{"entity": "researcher", "timestamp": "2026-08-22T08:35:03.963Z", "family": "Bauer", "given": "Paul", "initials": "P", "orcid": "0000-0002-2268-0065", "affiliations": ["Department of Applied Physics and Swedish e-Science Research Center, Science for Life Laboratory, KTH Royal Institute of Technology, 100 44Stockholm, Sweden."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/6298095af1ec4ad9956937755998d29b.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/6298095af1ec4ad9956937755998d29b"}}, "publications": [{"entity": "publication", "iuid": "5b43b70af750421ba91f5ec392c780a5", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/5b43b70af750421ba91f5ec392c780a5.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/5b43b70af750421ba91f5ec392c780a5"}}, "title": "Best Practices in Constant pH MD Simulations: Accuracy and Sampling.", "authors": [{"family": "Buslaev", "given": "Pavel", "initials": "P", "orcid": "0000-0003-2031-4691", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c0cd04388c6e49d88506e676091e78df.json"}}, {"family": "Aho", "given": "Noora", "initials": "N"}, {"family": "Jansen", "given": "Anton", "initials": "A"}, {"family": "Bauer", "given": "Paul", "initials": "P", "orcid": "0000-0002-2268-0065", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/6298095af1ec4ad9956937755998d29b.json"}}, {"family": "Hess", "given": "Berk", "initials": "B"}, {"family": "Groenhof", "given": "Gerrit", "initials": "G", "orcid": "0000-0001-8148-5334", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c6b4d7e99ff644b1a39e18533528b975.json"}}], "type": "journal article", "published": "2022-10-11", "journal": {"title": "J Chem Theory Comput", "issn": "1549-9626", "volume": "18", "issue": "10", "pages": "6134-6147", "issn-l": "1549-9618"}, "abstract": "Various approaches have been proposed to include the effect of pH in molecular dynamics (MD) simulations. Among these, the \u03bb-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.", "doi": "10.1021/acs.jctc.2c00517", "pmid": "36107791", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC9558372"}], "notes": [], "created": "2026-08-20T08:10:45.612Z", "modified": "2026-08-20T08:10:45.695Z"}, {"entity": "publication", "iuid": "d07cde9818eb487babc38c551956fcec", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/d07cde9818eb487babc38c551956fcec.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/d07cde9818eb487babc38c551956fcec"}}, "title": "Scalable Constant pH Molecular Dynamics in GROMACS.", "authors": [{"family": "Aho", "given": "Noora", "initials": "N"}, {"family": "Buslaev", "given": "Pavel", "initials": "P", "orcid": "0000-0003-2031-4691", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c0cd04388c6e49d88506e676091e78df.json"}}, {"family": "Jansen", "given": "Anton", "initials": "A"}, {"family": "Bauer", "given": "Paul", "initials": "P", "orcid": "0000-0002-2268-0065", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/6298095af1ec4ad9956937755998d29b.json"}}, {"family": "Groenhof", "given": "Gerrit", "initials": "G", "orcid": "0000-0001-8148-5334", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c6b4d7e99ff644b1a39e18533528b975.json"}}, {"family": "Hess", "given": "Berk", "initials": "B"}], "type": "journal article", "published": "2022-10-11", "journal": {"title": "J Chem Theory Comput", "issn": "1549-9626", "volume": "18", "issue": "10", "pages": "6148-6160", "issn-l": "1549-9618"}, "abstract": "Molecular dynamics (MD) computer simulations are used routinely to compute atomistic trajectories of complex systems. Systems are simulated in various ensembles, depending on the experimental conditions one aims to mimic. While constant energy, temperature, volume, and pressure are rather straightforward to model, pH, which is an equally important parameter in experiments, is more difficult to account for in simulations. Although a constant pH algorithm based on the \u03bb-dynamics approach by Brooks and co-workers [Kong, X.; Brooks III, C. L. J. Chem. Phys.1996, 105, 2414-2423] was implemented in a fork of the GROMACS molecular dynamics program, uptake has been rather limited, presumably due to the poor scaling of that code with respect to the number of titratable sites. To