Prediction of Enzyme Catalysis by Computing Reaction Energy Barriers via Steered QM/MM Molecular Dynamics Simulations and Machine Learning.


Journal

Journal of chemical information and modeling
ISSN: 1549-960X
Titre abrégé: J Chem Inf Model
Pays: United States
ID NLM: 101230060

Informations de publication

Date de publication:
14 08 2023
Historique:
pmc-release: 21 07 2024
medline: 15 8 2023
pubmed: 22 7 2023
entrez: 21 7 2023
Statut: ppublish

Résumé

The prediction of enzyme activity is one of the main challenges in catalysis. With computer-aided methods, it is possible to simulate the reaction mechanism at the atomic level. However, these methods are usually expensive if they are to be used on a large scale, as they are needed for protein engineering campaigns. To alleviate this situation, machine learning methods can help in the generation of predictive-decision models. Herein, we test different regression algorithms for the prediction of the reaction energy barrier of the rate-limiting step of the hydrolysis of mono-(2-hydroxyethyl)terephthalic acid by the MHETase of

Identifiants

pubmed: 37479222
doi: 10.1021/acs.jcim.3c00772
pmc: PMC10430765
doi:

Substances chimiques

Hydrolases EC 3.-

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

4623-4632

Références

Nat Commun. 2019 Apr 12;10(1):1717
pubmed: 30979881
Chembiochem. 2021 Jun 15;22(12):2032-2050
pubmed: 33470503
J Chem Theory Comput. 2012 Apr 10;7(4):931-948
pubmed: 23204947
Appl Microbiol Biotechnol. 2019 Jun;103(11):4253-4268
pubmed: 30957199
J Chem Theory Comput. 2013 Jul 9;9(7):3084-95
pubmed: 26583988
Proc Natl Acad Sci U S A. 2002 Oct 1;99(20):12562-6
pubmed: 12271136
Biochemistry. 2018 Feb 27;57(8):1338-1348
pubmed: 29360348
Nucleic Acids Res. 2012 Jul;40(Web Server issue):W537-41
pubmed: 22570416
J Biotechnol. 2017 Nov 10;261:194-206
pubmed: 28438579
Org Biomol Chem. 2019 Aug 28;17(34):7891-7899
pubmed: 31397456
Nucleic Acids Res. 2018 Jul 2;46(W1):W356-W362
pubmed: 29796670
Nat Chem. 2021 Jun;13(6):505-508
pubmed: 34059804
Science. 2004 Jan 9;303(5655):186-95
pubmed: 14716003
Phys Rev Lett. 2012 Feb 3;108(5):058301
pubmed: 22400967
J Chem Theory Comput. 2019 Dec 10;15(12):6660-6667
pubmed: 31765138
Curr Opin Chem Biol. 2011 Apr;15(2):201-10
pubmed: 21185770
ACS Omega. 2020 Mar 20;5(12):6487-6493
pubmed: 32258884
Proc Natl Acad Sci U S A. 2020 Oct 13;117(41):25476-25485
pubmed: 32989159
Curr Opin Biotechnol. 2002 Aug;13(4):345-51
pubmed: 12323357
Biotechnol Adv. 2015 Nov 15;33(7):1443-54
pubmed: 25747291
J Chem Theory Comput. 2020 Jan 14;16(1):528-552
pubmed: 31714766
Chem Rev. 2018 Jan 24;118(2):801-838
pubmed: 28876904
Phys Rev B Condens Matter. 1988 Jan 15;37(2):785-789
pubmed: 9944570
Nat Commun. 2021 Jul 22;12(1):4468
pubmed: 34294693
APL Bioeng. 2018 Jan 11;2(1):011501
pubmed: 31069285
Chem Rev. 2019 Jun 12;119(11):6613-6630
pubmed: 30277066
J Am Chem Soc. 2017 May 24;139(20):6780-6786
pubmed: 28493715
Sci Rep. 2019 Dec 30;9(1):20285
pubmed: 31889089
Front Microbiol. 2014 Apr 17;5:172
pubmed: 24860555
Nature. 2022 Apr;604(7907):662-667
pubmed: 35478237
J Am Chem Soc. 2019 Mar 6;141(9):4108-4118
pubmed: 30761897
Mol Cell. 2018 Oct 4;72(1):178-186.e5
pubmed: 30270109
Nucleic Acids Res. 2018 Jan 4;46(D1):D656-D660
pubmed: 29092055
J Chem Inf Model. 2020 Mar 23;60(3):1833-1843
pubmed: 32053362
J Am Chem Soc. 2015 Dec 2;137(47):14905-11
pubmed: 26555770
Mol Cell. 2016 Jul 21;63(2):337-346
pubmed: 27425410
J Chem Phys. 2011 Feb 21;134(7):074106
pubmed: 21341827
Chem Rev. 2021 Aug 25;121(16):9816-9872
pubmed: 34232033
Biophys J. 2012 Aug 22;103(4):786-96
pubmed: 22947940
J Chem Phys. 2004 Apr 1;120(13):5946-61
pubmed: 15267476
ACS Phys Chem Au. 2022 Jul 27;2(4):316-330
pubmed: 35936506

Auteurs

Daniel Platero-Rochart (D)

Laboratory of Computer-Aided Molecular Design, Division of Medicinal Chemistry, Otto-Loewi Research Center, Medical University of Graz, Neue Stiftingtalstraße 6/III, A-8010 Graz, Austria.

Tatyana Krivobokova (T)

Department of Statistics and Operations Research, University of Vienna, Oskar-Morgenstern-Platz 1, A-1090 Vienna, Austria.

Michael Gastegger (M)

Institute of Software Engineering and Theoretical Computer Science, Machine Learning Group, Technische Universität, 10587 Berlin, Germany.

Gilbert Reibnegger (G)

Laboratory of Computer-Aided Molecular Design, Division of Medicinal Chemistry, Otto-Loewi Research Center, Medical University of Graz, Neue Stiftingtalstraße 6/III, A-8010 Graz, Austria.

Pedro A Sánchez-Murcia (PA)

Laboratory of Computer-Aided Molecular Design, Division of Medicinal Chemistry, Otto-Loewi Research Center, Medical University of Graz, Neue Stiftingtalstraße 6/III, A-8010 Graz, Austria.

Articles similaires

Photosynthesis Ribulose-Bisphosphate Carboxylase Carbon Dioxide Molecular Dynamics Simulation Cyanobacteria
Fucosyltransferases Drug Repositioning Molecular Docking Simulation Molecular Dynamics Simulation Humans
Receptor, Cannabinoid, CB1 Ligands Molecular Dynamics Simulation Protein Binding Thermodynamics

Amyloid accelerator polyphosphate fits as the mystery density in α-synuclein fibrils.

Philipp Huettemann, Pavithra Mahadevan, Justine Lempart et al.
1.00
Polyphosphates alpha-Synuclein Humans Amyloid Molecular Dynamics Simulation

Classifications MeSH