Treffer: py-MCMD: Python Software for Performing Hybrid Monte Carlo/Molecular Dynamics Simulations with GOMC and NAMD.
Title:
py-MCMD: Python Software for Performing Hybrid Monte Carlo/Molecular Dynamics Simulations with GOMC and NAMD.
Authors:
Barhaghi MS; Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States., Crawford B; Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, Michigan 48202, United States., Schwing G; Department of Computer Science, Wayne State University, Detroit, Michigan 48202, United States., Hardy DJ; Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States., Stone JE; Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States., Schwiebert L; Department of Computer Science, Wayne State University, Detroit, Michigan 48202, United States., Potoff J; Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, Michigan 48202, United States., Tajkhorshid E; Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.
Source:
Journal of chemical theory and computation [J Chem Theory Comput] 2022 Aug 09; Vol. 18 (8), pp. 4983-4994. Date of Electronic Publication: 2022 May 27.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: American Chemical Society Country of Publication: United States NLM ID: 101232704 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1549-9626 (Electronic) Linking ISSN: 15499618 NLM ISO Abbreviation: J Chem Theory Comput Subsets: MEDLINE
Imprint Name(s):
Original Publication: Washington, D.C. : American Chemical Society, c2005-
MeSH Terms:
References:
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Soft Matter. 2018 Jan 17;14(3):411-423. (PMID: 29251311)
Proc Natl Acad Sci U S A. 2005 May 10;102(19):6679-85. (PMID: 15870208)
J Chem Phys. 2004 Oct 1;121(13):6392-400. (PMID: 15446937)
Chem Rev. 2019 May 8;119(9):6086-6161. (PMID: 30978005)
Proc Natl Acad Sci U S A. 2018 Dec 11;115(50):12751-12756. (PMID: 30482862)
J Mol Biol. 2009 Apr 17;387(5):1165-85. (PMID: 19248790)
J Chem Theory Comput. 2020 Mar 10;16(3):1779-1793. (PMID: 32004433)
Nat Methods. 2017 Jan;14(1):71-73. (PMID: 27819658)
J Comput Chem. 2013 Sep 30;34(25):2135-45. (PMID: 23832629)
J Am Chem Soc. 2015 Feb 25;137(7):2695-703. (PMID: 25625324)
J Comput Chem. 2012 May 5;33(12):1207-14. (PMID: 22370965)
Nat Struct Biol. 2003 Dec;10(12):980. (PMID: 14634627)
J Chem Inf Model. 2020 Oct 26;60(10):4436-4441. (PMID: 32835483)
J Chem Theory Comput. 2015 Aug 11;11(8):3696-713. (PMID: 26574453)
Mol Phys. 2020;118(9-10):. (PMID: 33100401)
J Chem Phys. 2018 Aug 21;149(7):072318. (PMID: 30134670)
J Comput Chem. 2006 Jan 30;27(2):203-16. (PMID: 16323162)
J Chem Phys. 2015 Jan 14;142(2):024101. (PMID: 25591332)
J Am Chem Soc. 2011 Mar 16;133(10):3625-34. (PMID: 21341653)
Annu Rev Biophys. 2012;41:429-52. (PMID: 22577825)
J Phys Chem B. 2016 Apr 21;120(15):3692-8. (PMID: 27031562)
Phys Rev A Gen Phys. 1986 Sep;34(3):2499-2500. (PMID: 9897546)
Biophys J. 2000 Aug;79(2):788-801. (PMID: 10920012)
J Mol Graph. 1996 Feb;14(1):33-8, 27-8. (PMID: 8744570)
J Chem Theory Comput. 2016 Nov 8;12(11):5501-5510. (PMID: 27685170)
J Chem Phys. 2008 Mar 21;128(11):115103. (PMID: 18361618)
J Chem Theory Comput. 2020 Oct 13;16(10):6061-6076. (PMID: 32955877)
Structure. 2009 Oct 14;17(10):1295-306. (PMID: 19836330)
Phys Rev A Gen Phys. 1985 Mar;31(3):1695-1697. (PMID: 9895674)
J Chem Theory Comput. 2016 Jan 12;12(1):405-13. (PMID: 26631602)
Nucleic Acids Res. 2000 Jan 1;28(1):235-42. (PMID: 10592235)
Front Cell Dev Biol. 2020 Nov 30;8:601145. (PMID: 33330494)
Chem Phys Lipids. 2015 Nov;192:60-74. (PMID: 26238099)
Mol Biol Cell. 2018 Apr 1;29(7):834-845. (PMID: 29444959)
Nucleic Acids Res. 2021 Jan 8;49(D1):D437-D451. (PMID: 33211854)
J Chem Phys. 2020 Jul 28;153(4):044130. (PMID: 32752662)
J Phys Chem B. 1998 Apr 30;102(18):3586-616. (PMID: 24889800)
J Med Chem. 2016 May 12;59(9):4035-61. (PMID: 26807648)
J Comput Chem. 2009 Jul 30;30(10):1545-614. (PMID: 19444816)
J Comput Chem. 2008 Aug;29(11):1859-65. (PMID: 18351591)
Trends Biochem Sci. 2020 Mar;45(3):202-216. (PMID: 31813734)
