Comparison of simple potential functions for simulating liquid water

Jorgensen, William L., Chandrasekhar, Jayaraman, Madura, Jeffry D., Impey, Roger W., Klein, Michael L.·1983·The Journal of Chemical Physics

DOI 10.1063/1.445869

Open this paper in CoChat — free account

Abstract

Classical Monte Carlo simulations have been carried out for liquid water in the NPT ensemble at 25 °C and 1 atm using six of the simpler intermolecular potential functions for the water dimer: Bernal–Fowler (BF), SPC, ST2, TIPS2, TIP3P, and TIP4P. Comparisons are made with experimental thermodynamic and structural data including the recent neutron diffraction results of Thiessen and Narten. The computed densities and potential energies are in reasonable accord with experiment except for the original BF model, which yields an 18% overestimate of the density and poor structural results. The TIPS2 and TIP4P potentials yield oxygen–oxygen partial structure functions in good agreement with the neutron diffraction results. The accord with the experimental OH and HH partial structure functions is poorer; however, the computed results for these functions are similar for all the potential functions. Consequently, the discrepancy may be due to the correction terms needed in processing the neutron data or to an effect uniformly neglected in the computations. Comparisons are also made for self-diffusion coefficients obtained from molecular dynamics simulations. Overall, the SPC, ST2, TIPS2, and TIP4P models give reasonable structural and thermodynamic descriptions of liquid water and they should be useful in simulations of aqueous solutions. The simplicity of the SPC, TIPS2, and TIP4P functions is also attractive from a computational standpoint.

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

Crossref · 10.1063/1.445869

Cites

Statistics

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.

