pyCHARMM: Embedding CHARMM Functionality in a Python Framework
CHARMM is rich in methodology and functionality as one of the first programs addressing problems of molecular dynamics and modeling of biological macromolecules and their partners, e.g., small molecule ligands. When combined with the highly developed CHARMM parameters for proteins, nucleic acids, sm...
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Veröffentlicht in: | Journal of chemical theory and computation 2023-06, Vol.19 (12), p.3752-3762 |
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creator | Buckner, Joshua Liu, Xiaorong Chakravorty, Arghya Wu, Yujin Cervantes, Luis F. Lai, Thanh T. |
description | CHARMM is rich in methodology and functionality as one of the first programs addressing problems of molecular dynamics and modeling of biological macromolecules and their partners, e.g., small molecule ligands. When combined with the highly developed CHARMM parameters for proteins, nucleic acids, small molecules, lipids, sugars, and other biologically relevant building blocks, and the versatile CHARMM scripting language, CHARMM has been a trendsetting platform for modeling studies of biological macromolecules. To further enhance the utility of accessing and using CHARMM functionality in increasingly complex workflows associated with modeling biological systems, we introduce pyCHARMM, Python bindings, functions, and modules to complement and extend the extensive set of modeling tools and methods already available in CHARMM. These include access to CHARMM function-generated variables associated with the system (psf), coordinates, velocities and forces, atom selection variables, and force field related parameters. The ability to augment CHARMM forces and energies with energy terms or methods derived from machine learning or other sources, written in Python, CUDA, or OpenCL and expressed as Python callable routines is introduced together with analogous functions callable during dynamics calculations. Integration of Python-based graphical engines for visualization of simulation models and results is also accessible. Loosely coupled parallelism is available for workflows such as free energy calculations, using MBAR/TI approaches or high-throughput multisite λ-dynamics (MSλD) free energy methods, string path optimization calculations, replica exchange, and molecular docking with a new Python-based CDOCKER module. CHARMM accelerated platform kernels through the CHARMM/OpenMM API, CHARMM/DOMDEC, and CHARMM/BLaDE API are also readily integrated into this Python framework. We anticipate that pyCHARMM will be a robust platform for the development of comprehensive and complex workflows utilizing Python and its extensive functionality as well as an optimal platform for users to learn molecular modeling methods and practices within a Python-friendly environment such as Jupyter Notebooks. |
doi_str_mv | 10.1021/acs.jctc.3c00364 |
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When combined with the highly developed CHARMM parameters for proteins, nucleic acids, small molecules, lipids, sugars, and other biologically relevant building blocks, and the versatile CHARMM scripting language, CHARMM has been a trendsetting platform for modeling studies of biological macromolecules. To further enhance the utility of accessing and using CHARMM functionality in increasingly complex workflows associated with modeling biological systems, we introduce pyCHARMM, Python bindings, functions, and modules to complement and extend the extensive set of modeling tools and methods already available in CHARMM. These include access to CHARMM function-generated variables associated with the system (psf), coordinates, velocities and forces, atom selection variables, and force field related parameters. The ability to augment CHARMM forces and energies with energy terms or methods derived from machine learning or other sources, written in Python, CUDA, or OpenCL and expressed as Python callable routines is introduced together with analogous functions callable during dynamics calculations. Integration of Python-based graphical engines for visualization of simulation models and results is also accessible. Loosely coupled parallelism is available for workflows such as free energy calculations, using MBAR/TI approaches or high-throughput multisite λ-dynamics (MSλD) free energy methods, string path optimization calculations, replica exchange, and molecular docking with a new Python-based CDOCKER module. CHARMM accelerated platform kernels through the CHARMM/OpenMM API, CHARMM/DOMDEC, and CHARMM/BLaDE API are also readily integrated into this Python framework. We anticipate that pyCHARMM will be a robust platform for the development of comprehensive and complex workflows utilizing Python and its extensive functionality as well as an optimal platform for users to learn molecular modeling methods and practices within a Python-friendly environment such as Jupyter Notebooks.</description><identifier>ISSN: 1549-9618</identifier><identifier>EISSN: 1549-9626</identifier><identifier>DOI: 10.1021/acs.jctc.3c00364</identifier><identifier>PMID: 37267404</identifier><language>eng</language><publisher>United States: American Chemical Society</publisher><subject>Biomolecular Systems ; Energy methods ; Free energy ; Lipids ; Machine learning ; Macromolecules ; Modelling ; Modules ; Molecular docking ; Molecular Docking Simulation ; Molecular dynamics ; Molecular Dynamics Simulation ; Nucleic Acids ; Optimization ; Parameters ; Proteins - metabolism ; Simulation models</subject><ispartof>Journal of chemical theory and computation, 2023-06, Vol.19 (12), p.3752-3762</ispartof><rights>2023 American Chemical Society</rights><rights>Copyright American Chemical Society Jun 27, 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a420t-8d0a8958a370e79e5b5033942911ad9a1c0781a76d21bdb7fd3da00e684eb97a3</citedby><cites>FETCH-LOGICAL-a420t-8d0a8958a370e79e5b5033942911ad9a1c0781a76d21bdb7fd3da00e684eb97a3</cites><orcidid>0000-0002-4467-1135 ; 0000-0002-7687-3604 ; 0000-0002-8149-5417</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://pubs.acs.org/doi/pdf/10.1021/acs.jctc.3c00364$$EPDF$$P50$$Gacs$$H</linktopdf><linktohtml>$$Uhttps://pubs.acs.org/doi/10.1021/acs.jctc.3c00364$$EHTML$$P50$$Gacs$$H</linktohtml><link.rule.ids>230,314,780,784,885,2765,27076,27924,27925,56738,56788</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37267404$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Buckner, Joshua</creatorcontrib><creatorcontrib>Liu, Xiaorong</creatorcontrib><creatorcontrib>Chakravorty, Arghya</creatorcontrib><creatorcontrib>Wu, Yujin</creatorcontrib><creatorcontrib>Cervantes, Luis F.