The DOI matches its Crossref record; 226 of the 226 references it cites were found in a registry or an index.
Recent Developments and Applications of the MMPBSA Method
Open accessAbstract
The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach has been widely applied as an efficient and reliable free energy simulation method to model molecular recognition, such as for protein-ligand binding interactions. In this review, we focus on recent developments and applications of the MMPBSA method. The methodology review covers solvation terms, the entropy term, extensions to membrane proteins and high-speed screening, and new automation toolkits. Recent applications in various important biomedical and chemical fields are also reviewed. We conclude with a few future directions aimed at making MMPBSA a more robust and efficient method.
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Cites
The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities
Genheden, S., & Ryde, U. (2015) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2015.1032936 · Cited by 5,534
Role of repulsive forces in determining the equilibrium structure of simple liquids
Weeks, J. D., Chandler, D., & Andersen, H. C. (1971) · The Journal of Chemical Physics · doi:10.1063/1.1674820 · Cited by 5,207
Kollman, P. A., Massova, I., Reyes, C., Kuhn, B., Huo, S., Chong, L., Lee, M., Lee, T., Duan, Y., Wang, W., et al. (2000) · Accounts of Chemical Research · doi:10.1021/ar000033j · Cited by 4,978
g_mmpbsa—A GROMACS Tool for High-Throughput MM-PBSA Calculations
Kumari, R., Kumar, R., Open Source Drug Discovery Consortium, & Lynn, A. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500020m · Cited by 4,882
MMPBSA.py: An Efficient Program for End-State Free Energy Calculations
Miller, B. R., McGee, T. D., Swails, J. M., Homeyer, N., Gohlke, H., & Roitberg, A. E. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct300418h · Cited by 4,609
High-Temperature Equation of State by a Perturbation Method. I. Nonpolar Gases
Zwanzig, R. W. (1954) · The Journal of Chemical Physics · doi:10.1063/1.1740409 · Cited by 3,066
Classical Electrostatics in Biology and Chemistry
Honig, B., & Nicholls, A. (1995) · Science · doi:10.1126/science.7761829 · Cited by 2,879
Efficient estimation of free energy differences from Monte Carlo data
Bennett, C. H. (1976) · Journal of Computational Physics · doi:10.1016/0021-9991(76)90078-4 · Cited by 2,755
Hydrophobicity at Small and Large Length Scales
Lum, K., Chandler, D., & Weeks, J. D. (1999) · The Journal of Physical Chemistry B · doi:10.1021/jp984327m · Cited by 1,670
Continuum Solvent Studies of the Stability of DNA, RNA, and Phosphoramidate−DNA Helices
Srinivasan, J., Cheatham, T. E., Cieplak, P., Kollman, P. A., & Case, D. A. (1998) · Journal of the American Chemical Society · doi:10.1021/ja981844+ · Cited by 1,628
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gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS
Valdés-Tresanco, M. S., Valdés-Tresanco, M. E., Valiente, P. A., & Moreno, E. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00645 · Cited by 3,393
Molecular Docking: Shifting Paradigms in Drug Discovery
Pinzi, L., & Rastelli, G. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20184331 · Cited by 2,344
A fast and high-quality charge model for the next generation general AMBER force field
He, X., Man, V. H., Yang, W., Lee, T.-S., & Wang, J. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0019056 · Cited by 783
Computational prediction of protein–protein binding affinities
Siebenmorgen, T., & Zacharias, M. (2020) · WIREs Computational Molecular Science · doi:10.1002/wcms.1448 · Cited by 184
“79 For rapid evaluation of single complex structures, usually explicit solvent molecules are not included.”
BackgroundMethodInfluentialIssahaku, A. R., Mukelabai, N., Agoni, C., Rudrapal, M., Aldosari, S. M., Almalki, S. G., & Khan, J. (2022) · Scientific Reports · doi:10.1038/s41598-022-22668-1 · Cited by 93
Metadata
- Authors
- Wang, C., Greene, D., Xiao, L., Qi, R., & Luo, R.
- Venue
- Frontiers in Molecular Biosciences
- Publisher
- Frontiers Media SA
- Published
- 2018-01-10
- Volume
- 4
- Pages
- 87-87
- ISSN
- 2296-889X (ISSN-L)
- OpenAlex
- W2783351753
- Cited by
- 655
- References
- 226
- FWCI
- 40.48
- Type
- journal article
- Language
- en
- Open access
- gold
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