{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T09:09:28Z","timestamp":1781773768099,"version":"3.54.5"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Summary: PyRosetta is a stand-alone Python-based implementation of the Rosetta molecular modeling package that allows users to write custom structure prediction and design algorithms using the major Rosetta sampling and scoring functions. PyRosetta contains Python bindings to libraries that define Rosetta functions including those for accessing and manipulating protein structure, calculating energies and running Monte Carlo-based simulations. PyRosetta can be used in two ways: (i) interactively, using iPython and (ii) script-based, using Python scripting. Interactive mode contains a number of help features and is ideal for beginners while script-mode is best suited for algorithm development. PyRosetta has similar computational performance to Rosetta, can be easily scaled up for cluster applications and has been implemented for algorithms demonstrating protein docking, protein folding, loop modeling and design.<\/jats:p>\n               <jats:p>Availability: PyRosetta is a stand-alone package available at http:\/\/www.pyrosetta.org under the Rosetta license which is free for academic and non-profit users. A tutorial, user's manual and sample scripts demonstrating usage are also available on the web site.<\/jats:p>\n               <jats:p>Contact: \u00a0pyrosetta@graylab.jhu.edu<\/jats:p>","DOI":"10.1093\/bioinformatics\/btq007","type":"journal-article","created":{"date-parts":[[2010,1,9]],"date-time":"2010-01-09T01:13:49Z","timestamp":1262999629000},"page":"689-691","source":"Crossref","is-referenced-by-count":837,"title":["PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta"],"prefix":"10.1093","volume":"26","author":[{"given":"Sidhartha","family":"Chaudhury","sequence":"first","affiliation":[{"name":"1 Program in Molecular Biophysics, 2 Department of Chemical and Biomolecular Engineering and 3 Institute of NanoBioTechnology, Sidney Kimmel Comprehensive Cancer Center and Institute of Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergey","family":"Lyskov","sequence":"additional","affiliation":[{"name":"1 Program in Molecular Biophysics, 2 Department of Chemical and Biomolecular Engineering and 3 Institute of NanoBioTechnology, Sidney Kimmel Comprehensive Cancer Center and Institute of Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey J.","family":"Gray","sequence":"additional","affiliation":[{"name":"1 Program in Molecular Biophysics, 2 Department of Chemical and Biomolecular Engineering and 3 Institute of NanoBioTechnology, Sidney Kimmel Comprehensive Cancer Center and Institute of Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA"},{"name":"1 Program in Molecular Biophysics, 2 Department of Chemical and Biomolecular Engineering and 3 Institute of NanoBioTechnology, Sidney Kimmel Comprehensive Cancer Center and Institute of Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA"},{"name":"1 Program in Molecular Biophysics, 2 Department of Chemical and Biomolecular Engineering and 3 Institute of NanoBioTechnology, Sidney Kimmel Comprehensive Cancer Center and Institute of Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2010,1,7]]},"reference":[{"key":"2023012511010425800_B1","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/j.str.2008.01.012","article-title":"Structure prediction of domain insertion proteins from structures of individual domains","volume":"16","author":"Berrondo","year":"2008","journal-title":"Structure"},{"key":"2023012511010425800_B2","author":"Boost","year":"2009","journal-title":"Boost C++ libraries."},{"key":"2023012511010425800_B3","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1002\/prot.21133","article-title":"Improved beta-protein structure prediction by multilevel optimization of nonlocal strand pairings and local backbone conformation","volume":"65","author":"Bradley","year":"2006","journal-title":"Proteins"},{"key":"2023012511010425800_B4","doi-asserted-by":"crossref","first-page":"1868","DOI":"10.1126\/science.1113801","article-title":"Toward high-resolution de novo structure prediction for small proteins","volume":"309","author":"Bradley","year":"2005","journal-title":"Science"},{"key":"2023012511010425800_B5","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1016\/j.jmb.2008.05.042","article-title":"Conformer selection and induced fit in flexible backbone protein-protein docking using computational and NMR ensembles","volume":"381","author":"Chaudhury","year":"2008","journal-title":"J. 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