dc.contributor.author | Buslaev, Pavel | |
dc.contributor.author | Aho, Noora | |
dc.contributor.author | Jansen, Anton | |
dc.contributor.author | Bauer, Paul | |
dc.contributor.author | Hess, Berk | |
dc.contributor.author | Groenhof, Gerrit | |
dc.date.accessioned | 2022-09-20T07:49:58Z | |
dc.date.available | 2022-09-20T07:49:58Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Buslaev, 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.other | CONVID_156578665 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/83280 | |
dc.description.abstract | Various 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.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | American Chemical Society (ACS) | |
dc.relation.ispartofseries | Journal of Chemical Theory and Computation | |
dc.rights | CC BY 4.0 | |
dc.subject.other | computer simulations | |
dc.subject.other | molecular mechanics | |
dc.subject.other | monomers | |
dc.subject.other | peptides and proteins | |
dc.subject.other | reaction mechanisms | |
dc.title | Best Practices in Constant pH MD Simulations : Accuracy and Sampling | |
dc.type | research article | |
dc.identifier.urn | URN:NBN:fi:jyu-202209204623 | |
dc.contributor.laitos | Kemian laitos | fi |
dc.contributor.laitos | Department of Chemistry | en |
dc.contributor.oppiaine | Orgaaninen kemia | fi |
dc.contributor.oppiaine | Fysikaalinen kemia | fi |
dc.contributor.oppiaine | Nanoscience Center | fi |
dc.contributor.oppiaine | Organic Chemistry | en |
dc.contributor.oppiaine | Physical Chemistry | en |
dc.contributor.oppiaine | Nanoscience Center | en |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 6134-6147 | |
dc.relation.issn | 1549-9618 | |
dc.relation.numberinseries | 10 | |
dc.relation.volume | 18 | |
dc.type.version | publishedVersion | |
dc.rights.copyright | © 2022 The Authors. Published by American Chemical Society | |
dc.rights.accesslevel | openAccess | fi |
dc.type.publication | article | |
dc.relation.grantnumber | 311031 | |
dc.relation.grantnumber | 342908 | |
dc.relation.grantnumber | 823830 | |
dc.relation.grantnumber | 823830 | |
dc.relation.grantnumber | 332743 | |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/H2020/823830/EU//BioExcel-2 | |
dc.subject.yso | simulointi | |
dc.subject.yso | molekyylit | |
dc.subject.yso | molekyylidynamiikka | |
dc.subject.yso | peptidit | |
dc.subject.yso | reaktiomekanismit | |
dc.subject.yso | mallintaminen | |
dc.subject.yso | proteiinit | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p4787 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p2984 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p29332 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p15258 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p21536 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p3533 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p4332 | |
dc.rights.url | https://creativecommons.org/licenses/by/4.0/ | |
dc.relation.doi | 10.1021/acs.jctc.2c00517 | |
dc.relation.funder | Research Council of Finland | en |
dc.relation.funder | Research Council of Finland | en |
dc.relation.funder | European Commission | en |
dc.relation.funder | Research Council of Finland | en |
dc.relation.funder | Suomen Akatemia | fi |
dc.relation.funder | Suomen Akatemia | fi |
dc.relation.funder | Euroopan komissio | fi |
dc.relation.funder | Suomen Akatemia | fi |
jyx.fundingprogram | Academy Project, AoF | en |
jyx.fundingprogram | Postdoctoral Researcher, AoF | en |
jyx.fundingprogram | Research infrastructures, H2020 | en |
jyx.fundingprogram | Academy Project, AoF | en |
jyx.fundingprogram | Akatemiahanke, SA | fi |
jyx.fundingprogram | Tutkijatohtori, SA | fi |
jyx.fundingprogram | Research infrastructures, H2020 | fi |
jyx.fundingprogram | Akatemiahanke, SA | fi |
jyx.fundinginformation | This 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.okm | A1 | |