The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A smooth particle mesh Ewald method
Abstract
The previously developed particle mesh Ewald method is reformulated in terms of efficient B-spline interpolation of the structure factors. This reformulation allows a natural extension of the method to potentials of the form 1/rp with p≥1. Furthermore, efficient calculation of the virial tensor follows. Use of B-splines in place of Lagrange interpolation leads to analytic gradients as well as a significant improvement in the accuracy. We demonstrate that arbitrary accuracy can be achieved, independent of system size N, at a cost that scales as N log(N). For biomolecular systems with many thousands of atoms this method permits the use of Ewald summation at a computational cost comparable to that of a simple truncation method of 10 Å or less.
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
Computer Simulation of Liquids
Allen, M. P., & Tildesley, D. J. (2017) · doi:10.1093/oso/9780198803195.001.0001 · Cited by 20,945
Cited by
Items 101–139 of 139
Mazzuca, C., Di Napoli, B., Biscaglia, F., Ripani, G., Rajendran, S., Braga, A., Benna, C., Mocellin, S., Gobbo, M., Meneghetti, M., et al. (2019) · Nanoscale Advances · doi:10.1039/c9na00096h · Cited by 16
Dual function of OmpM as outer membrane tether and nutrient uptake channel in diderm Firmicutes
Silale, A., Zhu, Y., Witwinowski, J., Smith, R. E., Newman, K. E., Bhamidimarri, S. P., Baslé, A., Khalid, S., Beloin, C., Gribaldo, S., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42601-y · Cited by 16
Visan, R. M., & Angelescu, D. G. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c02115 · Cited by 15
Platero-Rochart, D., Krivobokova, T., Gastegger, M., Reibnegger, G., & Sánchez-Murcia, P. A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00772 · Cited by 15
Bung, N., Pradhan, M., Srinivasan, H., & Bulusu, G. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003484 · Cited by 14
Sun, L., Zheng, Z.-M., Shao, C.-S., Zhang, Z.-Y., Li, M.-W., Wang, L., Wang, H., Zhao, G.-H., & Wang, P. (2022) · Pharmaceuticals · doi:10.3390/ph15060740 · Cited by 17
Structural mechanisms for VMAT2 inhibition by tetrabenazine
Dalton, M. P., Cheng, M. H., Bahar, I., & Coleman, J. A. (2024) · eLife · doi:10.7554/elife.91973.4 · Cited by 14
Samsudin, F., Gan, S. K.-E., & Bond, P. J. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.12.022 · Cited by 16
AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations
Capponi, S., Wang, S., Navarro, E. J., & Bianco, S. (2021) · The European Physical Journal E · doi:10.1140/epje/s10189-021-00119-5 · Cited by 12
Nguyen, H. L., Pham, D. L., O’Brien, E. P., & Li, M. S. (2018) · Scientific Reports · doi:10.1038/s41598-018-24344-9 · Cited by 12
Champion, C., Lehner, M., Smith, A. A., Ferrage, F., Bolik-Coulon, N., & Riniker, S. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0188416 · Cited by 11
Towards extreme scale dissipative particle dynamics simulations using multiple GPGPUs
Castagna, J., Guo, X., Seaton, M., & O’Cais, A. (2020) · Computer Physics Communications · doi:10.1016/j.cpc.2020.107159 · Cited by 10
Structure of the Human Signal Peptidase Complex Reveals the Determinants for Signal Peptide Cleavage
Liaci, A. M., Steigenberger, B., Tamara, S., de Souza, P. C. T., Gröllers-Mulderij, M., Ogrissek, P., Marrink, S. J., Scheltema, R. A., & Förster, F. (2020) · doi:10.1101/2020.11.11.378711 · Cited by 10
Computational insights into the active structure of SGK1 and its implication for ligand design
Akhoon, B. A., Gandhi, N. S., & Pandey, R. (2019) · Biochimie · doi:10.1016/j.biochi.2019.07.007 · Cited by 9
Free Energy of Membrane Pore Formation and Stability from Molecular Dynamics Simulations
Rivel, T., Biriukov, D., Kabelka, I., & Vácha, R. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01960 · Cited by 9
AlphaFold2 SLiM screen for LC3-LIR interactions in autophagy
Stuke, J. F. M., & Hummer, G. (2025) · Autophagy · doi:10.1080/15548627.2025.2493999 · Cited by 8
Chávez-García, C., Hénin, J., & Karttunen, M. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01354 · Cited by 8
Sadiq, S. K., Muñiz Chicharro, A., Friedrich, P., & Wade, R. C. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00673 · Cited by 8
Molecular insights into RNA recognition and gene regulation by the TRIM-NHL protein Mei-P26
Salerno-Kochan, A., Horn, A., Ghosh, P., Nithin, C., Kościelniak, A., Meindl, A., Strauss, D., Krutyhołowa, R., Rossbach, O., Bujnicki, J. M., et al. (2022) · Life Science Alliance · doi:10.26508/lsa.202201418 · Cited by 8
