G-RxMC simulations in ESPResSo, lowering the barrier for newcomers and opening the door to investigate complex systems not studied with these methods yet. To demonstrate some of the applications of pyMBE, we showcase several case studies where we successfully reproduce previously published simulations of charge-regulating peptides and globular proteins in bulk solution and weak polyelectrolytes in dialysis. The pyMBE module is publicly available as a GitHub repository (https://github.com/pyMBE-dev/pyMBE), which includes its source code and various sample and test scripts, including the ones that we used to generate the data presented in this article.Fil: Beyer, David. University of Stuttgart; AlemaniaFil: Torres, Paola Beatriz. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - San Luis. Instituto de Fisica Aplicada "dr. Jorge Andres Zgrablich". Grupo Vinculado Bionanotecnologia y Sistemas Complejos | Universidad Nacional de San Luis. Facultad de Cs.fisico Matematicas y Naturales. Instituto de Fisica Aplicada "dr. Jorge Andres Zgrablich". Grupo Vinculado Bionanotecnologia y Sistemas Complejos. - Universidad Tecnologica Nacional. Facultad Reg.san Rafael. Grupo Vinculado Bionanotecnologia y Sistemas Complejos.; ArgentinaFil: Pineda, Sebastian P.. Charles University; República ChecaFil: Narambuena, Claudio Fabian. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - San Luis. Instituto de Fisica Aplicada "dr. Jorge Andres Zgrablich". Grupo Vinculado Bionanotecnologia y Sistemas Complejos | Universidad Nacional de San Luis. Facultad de Cs.fisico Matematicas y Naturales. Instituto de Fisica Aplicada "dr. Jorge Andres Zgrablich". Grupo Vinculado Bionanotecnologia y Sistemas Complejos. - Universidad Tecnologica Nacional. Facultad Reg.san Rafael. Grupo Vinculado Bionanotecnologia y Sistemas Complejos.; ArgentinaFil: Grad, Jean Noël. University of Stuttgart; AlemaniaFil: Košovan, Peter. Norwegian University of Life Sciences; NoruegaFil: Blanco, Pablo M.. Norwegian University of Science and Technology; NoruegaAmerican Institute of Physics2024-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/262682Beyer, David; Torres, Paola Beatriz; Pineda, Sebastian P.; Narambuena, Claudio Fabian; Grad, Jean Noël; et al.; pyMBE: The Python-based molecule builder for ESPResSo; American Institute of Physics; Journal of Chemical Physics; 161; 2; 7-2024; 1-210021-9606CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://pubs.aip.org/jcp/article/161/2/022502/3303328/pyMBE-The-Python-based-molecule-builder-forinfo:eu-repo/semantics/altIdentifier/doi/10.1063/5.0216389info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:42:35Zoai:ri.conicet.gov.ar:11336/262682instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:42:35.752CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv pyMBE: The Python-based molecule builder for ESPResSo
title pyMBE: The Python-based molecule builder for ESPResSo
spellingShingle pyMBE: The Python-based molecule builder for ESPResSo
Beyer, David
Protein
ESPResSo
Peptide
Simulation
title_short pyMBE: The Python-based molecule builder for ESPResSo
title_full pyMBE: The Python-based molecule builder for ESPResSo
title_fullStr pyMBE: The Python-based molecule builder for ESPResSo
title_full_unstemmed pyMBE: The Python-based molecule builder for ESPResSo
title_sort pyMBE: The Python-based molecule builder for ESPResSo
dc.creator.none.fl_str_mv Beyer, David
Torres, Paola Beatriz
Pineda, Sebastian P.
Narambuena, Claudio Fabian
Grad, Jean Noël
Košovan, Peter
Blanco, Pablo M.
author Beyer, David
author_facet Beyer, David
Torres, Paola Beatriz
Pineda, Sebastian P.
Narambuena, Claudio Fabian
Grad, Jean Noël
Košovan, Peter
Blanco, Pablo M.
author_role author
author2 Torres, Paola Beatriz
Pineda, Sebastian P.
Narambuena, Claudio Fabian
Grad, Jean Noël
Košovan, Peter
Blanco, Pablo M.
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Protein
ESPResSo
Peptide
Simulation
topic Protein
ESPResSo
Peptide
Simulation
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We present the Python-based Molecule Builder for ESPResSo (pyMBE), an open source software application to design custom coarse-grained (CG) models, as well as pre-defined models of polyelectrolytes, peptides, and globular proteins in the Extensible Simulation Package for Research on Soft Matter (ESPResSo). The Python interface of ESPResSo offers a flexible framework, capable of building custom CG models from scratch. As a downside, building CG models from scratch is prone to mistakes, especially for newcomers in the field of CG modeling, or for molecules with complex architectures. The pyMBE module builds CG models in ESPResSo using a hierarchical bottom-up approach, providing a robust tool to automate the setup of CG models and helping new users prevent common mistakes. ESPResSo features the constant pH (cpH) and grand-reaction (G-RxMC) methods, which have been designed to study chemical reaction equilibria in macromolecular systems with many reactive species. However, setting up these methods for systems, which contain several types of reactive groups, is an error-prone task, especially for beginners. The pyMBE module enables the automatic setup of cpH and G-RxMC simulations in ESPResSo, lowering the barrier for newcomers and opening the door to investigate complex systems not studied with these methods yet. To demonstrate some of the applications of pyMBE, we showcase several case studies where we successfully reproduce previously published simulations of charge-regulating peptides and globular proteins in bulk solution and weak polyelectrolytes in dialysis. The pyMBE module is publicly available as a GitHub repository (https://github.com/pyMBE-dev/pyMBE), which includes its source code and various sample and test scripts, including the ones that we used to generate the data presented in this article.