Items 201–264 of 264

Structure-based design of CDC42 effector interaction inhibitors for the treatment of cancer
Jahid, S., Ortega, J. A., Vuong, L. M., Acquistapace, I. M., Hachey, S. J., Flesher, J. L., La Serra, M. A., Brindani, N., La Sala, G., Manigrasso, J., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110641 · Cited by 21
Charge Transfer at the Qo-Site of the Cytochrome bc1 Complex Leads to Superoxide Production
Salo, A. B., Husen, P., & Solov’yov, I. A. (2017) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6b10403 · Cited by 21
Predicting improved protein conformations with a temporal deep recurrent neural network
Pfeiffenberger, E., & Bates, P. A. (2018) · PLOS ONE · doi:10.1371/journal.pone.0202652 · Cited by 21
Mutagenesis and structural studies reveal the basis for the specific binding of SARS-CoV-2 SL3 RNA element with human TIA1 protein
Zhang, D., Qiao, L., Lei, X., Dong, X., Tong, Y., Wang, J., Wang, Z., & Zhou, R. (2023) · Nature Communications · doi:10.1038/s41467-023-39410-8 · Cited by 21
Characterization of Protein Flexibility Using Small-Angle X-Ray Scattering and Amplified Collective Motion Simulations
Wen, B., Peng, J., Zuo, X., Gong, Q., & Zhang, Z. (2014) · Biophysical Journal · doi:10.1016/j.bpj.2014.07.005 · Cited by 20
Predicting the conformational flexibility of antibody and T-cell receptor CDRs
Spoendlin, F. C., Fernández-Quintero, M. L., Raghavan, S. S. R., Turner, H. L., Gharpure, A., Loeffler, J. R., Wong, W. K., Bujotzek, A., Georges, G., Ward, A. B., et al. (2025) · doi:10.1101/2025.03.19.644119 · Cited by 20
Predicting the conformational flexibility of antibody and T cell receptor complementarity-determining regions
Spoendlin, F. C., Fernández-Quintero, M. L., Raghavan, S. S. R., Turner, H. L., Gharpure, A., Loeffler, J. R., Wong, W. K., Bujotzek, A., Georges, G., Ward, A. B., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01131-6 · Cited by 20
Markov modeling reveals novel intracellular modulation of the human TREK-2 selectivity filter
Harrigan, M. P., McKiernan, K. A., Shanmugasundaram, V., Denny, R. A., & Pande, V. S. (2017) · Scientific Reports · doi:10.1038/s41598-017-00256-y · Cited by 19
Caffeoyl malic acid is a potential dual inhibitor targeting TNFα/IL-4 evaluated by a combination strategy of network analysis-deep learning-molecular simulation
Wang, Y., Qin, D., Jin, L., & Liang, G. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105410 · Cited by 19
Cyclophilin A Inhibition: Targeting Transition-State-Bound Enzyme Conformations for Structure-Based Drug Design
Nagaraju, M., McGowan, L. C., & Hamelberg, D. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci300432w · Cited by 18
Probing the Role of Acid Site Distribution on the Water Structure in Aluminosilicate Zeolites: Insights from Molecular Dynamics
Zheng, M., & Bukowski, B. C. (2024) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.4c01087 · Cited by 18
Structural framework for the understanding spectroscopic and functional signatures of the cyanobacterial Orange Carotenoid Protein families
Sluchanko, N. N., Maksimov, E. G., Slonimskiy, Y. B., Varfolomeeva, L. A., Bukhanko, A. Y., Egorkin, N. A., Tsoraev, G. V., Khrenova, M. G., Ge, B., Qin, S., et al. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.127874 · Cited by 18
Tyr120Asp mutation alters domain flexibility and dynamics of MeCP2 DNA binding domain leading to impaired DNA interaction: Atomistic characterization of a Rett syndrome causing mutation
D'Annessa, I., Gandaglia, A., Brivio, E., Stefanelli, G., Frasca, A., Landsberger, N., & Di Marino, D. (2018) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2018.02.005 · Cited by 18
Mutations at the Qo Site of the Cytochrome bc1 Complex Strongly Affect Oxygen Binding
Husen, P., & Solov’yov, I. A. (2017) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6b08226 · Cited by 17
Milk Protein Adsorption on Metallic Iron Surfaces
(2023) · doi:10.3390/nano13121857 · Cited by 17
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
Lengthening the Intersubunit Linker of Procaspase 3 Leads to Constitutive Activation
MacKenzie, S. H., Schipper, J. L., England, E. J., Thomas, M. E., Blackburn, K., Swartz, P., & Clark, A. C. (2013) · Biochemistry · doi:10.1021/bi400793s · Cited by 15
FEP Protocol Builder: Optimization of Free Energy Perturbation Protocols Using Active Learning
de Oliveira, C., Leswing, K., Feng, S., Kanters, R., Abel, R., & Bhat, S. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00681 · Cited by 15
Antibiotic hyper-resistance in a class I aminoacyl-tRNA synthetase with altered active site signature motif
Brkic, A., Leibundgut, M., Jablonska, J., Zanki, V., Car, Z., Petrovic Perokovic, V., Marsavelski, A., Ban, N., & Gruic-Sovulj, I. (2023) · Nature Communications · doi:10.1038/s41467-023-41244-3 · Cited by 15
