The DOI matches its Crossref record.
Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on Peptides
Abstract
We present results of improving the OPLS-AA force field for peptides by means of refitting the key Fourier torsional coefficients. The fitting technique combines using accurate ab initio data as the target, choosing an efficient fitting subspace of the whole potential-energy surface, and determining weights for each of the fitting points based on magnitudes of the potential-energy gradient. The average energy RMS deviation from the LMP2/cc-pVTZ(-f)//HF/6-31G** data is reduced by ca. 40% from 0.81 to 0.47 kcal/mol as a result of the fitting for the electrostatically uncharged dipeptides. Transferability of the parameters is demonstrated by using the same alanine dipeptide-fitted backbone torsional parameters for all of the other dipeptides (with the appropriate side-chain refitting) and the alanine tetrapeptide. Parameters of nonbonded interactions have also been refitted for the sulfur-containing dipeptides (cysteine and methionine), and the validity of the new Coulombic charges and the van der Waals σ 's and ε 's is proved through reproducing gas-phase energies of complex formation heats of vaporization and densities of pure model liquids. Moreover, a novel approach to fitting torsional parameters for electrostatically charged molecular systems has been presented and successfully tested on five dipeptides with charged side chains.
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
Cited by
GROMACS: Fast, flexible, and free
Van Der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A. E., & Berendsen, H. J. C. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20291 · Cited by 19,182
Current Status of the AMOEBA Polarizable Force Field
Ponder, J. W., Wu, C., Ren, P., Pande, V. S., Chodera, J. D., Schnieders, M. J., Haque, I., Mobley, D. L., Lambrecht, D. S., DiStasio, R. A., et al. (2010) · The Journal of Physical Chemistry B · doi:10.1021/jp910674d · Cited by 1,440
Wang, L., Wu, Y., Deng, Y., Kim, B., Pierce, L., Krilov, G., Lupyan, D., Robinson, S., Dahlgren, M. K., Greenwood, J., et al. (2015) · Journal of the American Chemical Society · doi:10.1021/ja512751q · Cited by 1,313
Self-citationImproved Peptide and Protein Torsional Energetics with the OPLS-AA Force Field
Robertson, M. J., Tirado-Rives, J., & Jorgensen, W. L. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00356 · Cited by 854
Self-citationPiana, S., Donchev, A. G., Robustelli, P., & Shaw, D. E. (2015) · The Journal of Physical Chemistry B · doi:10.1021/jp508971m · Cited by 852
Tailoring electrolyte solvation for Li metal batteries cycled at ultra-low temperature
Holoubek, J., Liu, H., Wu, Z., Yin, Y., Xing, X., Cai, G., Yu, S., Zhou, H., Pascal, T. A., Chen, Z., et al. (2021) · Nature Energy · doi:10.1038/s41560-021-00783-z · Cited by 851
Lyne, P. D., Lamb, M. L., & Saeh, J. C. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm060522a · Cited by 699
Role of the Active-Site Solvent in the Thermodynamics of Factor Xa Ligand Binding
Abel, R., Young, T., Farid, R., Berne, B. J., & Friesner, R. A. (2008) · Journal of the American Chemical Society · doi:10.1021/ja0771033 · Cited by 668
Self-citationMolecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development
Salo-Ahen, O. M. H., Alanko, I., Bhadane, R., Bonvin, A. M. J. J., Honorato, R. V., Hossain, S., Juffer, A. H., Kabedev, A., Lahtela-Kakkonen, M., Larsen, A. S., et al. (2020) · Processes · doi:10.3390/pr9010071 · Cited by 659
Ramírez, D., & Caballero, J. (2018) · Molecules · doi:10.3390/molecules23051038 · Cited by 624
