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Statistical machine learning for comparative protein dynamics with the DROIDS/maxDemon software pipeline

Comparative analysis of protein structure or sequence alignments often ignores the protein dynamics and function. We offer a graphical user interface to a computing pipeline, complete with molecular visualization, enabling the biophysical simulation and statistical comparison of two-state functional...

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Detalles Bibliográficos
Autores principales: Babbitt, Gregory A., Fokoue, Ernest P., Srivastava, Harsh R., Callahan, Breanna, Rajendran, Madhusudan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8888980/
https://www.ncbi.nlm.nih.gov/pubmed/35252883
http://dx.doi.org/10.1016/j.xpro.2022.101194
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author Babbitt, Gregory A.
Fokoue, Ernest P.
Srivastava, Harsh R.
Callahan, Breanna
Rajendran, Madhusudan
author_facet Babbitt, Gregory A.
Fokoue, Ernest P.
Srivastava, Harsh R.
Callahan, Breanna
Rajendran, Madhusudan
author_sort Babbitt, Gregory A.
collection PubMed
description Comparative analysis of protein structure or sequence alignments often ignores the protein dynamics and function. We offer a graphical user interface to a computing pipeline, complete with molecular visualization, enabling the biophysical simulation and statistical comparison of two-state functional protein dynamics (i.e., single unbound state vs. complex with a ligand, DNA, or protein). We utilize multi-agent machine learning classifiers to identify functionally conserved dynamic motions and compare them in genetic or drug-class variants. For complete details on the use and execution of this profile, please refer to Babbitt et al. (2020b, 2020a, 2018) and Rynkiewicz et al. (2021).
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spelling pubmed-88889802022-03-03 Statistical machine learning for comparative protein dynamics with the DROIDS/maxDemon software pipeline Babbitt, Gregory A. Fokoue, Ernest P. Srivastava, Harsh R. Callahan, Breanna Rajendran, Madhusudan STAR Protoc Protocol Comparative analysis of protein structure or sequence alignments often ignores the protein dynamics and function. We offer a graphical user interface to a computing pipeline, complete with molecular visualization, enabling the biophysical simulation and statistical comparison of two-state functional protein dynamics (i.e., single unbound state vs. complex with a ligand, DNA, or protein). We utilize multi-agent machine learning classifiers to identify functionally conserved dynamic motions and compare them in genetic or drug-class variants. For complete details on the use and execution of this profile, please refer to Babbitt et al. (2020b, 2020a, 2018) and Rynkiewicz et al. (2021). Elsevier 2022-02-24 /pmc/articles/PMC8888980/ /pubmed/35252883 http://dx.doi.org/10.1016/j.xpro.2022.101194 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Protocol
Babbitt, Gregory A.
Fokoue, Ernest P.
Srivastava, Harsh R.
Callahan, Breanna
Rajendran, Madhusudan
Statistical machine learning for comparative protein dynamics with the DROIDS/maxDemon software pipeline
title Statistical machine learning for comparative protein dynamics with the DROIDS/maxDemon software pipeline
title_full Statistical machine learning for comparative protein dynamics with the DROIDS/maxDemon software pipeline
title_fullStr Statistical machine learning for comparative protein dynamics with the DROIDS/maxDemon software pipeline
title_full_unstemmed Statistical machine learning for comparative protein dynamics with the DROIDS/maxDemon software pipeline
title_short Statistical machine learning for comparative protein dynamics with the DROIDS/maxDemon software pipeline
title_sort statistical machine learning for comparative protein dynamics with the droids/maxdemon software pipeline
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8888980/
https://www.ncbi.nlm.nih.gov/pubmed/35252883
http://dx.doi.org/10.1016/j.xpro.2022.101194
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