Prediction of Enzyme Catalysis by Computing Reaction Energy Barriers via Steered QM/MM Molecular Dynamics Simulations and Machine Learning
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
Insights into the structure and function of the human organic anion transporter 1 in lipid bilayer membranes
Janaszkiewicz, A., Tóth, Á., Faucher, Q., Martin, M., Chantemargue, B., Barin-Le Guellec, C., Marquet, P., & Di Meo, F. (2022) · Scientific Reports · doi:10.1038/s41598-022-10755-2 · Cited by 15
CRISPR-Cas12a REC2–Nuc interactions drive target-strand cleavage and constrain trans cleavage
Newman, A., Saha, A., Starrs, L., Arantes, P. R., Palermo, G., & Burgio, G. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf988 · Cited by 14
Sustained administration of hormones exploiting nanoconfined diffusion through nanochannel membranes
Geninatti, T., Hood, R., Bruno, G., Jain, P., Nicolov, E., Ziemys, A., & Grattoni, A. (2015) · Materials · doi:10.3390/ma8085241 · Cited by 13
Photocobilins integrate B12 and bilin photochemistry for enzyme control
Zhang, S., Jeffreys, L. N., Poddar, H., Yu, Y., Liu, C., Patel, K., Johannissen, L. O., Zhu, L., Cliff, M. J., Yan, C., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46995-1 · Cited by 13
A Putative Binding Model of Nitazene Derivatives at the μ-Opioid Receptor
Clayton, J., Shi, L., Robertson, M. J., Skiniotis, G., Michaelides, M., Stavitskaya, L., & Shen, J. (2025) · Neuropharmacology · doi:10.1016/j.neuropharm.2025.110437 · Cited by 12
Amino acid sequence encodes protein abundance shaped by protein stability at reduced synthesis cost
(2024) · doi:10.1002/pro.5239 · Cited by 12
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
Association of Both Inhibitory and Stimulatory Gα Subunits Implies Adenylyl Cyclase 5 Deactivation
van Keulen, S. C., Narzi, D., & Rothlisberger, U. (2019) · Biochemistry · doi:10.1021/acs.biochem.9b00662 · Cited by 11
Unraveling motion in proteins by combining NMR relaxometry and molecular dynamics simulations: A case study on ubiquitin
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
Probing the Microscopic Behavior of PFOS Clustering and Adsorption at LNAPL Interfaces; a PFOS–Water–Cyclohexane System
Sookhak Lari, K., Reinhardt, A., Warden, A. C., Rayner, J. L., & Davis, G. B. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c01748 · Cited by 11
Targeting Tuberculosis: Novel Scaffolds for Inhibiting Cytochrome bd Oxidase
Seitz, C., Ahn, S.-H., Wei, H., Kyte, M., Cook, G. M., Krause, K. L., & McCammon, J. A. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00344 · Cited by 11
Distinct pathways for evolution of enhanced receptor binding and cell entry in SARS-like bat coronaviruses
(2024) · doi:10.1371/journal.ppat.1012704 · Cited by 10
Cosolvent and Dynamic Effects in Binding Pocket Search by Docking Simulations
(2021) · doi:10.1021/acs.jcim.1c00924 · 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
Multiscale Computational Study of the Conformation of the Full-Length Intrinsically Disordered Protein MeCP2
Chávez-García, C., Hénin, J., & Karttunen, M. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01354 · Cited by 8
Multiscale Approach for Computing Gated Ligand Binding from Molecular Dynamics and Brownian Dynamics Simulations
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
Unveiling the Catalytic Mechanism and Conformational Dynamics of Guinea Pig l -Asparaginase Type 1 for Leukemia Drug Design
Andjelkovic, M., Zinovjev, K., Ruiz-Pernía, J. J., & Tuñón, I. (2025) · ACS Catalysis · doi:10.1021/acscatal.4c07791 · Cited by 7
Identification and characterization of substrate- and product-selective nylon hydrolases
Drufva, E. E., Cahill, J. F., Saint-Vincent, P. M. B., Williams, A. N., Bocharova, V., Capra, N., Meilleur, F., Carper, D. L., Bourgery, C., Miyazaki, K., et al. (2025) · Chem Catalysis · doi:10.1016/j.checat.2025.101418 · Cited by 7
Continuous millisecond conformational cycle of a DEAH box helicase reveals control of domain motions by atomic-scale transitions
Becker, R. A., & Hub, J. S. (2023) · Communications Biology · doi:10.1038/s42003-023-04751-z · Cited by 7
Hidden GPCR structural transitions addressed by multiple walker supervised molecular dynamics (mwSuMD)
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
Conformations of a highly expressed Z19 α-zein studied with AlphaFold2 and MD simulations
(2024) · doi:10.1371/journal.pone.0293786 · Cited by 7
Atomic-Resolution Prediction of Degrader-mediated Ternary Complex Structures by Combining Molecular Simulations with Hydrogen Deuterium Exchange
Dixon, T., MacPherson, D., Mostofian, B., Dauzhenka, T., Lotz, S., McGee, D., Shechter, S., Shrestha, U. R., Wiewiora, R., McDargh, Z. A., et al. (2021) · doi:10.1101/2021.09.26.461830 · 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
