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dc.contributor.authorBuslaev, Pavel
dc.contributor.authorAho, Noora
dc.contributor.authorJansen, Anton
dc.contributor.authorBauer, Paul
dc.contributor.authorHess, Berk
dc.contributor.authorGroenhof, Gerrit
dc.date.accessioned2022-09-20T07:49:58Z
dc.date.available2022-09-20T07:49:58Z
dc.date.issued2022
dc.identifier.citationBuslaev, P., Aho, N., Jansen, A., Bauer, P., Hess, B., & Groenhof, G. (2022). Best Practices in Constant pH MD Simulations : Accuracy and Sampling. <i>Journal of Chemical Theory and Computation</i>, <i>18</i>(10), 6134-6147. <a href="https://doi.org/10.1021/acs.jctc.2c00517" target="_blank">https://doi.org/10.1021/acs.jctc.2c00517</a>
dc.identifier.otherCONVID_156578665
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/83280
dc.description.abstractVarious approaches have been proposed to include the effect of pH in molecular dynamics (MD) simulations. Among these, the λ-dynamics approach proposed by Brooks and co-workers [Kong, X.; Brooks III, C. L. J. Chem. Phys.1996, 105, 2414−2423] can be performed with little computational overhead and hfor each typeence be used to routinely perform MD simulations at microsecond time scales, as shown in the accompanying paper [Aho, N. et al. J. Chem. Theory Comput.2022, DOI: 10.1021/acs.jctc.2c00516]. At such time scales, however, the accuracy of the molecular mechanics force field and the parametrization becomes critical. Here, we address these issues and provide the community with guidelines on how to set up and perform long time scale constant pH MD simulations. We found that barriers associated with the torsions of side chains in the CHARMM36m force field are too high for reaching convergence in constant pH MD simulations on microsecond time scales. To avoid the high computational cost of extending the sampling, we propose small modifications to the force field to selectively reduce the torsional barriers. We demonstrate that with such modifications we obtain converged distributions of both protonation and torsional degrees of freedom and hence consistent pKa estimates, while the sampling of the overall configurational space accessible to proteins is unaffected as compared to normal MD simulations. We also show that the results of constant pH MD depend on the accuracy of the correction potentials. While these potentials are typically obtained by fitting a low-order polynomial to calculated free energy profiles, we find that higher order fits are essential to provide accurate and consistent results. By resolving problems in accuracy and sampling, the work described in this and the accompanying paper paves the way to the widespread application of constant pH MD beyond pKa prediction.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherAmerican Chemical Society (ACS)
dc.relation.ispartofseriesJournal of Chemical Theory and Computation
dc.rightsCC BY 4.0
dc.subject.othercomputer simulations
dc.subject.othermolecular mechanics
dc.subject.othermonomers
dc.subject.otherpeptides and proteins
dc.subject.otherreaction mechanisms
dc.titleBest Practices in Constant pH MD Simulations : Accuracy and Sampling
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202209204623
dc.contributor.laitosKemian laitosfi
dc.contributor.laitosDepartment of Chemistryen
dc.contributor.oppiaineOrgaaninen kemiafi
dc.contributor.oppiaineFysikaalinen kemiafi
dc.contributor.oppiaineNanoscience Centerfi
dc.contributor.oppiaineOrganic Chemistryen
dc.contributor.oppiainePhysical Chemistryen
dc.contributor.oppiaineNanoscience Centeren
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange6134-6147
dc.relation.issn1549-9618
dc.relation.numberinseries10
dc.relation.volume18
dc.type.versionpublishedVersion
dc.rights.copyright© 2022 The Authors. Published by American Chemical Society
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber311031
dc.relation.grantnumber342908
dc.relation.grantnumber823830
dc.relation.grantnumber823830
dc.relation.grantnumber332743
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/823830/EU//BioExcel-2
dc.subject.ysosimulointi
dc.subject.ysomolekyylit
dc.subject.ysomolekyylidynamiikka
dc.subject.ysopeptidit
dc.subject.ysoreaktiomekanismit
dc.subject.ysomallintaminen
dc.subject.ysoproteiinit
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p4787
jyx.subject.urihttp://www.yso.fi/onto/yso/p2984
jyx.subject.urihttp://www.yso.fi/onto/yso/p29332
jyx.subject.urihttp://www.yso.fi/onto/yso/p15258
jyx.subject.urihttp://www.yso.fi/onto/yso/p21536
jyx.subject.urihttp://www.yso.fi/onto/yso/p3533
jyx.subject.urihttp://www.yso.fi/onto/yso/p4332
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1021/acs.jctc.2c00517
dc.relation.funderResearch Council of Finlanden
dc.relation.funderResearch Council of Finlanden
dc.relation.funderEuropean Commissionen
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
dc.relation.funderEuroopan komissiofi
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundingprogramPostdoctoral Researcher, AoFen
jyx.fundingprogramResearch infrastructures, H2020en
jyx.fundingprogramAcademy Project, AoFen
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundingprogramTutkijatohtori, SAfi
jyx.fundingprogramResearch infrastructures, H2020fi
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundinginformationThis research was supported by the Swedish Research Council (grant no. 2019-04477), Academy of Finland (grants 311031, 332743, and 342908), and BioExcel CoE (Grant No. H2020-INFRAEDI-02-2018-823830). The simulations were performed on resources provided by the CSC-IT Center for Science, Finland, and the Swedish National Infrastructure for Computing (SNIC 2021/1-38). We thank Dmitry Morozov for help with obtaining the torsion potentials at the MP2 level of theory.
dc.type.okmA1


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