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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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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). |
format | Online Article Text |
id | pubmed-8888980 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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