Johnson, T. W., Richardson, P. F., Bailey, S., Brooun, A., Burke, B. J., Collins, M. R., Cui, J. J., Deal, J. G., Deng, Y.-L., Dinh, D., et al. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm500261q · Cited by 594
Friesner, R. A., & Guallar, V. (2005) · Annual Review of Physical Chemistry · doi:10.1146/annurev.physchem.55.091602.094410 · Cited by 522
Self-citationAssessing the Stability of Alzheimer’s Amyloid Protofibrils Using Molecular Dynamics
Lemkul, J. A., & Bevan, D. R. (2010) · The Journal of Physical Chemistry B · doi:10.1021/jp9110794 · Cited by 508
Hou, T., Yang, G., Rajput, N. N., Self, J., Park, S.-W., Nanda, J., & Persson, K. A. (2019) · Nano Energy · doi:10.1016/j.nanoen.2019.103881 · Cited by 470
QM/MM Methods for Biological Systems
Senn, H. M., & Thiel, W. (2006) · Topics in Current Chemistry · doi:10.1007/128_2006_084 · Cited by 428
Li, Y., Yang, Z., Wang, Y., Bai, Z., Zheng, T., Dai, X., Liu, S., Gui, D., Liu, W., Chen, M., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01208-w · Cited by 409
Polarizable Force Field for Peptides and Proteins Based on the Classical Drude Oscillator
Lopes, P. E. M., Huang, J., Shim, J., Luo, Y., Li, H., Roux, B., & MacKerell, A. D. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400781b · Cited by 386
Understanding folding and design: Replica-exchange simulations of ``Trp-cage'' miniproteins
Pitera, J. W., & Swope, W. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1330954100 · Cited by 344
Biological and synthetic membranes: What can be learned from a coarse-grained description?
MULLER, M., KATSOV, K., & SCHICK, M. (2006) · Physics Reports · doi:10.1016/j.physrep.2006.08.003 · Cited by 320
Huang, Y., Li, Z., Pei, Z., Liu, Z., Li, H., Zhu, M., Fan, J., Dai, Q., Zhang, M., Dai, L., et al. (2018) · Advanced Energy Materials · doi:10.1002/aenm.201802288 · Cited by 319
Zheng, J., Tan, G., Shan, P., Liu, T., Hu, J., Feng, Y., Yang, L., Zhang, M., Chen, Z., Lin, Y., et al. (2018) · Chem · doi:10.1016/j.chempr.2018.09.004 · Cited by 308
Muller, M. P., Jiang, T., Sun, C., Lihan, M., Pant, S., Mahinthichaichan, P., Trifan, A., & Tajkhorshid, E. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00608 · Cited by 282
Hall, Z., Politis, A., Bush, M. F., Smith, L. J., & Robinson, C. V. (2012) · Journal of the American Chemical Society · doi:10.1021/ja2096859 · Cited by 267
Predicting free energy changes using structural ensembles
Benedix, A., Becker, C. M., de Groot, B. L., Caflisch, A., & Böckmann, R. A. (2009) · Nature Methods · doi:10.1038/nmeth0109-3 · Cited by 234
Accelerated molecular dynamics simulations of protein folding
Miao, Y., Feixas, F., Eun, C., & McCammon, J. A. (2015) · Journal of Computational Chemistry · doi:10.1002/jcc.23964 · Cited by 202
Direct generation of protein conformational ensembles via machine learning
Janson, G., Valdes-Garcia, G., Heo, L., & Feig, M. (2023) · Nature Communications · doi:10.1038/s41467-023-36443-x · Cited by 192
CG2AT2: An enhanced fragment-based approach for serial multi-scale molecular dynamics simulations
Vickery, O. N., & Stansfeld, P. J. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00295 · Cited by 150
The Unfolded Ensemble and Folding Mechanism of the C-Terminal GB1 β-Hairpin
Bonomi, M., Branduardi, D., Gervasio, F. L., & Parrinello, M. (2008) · Journal of the American Chemical Society · doi:10.1021/ja803652f · Cited by 97
Biomolecular simulation and modelling: status, progress and prospects
van der Kamp, M. W., Shaw, K. E., Woods, C. J., & Mulholland, A. J. (2008) · Journal of The Royal Society Interface · doi:10.1098/rsif.2008.0105.focus · Cited by 90