J Chem Inf Model. 2019 Feb 25;59(2):754-765. (PMID: 30640456)
J Chem Phys. 2013 May 7;138(17):174102. (PMID: 23656109)
Genome Res. 1999 Nov;9(11):1106-15. (PMID: 10568750)
J Chem Theory Comput. 2018 Dec 11;14(12):6701-6713. (PMID: 30407818)
J Chem Theory Comput. 2019 Apr 9;15(4):2684-2691. (PMID: 30835999)
PLoS Comput Biol. 2017 Jul 26;13(7):e1005659. (PMID: 28746339)
Sci Rep. 2016 May 24;6:26536. (PMID: 27216779)
J Chem Phys. 2018 Aug 21;149(7):072325. (PMID: 30134684)
J Phys Chem B. 2014 Jan 16;118(2):547-56. (PMID: 24341749)
Proc Math Phys Eng Sci. 2016 May;472(2189):20160138. (PMID: 27279779)
J Chem Phys. 2011 Mar 28;134(12):124708. (PMID: 21456696)
J Chem Theory Comput. 2007 Jan;3(1):26-41. (PMID: 26627148)
Entropy (Basel). 2018 May;20(5):. (PMID: 30393452)
J Chem Phys. 2011 Oct 7;135(13):134121. (PMID: 21992296)
BMC Biol. 2011 Oct 28;9:71. (PMID: 22035460)
ACS Med Chem Lett. 2019 Dec 11;11(1):77-82. (PMID: 31938467)
J Chem Theory Comput. 2020 Dec 8;16(12):7883-7894. (PMID: 33206520)
J Mol Biol. 1984 Dec 5;180(2):301-29. (PMID: 6210373)
J Phys Chem B. 2010 Jun 17;114(23):7830-43. (PMID: 20496934)
Chem Rev. 2019 May 8;119(9):5607-5774. (PMID: 30859819)
J Chem Theory Comput. 2022 Aug 9;18(8):4983-4994. (PMID: 35621307)
J Chem Theory Comput. 2012 Sep 11;8(9):3257-3273. (PMID: 23341755)
J Chem Phys. 2008 Sep 28;129(12):124105. (PMID: 19045004)
Soft Matter. 2018 Jan 17;14(3):411-423. (PMID: 29251311)
Proc Natl Acad Sci U S A. 2005 May 10;102(19):6679-85. (PMID: 15870208)
J Chem Phys. 2004 Oct 1;121(13):6392-400. (PMID: 15446937)
Chem Rev. 2019 May 8;119(9):6086-6161. (PMID: 30978005)
Proc Natl Acad Sci U S A. 2018 Dec 11;115(50):12751-12756. (PMID: 30482862)
J Mol Biol. 2009 Apr 17;387(5):1165-85. (PMID: 19248790)
J Chem Theory Comput. 2020 Mar 10;16(3):1779-1793. (PMID: 32004433)
Nat Methods. 2017 Jan;14(1):71-73. (PMID: 27819658)
J Comput Chem. 2013 Sep 30;34(25):2135-45. (PMID: 23832629)
J Am Chem Soc. 2015 Feb 25;137(7):2695-703. (PMID: 25625324)
J Comput Chem. 2012 May 5;33(12):1207-14. (PMID: 22370965)
Grant Information:
P41 GM104601 United States GM NIGMS NIH HHS; R24 GM145965 United States GM NIGMS NIH HHS
Substance Nomenclature:
059QF0KO0R (Water)
Entry Date(s):
Date Created: 20220527 Date Completed: 20220810 Latest Revision: 20230810
Update Code:
20250114
PubMed Central ID:
PMC9760104
DOI:
10.1021/acs.jctc.1c00911
PMID:
35621307
Database:
MEDLINE
Weitere Informationen
py-MCMD, an open-source Python software, provides a robust workflow layer that manages communication of relevant system information between the simulation engines NAMD and GOMC and generates coherent thermodynamic properties and trajectories for analysis. To validate the workflow and highlight its capabilities, hybrid Monte Carlo/molecular dynamics (MC/MD) simulations are performed for SPC/E water in the isobaric-isothermal ( NPT ) and grand canonical (GC) ensembles as well as with Gibbs ensemble Monte Carlo (GEMC). The hybrid MC/MD approach shows close agreement with reference MC simulations and has a computational efficiency that is 2 to 136 times greater than traditional Monte Carlo simulations. MC/MD simulations performed for water in a graphene slit pore illustrate significant gains in sampling efficiency when the coupled-decoupled configurational-bias MC (CD-CBMC) algorithm is used compared with simulations using a single unbiased random trial position. Simulations using CD-CBMC reach equilibrium with 25 times fewer cycles than simulations using a single unbiased random trial position, with a small increase in computational cost. In a more challenging application, hybrid grand canonical Monte Carlo/molecular dynamics (GCMC/MD) simulations are used to hydrate a buried binding pocket in bovine pancreatic trypsin inhibitor. Water occupancies produced by GCMC/MD simulations are in close agreement with crystallographically identified positions, and GCMC/MD simulations have a computational efficiency that is 5 times better than MD simulations. py-MCMD is available on GitHub at https://github.com/GOMC-WSU/py-MCMD.