ProDy: Protein Dynamics Inferred from Theory and Experiments
Summary: We developed a Python package, ProDy, for structure-based analysis of protein dynamics. ProDy allows for quantitative characterization of structural variations in heterogeneous datasets of structures experimentally resolved for a given biomolecular system, and for comparison of these variat...
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| Format: | Online |
| Language: | English |
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Oxford University Press
2011
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3102222/ |
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pubmed-31022222011-05-31 ProDy: Protein Dynamics Inferred from Theory and Experiments Bakan, Ahmet Meireles, Lidio M. Bahar, Ivet Applications Note Summary: We developed a Python package, ProDy, for structure-based analysis of protein dynamics. ProDy allows for quantitative characterization of structural variations in heterogeneous datasets of structures experimentally resolved for a given biomolecular system, and for comparison of these variations with the theoretically predicted equilibrium dynamics. Datasets include structural ensembles for a given family or subfamily of proteins, their mutants and sequence homologues, in the presence/absence of their substrates, ligands or inhibitors. Numerous helper functions enable comparative analysis of experimental and theoretical data, and visualization of the principal changes in conformations that are accessible in different functional states. ProDy application programming interface (API) has been designed so that users can easily extend the software and implement new methods. Oxford University Press 2011-06-01 2011-04-05 /pmc/articles/PMC3102222/ /pubmed/21471012 http://dx.doi.org/10.1093/bioinformatics/btr168 Text en © The Author(s) 2011. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.5 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Bakan, Ahmet Meireles, Lidio M. Bahar, Ivet |
| spellingShingle |
Bakan, Ahmet Meireles, Lidio M. Bahar, Ivet ProDy: Protein Dynamics Inferred from Theory and Experiments |
| author_facet |
Bakan, Ahmet Meireles, Lidio M. Bahar, Ivet |
| author_sort |
Bakan, Ahmet |
| title |
ProDy: Protein Dynamics Inferred from Theory and Experiments |
| title_short |
ProDy: Protein Dynamics Inferred from Theory and Experiments |
| title_full |
ProDy: Protein Dynamics Inferred from Theory and Experiments |
| title_fullStr |
ProDy: Protein Dynamics Inferred from Theory and Experiments |
| title_full_unstemmed |
ProDy: Protein Dynamics Inferred from Theory and Experiments |
| title_sort |
prody: protein dynamics inferred from theory and experiments |
| description |
Summary: We developed a Python package, ProDy, for structure-based analysis of protein dynamics. ProDy allows for quantitative characterization of structural variations in heterogeneous datasets of structures experimentally resolved for a given biomolecular system, and for comparison of these variations with the theoretically predicted equilibrium dynamics. Datasets include structural ensembles for a given family or subfamily of proteins, their mutants and sequence homologues, in the presence/absence of their substrates, ligands or inhibitors. Numerous helper functions enable comparative analysis of experimental and theoretical data, and visualization of the principal changes in conformations that are accessible in different functional states. ProDy application programming interface (API) has been designed so that users can easily extend the software and implement new methods. |
| publisher |
Oxford University Press |
| publishDate |
2011 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3102222/ |
| _version_ |
1611455827223248896 |