A quantitative measure for protein conformational heterogeneity
Lyle, N., Das, R. K., & Pappu, R. V. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4812791 · Cited by 85
Yang, Y., Lightstone, F. C., & Wong, S. E. (2013) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2013.749853 · Cited by 65
Transferable deep generative modeling of intrinsically disordered protein conformations
Janson, G., & Feig, M. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1012144 · Cited by 62
Grover, A., Katiyar, S. P., Jeyakanthan, J., Dubey, V. K., & Sundar, D. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-s7-s20 · Cited by 52
Structural insights into the allosteric inhibition of P2X4 receptors
Shen, C., Zhang, Y., Cui, W., Zhao, Y., Sheng, D., Teng, X., Shao, M., Ichikawa, M., Wang, J., & Hattori, M. (2023) · Nature Communications · doi:10.1038/s41467-023-42164-y · Cited by 49
Piana, S., Laio, A., Marinelli, F., Van Troys, M., Bourry, D., Ampe, C., & Martins, J. C. (2008) · Journal of Molecular Biology · doi:10.1016/j.jmb.2007.10.020 · Cited by 48
Ding, B., Wang, J., Li, N., & Wang, W. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci300508m · Cited by 45
Folding kinetics and unfolded state dynamics of the GB1 hairpin from molecular simulation
De Sancho, D., Mittal, J., & Best, R. B. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct301033r · Cited by 35
Bostick, D. L., & Brooks, C. L. (2009) · Biophysical Journal · doi:10.1016/j.bpj.2009.03.001 · Cited by 26
Challenges of docking in large, flexible and promiscuous binding sites
Kotev, M., Soliva, R., & Orozco, M. (2016) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2016.08.010 · Cited by 25
Jamal, S., Goyal, S., Shanker, A., & Grover, A. (2015) · PLOS ONE · doi:10.1371/journal.pone.0129370 · Cited by 19
Scoring Functions for Protein–Ligand Interactions
Sotriffer, C. (2012) · Protein‐Ligand Interactions · doi:10.1002/9783527645947.ch12 · Cited by 16
Structural insights into the orthosteric inhibition of P2X receptors by non-ATP analog antagonists
(2023) · doi:10.7554/elife.92829 · Cited by 14
Actomyosin forces trigger a conformational change in desmoplakin within desmosomes
Dong, Y., Elgerbi, A., Xie, B., Han, Y., Kwiatkowski, A. V., Choy, J. S., & Sivasankar, S. (2025) · Nature Communications · doi:10.1038/s41467-025-64124-4 · Cited by 10
Conformations of a highly expressed Z19 α-zein studied with AlphaFold2 and MD simulations
(2024) · doi:10.1371/journal.pone.0293786 · Cited by 7
(2024) · doi:10.1093/chemse/bjae018 · Cited by 4
Structural insights into the orthosteric inhibition of P2X receptors by non-ATP-analog antagonists
Sheng, D., Yue, C., Jin, F., Wang, Y., Ichikawa, M., Yu, Y., Guo, C.-R., & Hattori, M. (2023) · doi:10.1101/2023.09.15.558037 · Cited by 3
Structural insights into the orthosteric inhibition of P2X receptors by non-ATP-analog antagonists
Sheng, D., Yue, C., Jin, F., Wang, Y., Ichikawa, M., Yu, Y., Guo, C.-R., & Hattori, M. (2024) · doi:10.7554/elife.92829.2
Structural insights into the orthosteric inhibition of P2X receptors by non-ATP analog antagonists
Sheng, D., Yue, C.-X., Jin, F., Wang, Y., Ichikawa, M., Yu, Y., Guo, C.-R., & Hattori, M. (2024) · eLife · doi:10.7554/elife.92829.3
Metadata
- Authors
- Kaminski, G. A., Friesner, R. A., Tirado-Rives, J., & Jorgensen, W. L.
- Venue
- The Journal of Physical Chemistry B
- Publisher
- American Chemical Society (ACS)
- Published
- 2001-04-19
- Volume
- 105
- Issue
- 28
- Pages
- 6474-6487
- ISSN
- 1520-5207 (ISSN-L)
- 1520-6106
- OpenAlex
- W2054881399
- Cited by
- 4,060
- References
- 0
- FWCI
- 12.73
- 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.