The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Constant pH Replica Exchange Molecular Dynamics in Explicit Solvent Using Discrete Protonation States: Implementation, Testing, and Validation
Open accessAbstract
Abstract By utilizing Graphics Processing Units, we show that constant pH molecular dynamics simulations (CpHMD) run in Generalized Born (GB) implicit solvent for long time scales can yield poor pKa predictions as a result of sampling unrealistic conformations. To address this shortcoming, we present a method for performing constant pH molecular dynamics simulations (CpHMD) in explicit solvent using a discrete protonation state model. The method involves standard molecular dynamics (MD) being propagated in explicit solvent followed by protonation state changes being attempted in GB implicit solvent at fixed intervals. Replica exchange along the pH-dimension (pH-REMD) helps to obtain acceptable titration behavior with the proposed method. We analyzed the effects of various parameters and settings on the titration behavior of CpHMD and pH-REMD in explicit solvent, including the size of the simulation unit cell and the length of the relaxation dynamics following protonation state changes. We tested the method with the amino acid model compounds, a small pentapeptide with two titratable sites, and hen egg white lysozyme (HEWL). The proposed method yields superior predicted pKa values for HEWL over hundreds of nanoseconds of simulation relative to corresponding predicted values from simulations run in implicit solvent.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Comparison of simple potential functions for simulating liquid water
Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L. (1983) · The Journal of Chemical Physics · doi:10.1063/1.445869 · Cited by 41,298
Particle mesh Ewald: An N ⋅log( N ) method for Ewald sums in large systems
Darden, T., York, D., & Pedersen, L. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464397 · Cited by 30,546
Self-citationA smooth particle mesh Ewald method
Essmann, U., Perera, L., Berkowitz, M. L., Darden, T., Lee, H., & Pedersen, L. G. (1995) · The Journal of Chemical Physics · doi:10.1063/1.470117 · Cited by 22,514
Ryckaert, J.-P., Ciccotti, G., & Berendsen, H. J. C. (1977) · Journal of Computational Physics · doi:10.1016/0021-9991(77)90098-5 · Cited by 21,239
Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models
Miyamoto, S., & Kollman, P. A. (1992) · Journal of Computational Chemistry · doi:10.1002/jcc.540130805 · Cited by 7,344
Comparison of multiple Amber force fields and development of improved protein backbone parameters
Hornak, V., Abel, R., Okur, A., Strockbine, B., Roitberg, A., & Simmerling, C. (2006) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.21123 · Cited by 6,914
Self-citationOnufriev, A., Bashford, D., & Case, D. A. (2004) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20033 · Cited by 2,507
Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 1. Generalized Born
Götz, A. W., Williamson, M. J., Xu, D., Poole, D., Le Grand, S., & Walker, R. C. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct200909j · Cited by 2,046
Zgarbová, M., Otyepka, M., Šponer, J., Mládek, A., Banáš, P., Cheatham, T. E., & Jurečka, P. (2011) · Journal of Chemical Theory and Computation · doi:10.1021/ct200162x · Cited by 1,165
Hawkins, G. D., Cramer, C. J., & Truhlar, D. G. (1996) · The Journal of Physical Chemistry · doi:10.1021/jp961710n · Cited by 884
Cited by
Scalable molecular dynamics on CPU and GPU architectures with NAMD
Phillips, J. C., Hardy, D. J., Maia, J. D. C., Stone, J. E., Ribeiro, J. V., Bernardi, R. C., Buch, R., Fiorin, G., Hénin, J., Jiang, W., et al. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0014475 · Cited by 3,144
Role of Molecular Dynamics and Related Methods in Drug Discovery
De Vivo, M., Masetti, M., Bottegoni, G., & Cavalli, A. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b01684 · Cited by 1,297
Lee, T.-S., Cerutti, D. S., Mermelstein, D., Lin, C., LeGrand, S., Giese, T. J., Roitberg, A., Case, D. A., Walker, R. C., & York, D. M. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00462 · Cited by 481
Self-citationLarge-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects
Schindler, C. E. M., Baumann, H., Blum, A., Böse, D., Buchstaller, H.-P., Burgdorf, L., Cappel, D., Chekler, E., Czodrowski, P., Dorsch, D., et al. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00900 · Cited by 303
CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed
Hwang, W., Austin, S. L., Blondel, A., Boittier, E. D., Boresch, S., Buck, M., Buckner, J., Caflisch, A., Chang, H.-T., Cheng, X., et al. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c04100 · Cited by 136
Protein p K a Prediction by Tree-Based Machine Learning
Chen, A. Y., Lee, J., Damjanovic, A., & Brooks, B. R. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c01257 · Cited by 37
Coskun, D., Chen, W., Clark, A. J., Lu, C., Harder, E. D., Wang, L., Friesner, R. A., & Miller, E. B. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00954 · Cited by 28
Xu, S., & Onoda, A. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01288 · Cited by 13
Tulika, T., Pedersen, R. W., Rimbault, C., Ahmadi, S., Rivera‐de‐Torre, E., Fernández‐Quintero, M. L., Loeffler, J. R., Bohn, M., Ljungars, A., Ledsgaard, L., et al. (2023) · Protein Science · doi:10.1002/pro.4821 · Cited by 11
Notari, E., Wood, C. W., & Michel, J. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01695 · Cited by 4
The mechanism of mammalian proton-coupled peptide transporters
(2024) · doi:10.1101/2024.02.04.578827 · Cited by 1
Metadata
- Authors
- Swails, J. M., York, D. M., & Roitberg, A. E.
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-02-25
- Volume
- 10
- Issue
- 3
- Pages
- 1341-1352
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- OpenAlex
- W2032427406
- Cited by
- 268
- References
- 40
- FWCI
- 5.35
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.