Fil: Beyer, David. University of Stuttgart; Alemania
Fil: Torres, Paola Beatriz. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - San Luis. Instituto de Fisica Aplicada "dr. Jorge Andres Zgrablich". Grupo Vinculado Bionanotecnologia y Sistemas Complejos | Universidad Nacional de San Luis. Facultad de Cs.fisico Matematicas y Naturales. Instituto de Fisica Aplicada "dr. Jorge Andres Zgrablich". Grupo Vinculado Bionanotecnologia y Sistemas Complejos. - Universidad Tecnologica Nacional. Facultad Reg.san Rafael. Grupo Vinculado Bionanotecnologia y Sistemas Complejos.; Argentina
Fil: Pineda, Sebastian P.. Charles University; República Checa
Fil: Narambuena, Claudio Fabian. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - San Luis. Instituto de Fisica Aplicada "dr. Jorge Andres Zgrablich". Grupo Vinculado Bionanotecnologia y Sistemas Complejos | Universidad Nacional de San Luis. Facultad de Cs.fisico Matematicas y Naturales. Instituto de Fisica Aplicada "dr. Jorge Andres Zgrablich". Grupo Vinculado Bionanotecnologia y Sistemas Complejos. - Universidad Tecnologica Nacional. Facultad Reg.san Rafael. Grupo Vinculado Bionanotecnologia y Sistemas Complejos.; Argentina
Fil: Grad, Jean Noël. University of Stuttgart; Alemania
Fil: Košovan, Peter. Norwegian University of Life Sciences; Noruega
Fil: Blanco, Pablo M.. Norwegian University of Science and Technology; Noruega
description We present the Python-based Molecule Builder for ESPResSo (pyMBE), an open source software application to design custom coarse-grained (CG) models, as well as pre-defined models of polyelectrolytes, peptides, and globular proteins in the Extensible Simulation Package for Research on Soft Matter (ESPResSo). The Python interface of ESPResSo offers a flexible framework, capable of building custom CG models from scratch. As a downside, building CG models from scratch is prone to mistakes, especially for newcomers in the field of CG modeling, or for molecules with complex architectures. The pyMBE module builds CG models in ESPResSo using a hierarchical bottom-up approach, providing a robust tool to automate the setup of CG models and helping new users prevent common mistakes. ESPResSo features the constant pH (cpH) and grand-reaction (G-RxMC) methods, which have been designed to study chemical reaction equilibria in macromolecular systems with many reactive species. However, setting up these methods for systems, which contain several types of reactive groups, is an error-prone task, especially for beginners. The pyMBE module enables the automatic setup of cpH and G-RxMC simulations in ESPResSo, lowering the barrier for newcomers and opening the door to investigate complex systems not studied with these methods yet. To demonstrate some of the applications of pyMBE, we showcase several case studies where we successfully reproduce previously published simulations of charge-regulating peptides and globular proteins in bulk solution and weak polyelectrolytes in dialysis. The pyMBE module is publicly available as a GitHub repository (https://github.com/pyMBE-dev/pyMBE), which includes its source code and various sample and test scripts, including the ones that we used to generate the data presented in this article.
publishDate 2024
dc.date.none.fl_str_mv 2024-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/262682
Beyer, David; Torres, Paola Beatriz; Pineda, Sebastian P.; Narambuena, Claudio Fabian; Grad, Jean Noël; et al.; pyMBE: The Python-based molecule builder for ESPResSo; American Institute of Physics; Journal of Chemical Physics; 161; 2; 7-2024; 1-21
0021-9606
CONICET Digital
CONICET
url http://hdl.handle.net/11336/262682
identifier_str_mv Beyer, David; Torres, Paola Beatriz; Pineda, Sebastian P.; Narambuena, Claudio Fabian; Grad, Jean Noël; et al.; pyMBE: The Python-based molecule builder for ESPResSo; American Institute of Physics; Journal of Chemical Physics; 161; 2; 7-2024; 1-21
0021-9606
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://pubs.aip.org/jcp/article/161/2/022502/3303328/pyMBE-The-Python-based-molecule-builder-for
info:eu-repo/semantics/altIdentifier/doi/10.1063/5.0216389
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Institute of Physics
publisher.none.fl_str_mv American Institute of Physics
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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