A Coarse-Grained MARTINI Model for Mucins
Kanesalingam, T., Weiand, E., Cann, P. M., Masen, M., & Ewen, J. P. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c01655 · Cited by 6
Applying Computational Protein Design to Engineer Affibodies for Affinity-controlled Delivery of Vascular Endothelial Growth Factor and Platelet-Derived Growth Factor
(2025) · doi:10.1021/acs.biomac.5c00097 · Cited by 5
Conformations and sequence determinants in the lipid binding of an adhesive peptide derived from Vibrio cholerae biofilms
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
Computational Procedure for Predicting Excipient Effects on Protein–Protein Affinities
Dignon, G. L., & Dill, K. A. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c01197 · Cited by 5
Prediction of Kv11.1 potassium channel PAS-domain variants trafficking via machine learning
Immadisetty, K., Fang, X., Ramon, G. S., Hartle, C. M., McCoy, T. P., Center, R. G., Mirshahi, T., Delisle, B. P., & Kekenes-Huskey, P. M. (2023) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2023.05.002 · Cited by 5
Mapping structural and dynamic divergence across the MBOAT family
Ansell, T. B., Healy, M., Coupland, C. E., Sansom, M. S. P., & Siebold, C. (2024) · Structure · doi:10.1016/j.str.2024.03.014 · Cited by 4
Insights into the Catalytic Activity of a Metagenome-Derived Urethanase
Świderek, K., Arafet, K., de Sousa Batista, V., Grajales-Hernández, D., López-Gallego, F., & Moliner, V. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.5c13147 · Cited by 4
Anillin-related Mid1 as an adaptive and multimodal contractile ring anchoring protein: A simulation study
Hall, A. R., Choi, Y. K., Im, W., & Vavylonis, D. (2024) · Structure · doi:10.1016/j.str.2023.11.010 · Cited by 3
The Oncogenic EGFR–SHC1 Fusion Confers Insensitivity to EGFR-TKIs via Dual Activation of N-EGFR Kinase Domain and C-SHC1 Phosphorylation Sites in Lung Cancer
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
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 systems, prior to minimization or MD simulations, were solvated in a 0.1 M NaCl solution with the TIP3P water model 132 using CHARMM-GUI with a solvent buffer of 20 Å for the unfolded EXOSC3 (pentapeptide) model or 10 Å for all other models.”
Influential
In silico design and validation of high-affinity RNA aptamers for SARS-CoV-2 comparable to neutralizing antibodies
Yang, Y., Qiao, L., Jiang, Y., Wang, Z., Zhang, D., Buratto, D., Huang, L., & Zhou, R. (2026) · eLife · doi:10.7554/elife.107785.4 · Cited by 1
Dimerization of MilM Is Essential for Catalyzing the Pyridoxal-5′-phosphate (PLP)-Dependent Cγ-Hydroxylation of l -Arginine during Mildiomycin Biosynthesis
(2026) · doi:10.1021/acs.biochem.6c00271 · Cited by 1
Mobile barrier mechanisms for Na+-coupled symport in an MFS sugar transporter
Hariharan, P., Shi, Y., Katsube, S., Willibal, K., Burrows, N. D., Mitchell, P., Bakhtiiari, A., Stanfield, S., Pardon, E., Kaback, H. R., et al. (2024) · doi:10.7554/elife.92462.2
Conformational distributions of isolated myosin motor domains encode their mechanochemical properties
Porter, J. R., Meller, A., Zimmerman, M. I., Greenberg, M. J., & Bowman, G. R. (2019) · doi:10.1101/2019.12.16.878264
Structural dynamics of IRE1 and its interaction with unfolded peptides
Spinetti, E., Ścibisz, G., Karagöz, G. E., & Covino, R. (2026) · eLife · doi:10.7554/elife.106716
SMC complex unidirectionally translocates DNA by coupling segment capture with an asymmetric kleisin path
Yamauchi, M., Brandani, G. B., Terakawa, T., & Takada, S. (2026) · eLife · doi:10.7554/elife.106752
Mechanistic origin of C - over O -glycosylation in glycosyltransferase GgCGT: a QM/MM study
Terada, D., Inagaki, T., & Hatanaka, M. (2026) · Bulletin of the Chemical Society of Japan · doi:10.1093/bulcsj/uoag026
Scalable emulation of protein equilibrium ensembles with generative deep learning
Lewis, S., Hempel, T., Jiménez Luna, J., Gastegger, M., Xie, Y., Foong, A. Y. K., García Satorras, V., Abdin, O., Veeling, B. S., Zaporozhets, I., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.05.626885 · Cited by 349

Authors: Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L.
Venue: The Journal of Chemical Physics
Publisher: AIP Publishing
Published: 1983-07-15
Volume: 79
Issue: 2
Pages: 926-935
ISSN: 0021-9606 (ISSN-L)
1089-7690
DOI: 10.1063/1.445869
OpenAlex: W1976499671
Cited by: 41,298
References: 0
FWCI: 24.17
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.

Comparison of simple potential functions for simulating liquid water

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cited by

Metadata

Record

About these checks

Data sources