overcome this limitation, we implemented an alternative scheme for interpolating the Hamiltonians of the protonation states that makes the constant pH molecular dynamics simulations almost as fast as a normal MD simulation with GROMACS. In addition, we implemented a simpler scheme, called multisite representation, for modeling side chains with multiple titratable sites, such as imidazole rings. This scheme, which is based on constraining the sum of the \u03bb-coordinates, not only reduces the complexity associated with parametrizing the intramolecular interactions between the sites but also is easily extendable to other molecules with multiple titratable sites. With the combination of a more efficient interpolation scheme and multisite representation of titratable groups, we anticipate a rapid uptake of constant pH molecular dynamics simulations within the GROMACS user community.", "doi": "10.1021/acs.jctc.2c00516", "pmid": "36128977", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC9558312"}], "notes": [], "created": "2026-08-20T08:10:43.796Z", "modified": "2026-08-20T08:10:43.917Z"}, {"entity": "publication", "iuid": "19368aab418345f7bd0770198bb0cd57", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/19368aab418345f7bd0770198bb0cd57.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/19368aab418345f7bd0770198bb0cd57"}}, "title": "Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS.", "authors": [{"family": "P\u00e1ll", "given": "Szil\u00e1rd", "initials": "S", "orcid": "0000-0003-0603-5514", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9ede2bb18049424894a05509e29e91c9.json"}}, {"family": "Zhmurov", "given": "Artem", "initials": "A"}, {"family": "Bauer", "given": "Paul", "initials": "P", "orcid": "0000-0002-2268-0065", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/6298095af1ec4ad9956937755998d29b.json"}}, {"family": "Abraham", "given": "Mark", "initials": "M", "orcid": "0000-0001-6363-2521", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8221a33c9d53447b93db57bc5636f5f2.json"}}, {"family": "Lundborg", "given": "Magnus", "initials": "M", "orcid": "0000-0002-0873-7854", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ab1b1b7c73fd44b9b880106feaa56feb.json"}}, {"family": "Gray", "given": "Alan", "initials": "A"}, {"family": "Hess", "given": "Berk", "initials": "B", "orcid": "0000-0002-7498-7763", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7f8bd0772d1341ddbcd9bba2f66b042d.json"}}, {"family": "Lindahl", "given": "Erik", "initials": "E", "orcid": "0000-0002-2734-2794", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9eb30fa60f9b4b95842ac9d9f3a0eaa9.json"}}], "type": "journal article", "published": "2020-10-07", "journal": {"title": "J Chem Phys", "issn": "1089-7690", "volume": "153", "issue": "13", "pages": "134110", "issn-l": "0021-9606"}, "abstract": "The introduction of accelerator devices such as graphics processing units (GPUs) has had profound impact on molecular dynamics simulations and has enabled order-of-magnitude performance advances using commodity hardware. To fully reap these benefits, it has been necessary to reformulate some of the most fundamental algorithms, including the Verlet list, pair searching, and cutoffs. Here, we present the heterogeneous parallelization and acceleration design of molecular dynamics implemented in the GROMACS codebase over the last decade. The setup involves a general cluster-based approach to pair lists and non-bonded pair interactions that utilizes both GPU and central processing unit (CPU) single instruction, multiple data acceleration efficiently, including the ability to load-balance tasks between CPUs and GPUs. The algorithm work efficiency is tuned for each type of hardware, and to use accelerators more efficiently, we introduce dual pair lists with rolling pruning updates. Combined with new direct GPU-GPU communication and GPU integration, this enables excellent performance from single GPU simulations through strong scaling across multiple GPUs and efficient multi-node parallelization.", "doi": "10.1063/5.0018516", "pmid": "33032406", "labels": [], "xrefs": [], "notes": [], "created": "2026-08-21T11:59:48.994Z", "modified": "2026-08-21T11:59:49.212Z"}]}