Deganutti, G., Pipito, L., Rujan, R. M., Weizmann, T., Griffin, P., Ciancetta, A., Moro, S., & Reynolds, C. A. (2025) · eLife · doi:10.7554/elife.96513 · Cited by 7
Yan, Y., Shetty, M., Harding, H. P., George, G., Zyryanova, A., Labbé, K., Mafi, A., Hao, Q., Sidrauski, C., & Ron, D. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2320013121 · Cited by 7
Control of Golgi- V-ATPase through Sac1-dependent co-regulation of PI(4)P and cholesterol
Zhou, X., van der Stoel, M. M., Kaptan, S., Li, H., Li, S., Hölttä, M., Vihinen, H., Jokitalo, E., Thiele, C., Pietiläinen, O., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-63125-7 · Cited by 6
Molecular insights into human phosphatidylserine synthase 2 and its regulation of SREBP pathways
Li, D., Chen, H., Vale, G., Elghobashi-Meinhardt, N., Hatton, A., Rong, S., McDonald, J. G., & Li, X. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2501177122 · Cited by 6
Engineering of soluble bacteriorhodopsin
Nikolaev, A., Orlov, Y., Tsybrov, F., Kuznetsova, E., Shishkin, P., Kuzmin, A., Mikhailov, A., Nikolaeva, Y. S., Anuchina, A., Chizhov, I., et al. (2025) · Chemical Science · doi:10.1039/d5sc02453f · Cited by 6
Scharf, M. M., Kinsolving, J., Grätz, L., Voss, J. H., Carrasco-Busturia, D., Forsberg, B., Kolb, P., & Schulte, G. (2025) · Nature Communications · doi:10.1038/s41467-025-67147-z · Cited by 6
PURA and GLUT1: Sweet partners for brain health
Colombo, R. B., Maxit, C., Martinelli, D., Anderson, M., Masone, D., & Mayorga, L. (2024) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2024.167261 · Cited by 5
Huang, X., Prasad, R., Saluja, S., Yang, Y., Yan, Q., Shuster, S. O., Karatekin, E., Olson, R., Lin, C., Davis, C. M., et al. (2026) · PLOS Pathogens · doi:10.1371/journal.ppat.1013990 · Cited by 5
Architecture of the ATP-driven motor for protein import into chloroplasts
Wang, N., Xing, J., Su, X., Pan, J., Chen, H., Shi, L., Si, L., Yang, W., & Li, M. (2024) · Molecular Plant · doi:10.1016/j.molp.2024.09.010 · Cited by 5
SWEET Family Transporters Act as Water-Conducting Carrier Proteins in Plants
Selvam, B., Paul, A., Yu, Y.-C., Chen, L.-Q., & Shukla, D. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00110 · Cited by 3
A novel model for proton transport mediated by uncoupling protein 1
Jacobsen, L., Menon, S., Gaudry, M. J., Hakami Zanjani, A. A., Reinholdt, P., Jastroch, M., & Khandelia, H. (2025) · Protein Science · doi:10.1002/pro.70357 · Cited by 3
Ulug, M. E., Ikram, S., Sayyah, E., & Durdağı, S. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.142602 · Cited by 3
Zheng, J., Zhao, S., Zhan, J., Zhuang, W., Chen, M., Jiang, W., Huang, Y., He, J., Hu, L., Pang, L., et al. (2026) · Cancer Discovery · doi:10.1158/2159-8290.cd-25-1936 · Cited by 2
Protein pKa prediction with machine learning
Cai, Z., Luo, F., Wang, Y., Li, E., & Huang, Y. (2021) · doi:10.33774/chemrxiv-2021-7gk5l-v2 · Cited by 2
Jackson, S. N., Lee, D. E., Blount, J. M., Croney, K. A., Ibershof, J. W., Ceravolo, C. M., Brown, K. M., Goodwin-Rice, N. J., Whitham, K. M., McCarty, J., et al. (2025) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2025.108382 · Cited by 2
EXOSC3 G191 Variants Trigger System-Wide Recalibration of RNA Processing Machinery
Wijeratne, H. R. S., Runnebohm, A. M., Barron, M. P., Hampton, A. J., Li, R., Peck Justice, S. A., Lenceski, S. H., Rooney, J. D., Smith-Kinnaman, W. R., Chang, G. S., et al. (2026) · doi:10.64898/2026.01.26.701775 · Cited by 1
“All simulations were performed in an isothermal-isobaric ensemble with a pressure of 1.0 atm and a temperature of 30 °C. Long-range electrostatic interactions were modelled with particle mesh Ewald (PME) with force switching and nonbonded cutoffs set to 9 and 10 Å, respectively 76,77,79,139 .”
InfluentialStructural characterization of DNA-binding domain of essential mammalian protein TTF 1
Singh, G., Bhopale, A. J., Khatri, S., Prakash, P., Kumar, R., Singh, S. M., & Singh, S. K. (2024) · Bioscience Reports · doi:10.1042/bsr20240800 · Cited by 1
Yamauchi, M., Brandani, G. B., Terakawa, T., & Takada, S. (2026) · eLife · doi:10.7554/elife.106752
Metadata
- Authors
- Essmann, U., Perera, L., Berkowitz, M. L., Darden, T., Lee, H., & Pedersen, L. G.
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 1995-11-15
- Volume
- 103
- Issue
- 19
- Pages
- 8577-8593
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W2035687084
- Cited by
- 22,514
- References
- 1
- FWCI
- 17.51
- Type
- journal article
- Language
- en
- Open access
- closed
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.