</creatorcontrib><creatorcontrib>Lai, Thanh T.</creatorcontrib><title>pyCHARMM: Embedding CHARMM Functionality in a Python Framework</title><title>Journal of chemical theory and computation</title><addtitle>J. Chem. Theory Comput</addtitle><description>CHARMM is rich in methodology and functionality as one of the first programs addressing problems of molecular dynamics and modeling of biological macromolecules and their partners, e.g., small molecule ligands. When combined with the highly developed CHARMM parameters for proteins, nucleic acids, small molecules, lipids, sugars, and other biologically relevant building blocks, and the versatile CHARMM scripting language, CHARMM has been a trendsetting platform for modeling studies of biological macromolecules. To further enhance the utility of accessing and using CHARMM functionality in increasingly complex workflows associated with modeling biological systems, we introduce pyCHARMM, Python bindings, functions, and modules to complement and extend the extensive set of modeling tools and methods already available in CHARMM. These include access to CHARMM function-generated variables associated with the system (psf), coordinates, velocities and forces, atom selection variables, and force field related parameters. The ability to augment CHARMM forces and energies with energy terms or methods derived from machine learning or other sources, written in Python, CUDA, or OpenCL and expressed as Python callable routines is introduced together with analogous functions callable during dynamics calculations. Integration of Python-based graphical engines for visualization of simulation models and results is also accessible. Loosely coupled parallelism is available for workflows such as free energy calculations, using MBAR/TI approaches or high-throughput multisite λ-dynamics (MSλD) free energy methods, string path optimization calculations, replica exchange, and molecular docking with a new Python-based CDOCKER module. CHARMM accelerated platform kernels through the CHARMM/OpenMM API, CHARMM/DOMDEC, and CHARMM/BLaDE API are also readily integrated into this Python framework. 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Liu, Xiaorong ; Chakravorty, Arghya ; Wu, Yujin ; Cervantes, Luis F. ; Lai, Thanh T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a420t-8d0a8958a370e79e5b5033942911ad9a1c0781a76d21bdb7fd3da00e684eb97a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Biomolecular Systems</topic><topic>Energy methods</topic><topic>Free energy</topic><topic>Lipids</topic><topic>Machine learning</topic><topic>Macromolecules</topic><topic>Modelling</topic><topic>Modules</topic><topic>Molecular docking</topic><topic>Molecular Docking Simulation</topic><topic>Molecular dynamics</topic><topic>Molecular Dynamics Simulation</topic><topic>Nucleic Acids</topic><topic>Optimization</topic><topic>Parameters</topic><topic>Proteins - metabolism</topic><topic>Simulation models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Buckner, Joshua</creatorcontrib><creatorcontrib>Liu, Xiaorong</creatorcontrib><creatorcontrib>Chakravorty, Arghya</creatorcontrib><creatorcontrib>Wu, Yujin</creatorcontrib><creatorcontrib>Cervantes, Luis F.</creatorcontrib><creatorcontrib>Lai, Thanh T.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of chemical theory and computation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Buckner, Joshua</au><au>Liu, Xiaorong</au><au>Chakravorty, Arghya</au><au>Wu, Yujin</au><au>Cervantes, Luis F.</au><au>Lai, Thanh T.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>pyCHARMM: Embedding CHARMM Functionality in a Python Framework</atitle><jtitle>Journal of chemical theory and computation</jtitle><addtitle>J. Chem. Theory Comput</addtitle><date>2023-06-27</date><risdate>2023</risdate><volume>19</volume><issue>12</issue><spage>3752</spage><epage>3762</epage><pages>3752-3762</pages><issn>1549-9618</issn><eissn>1549-9626</eissn><abstract>CHARMM is rich in methodology and functionality as one of the first programs addressing problems of molecular dynamics and modeling of biological macromolecules and their partners, e.g., small molecule ligands. When combined with the highly developed CHARMM parameters for proteins, nucleic acids, small molecules, lipids, sugars, and other biologically relevant building blocks, and the versatile CHARMM scripting language, CHARMM has been a trendsetting platform for modeling studies of biological macromolecules. To further enhance the utility of accessing and using CHARMM functionality in increasingly complex workflows associated with modeling biological systems, we introduce pyCHARMM, Python bindings, functions, and modules to complement and extend the extensive set of modeling tools and methods already available in CHARMM. These include access to CHARMM function-generated variables associated with the system (psf), coordinates, velocities and forces, atom selection variables, and force field related parameters. The ability to augment CHARMM forces and energies with energy terms or methods derived from machine learning or other sources, written in Python, CUDA, or OpenCL and expressed as Python callable routines is introduced together with analogous functions callable during dynamics calculations. Integration of Python-based graphical engines for visualization of simulation models and results is also accessible. Loosely coupled parallelism is available for workflows such as free energy calculations, using MBAR/TI approaches or high-throughput multisite λ-dynamics (MSλD) free energy methods, string path optimization calculations, replica exchange, and molecular docking with a new Python-based CDOCKER module. CHARMM accelerated platform kernels through the CHARMM/OpenMM API, CHARMM/DOMDEC, and CHARMM/BLaDE API are also readily integrated into this Python framework. 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subjects | Biomolecular Systems Energy methods Free energy Lipids Machine learning Macromolecules Modelling Modules Molecular docking Molecular Docking Simulation Molecular dynamics Molecular Dynamics Simulation Nucleic Acids Optimization Parameters Proteins - metabolism Simulation models |
title | pyCHARMM: Embedding CHARMM Functionality in a Python Framework |
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