The DOI matches its Crossref record; 90 of the 90 references it cites were found in a registry or an index.
g_mmpbsa—A GROMACS Tool for High-Throughput MM-PBSA Calculations
Abstract
Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA), a method to estimate interaction free energies, has been increasingly used in the study of biomolecular interactions. Recently, this method has also been applied as a scoring function in computational drug design. Here a new tool g_mmpbsa, which implements the MM-PBSA approach using subroutines written in-house or sourced from the GROMACS and APBS packages is described. g_mmpbsa was developed as part of the Open Source Drug Discovery (OSDD) consortium. Its aim is to integrate high-throughput molecular dynamics (MD) simulations with binding energy calculations. The tool provides options to select alternative atomic radii and different nonpolar solvation models including models based on the solvent accessible surface area (SASA), solvent accessible volume (SAV), and a model which contains both repulsive (SASA-SAV) and attractive components (described using a Weeks-Chandler-Andersen like integral method). We showcase the effectiveness of the tool by comparing the calculated interaction energy of 37 structurally diverse HIV-1 protease inhibitor complexes with their experimental binding free energies. The effect of varying several combinations of input parameters such as atomic radii, dielectric constant, grid resolution, solute-solvent dielectric boundary definition, and nonpolar models was investigated. g_mmpbsa can also be used to estimate the energy contribution per residue to the binding energy. It has been used to identify those residues in HIV-1 protease that are most critical for binding a range of inhibitors.
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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
Molecular dynamics with coupling to an external bath
Berendsen, H. J. C., Postma, J. P. M., van Gunsteren, W. F., DiNola, A., & Haak, J. R. (1984) · The Journal of Chemical Physics · doi:10.1063/1.448118 · Cited by 30,700
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
Development and testing of a general amber force field
Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A., & Case, D. A. (2004) · Journal of Computational Chemistry · doi:10.1002/jcc.20035 · Cited by 19,506
van der Waals Volumes and Radii
Bondi, A. (1964) · The Journal of Physical Chemistry · doi:10.1021/j100785a001 · Cited by 19,350
LINCS: A linear constraint solver for molecular simulations
Hess, B., Bekker, H., Berendsen, H. J. C., & Fraaije, J. G. E. M. (1997) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(199709)18:12<1463::aid-jcc4>3.0.co;2-h · Cited by 18,260
Canonical sampling through velocity rescaling
Bussi, G., Donadio, D., & Parrinello, M. (2007) · The Journal of Chemical Physics · doi:10.1063/1.2408420 · Cited by 18,089
Scalable molecular dynamics with NAMD
Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20289 · Cited by 16,914
Yung-Chi, C., & Prusoff, W. H. (1973) · Biochemical Pharmacology · doi:10.1016/0006-2952(73)90196-2 · Cited by 12,922
Open Babel: An open chemical toolbox
O'Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2011) · Journal of Cheminformatics · doi:10.1186/1758-2946-3-33 · Cited by 10,939
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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
Accurate calculation of the absolute free energy of binding for drug molecules
Aldeghi, M., Heifetz, A., Bodkin, M. J., Knapp, S., & Biggin, P. C. (2016) · Chemical Science · doi:10.1039/c5sc02678d · Cited by 347
Structural basis of ketamine action on human NMDA receptors
Zhang, Y., Ye, F., Zhang, T., Lv, S., Zhou, L., Du, D., Lin, H., Guo, F., Luo, C., & Zhu, S. (2021) · Nature · doi:10.1038/s41586-021-03769-9 · Cited by 218
The SARS-CoV-2 Spike variant D614G favors an open conformational state
Mansbach, R. A., Chakraborty, S., Nguyen, K., Montefiori, D. C., Korber, B., & Gnanakaran, S. (2021) · Science Advances · doi:10.1126/sciadv.abf3671 · Cited by 206
RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding
(2022) · doi:10.1002/pro.4527 · Cited by 90
Singh, A. K., Kushwaha, P. P., Prajapati, K. S., Shuaib, M., Gupta, S., & Kumar, S. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104185 · Cited by 58
Kushwaha, P. P., Singh, A. K., Prajapati, K. S., Shuaib, M., Gupta, S., & Kumar, S. (2021) · Microbial Pathogenesis · doi:10.1016/j.micpath.2021.104954 · Cited by 53
Wang, Y., Qin, D., Jin, L., & Liang, G. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105410 · Cited by 19
Gao, S., Wang, L., Bai, F., & Xu, S. (2023) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2023.108614 · Cited by 12
Barraza, G. A., Castro-Guijarro, A. C., de la Fuente Hoffmann, V., Bolívar Ávila, S. J., Flamini, M. I., & Sanchez, A. M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108988 · Cited by 9
Rushing, B., Rohlik, D., Roy, S., Skaff, D., & Garcia, B. (2020) · Molecules · doi:10.3390/molecules25174016 · Cited by 9
Metadata
- Authors
- Kumari, R., Kumar, R., Open Source Drug Discovery Consortium, & Lynn, A.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-06-19
- Volume
- 54
- Issue
- 7
- Pages
- 1951-1962
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2044172327
- Cited by
- 4,882
- References
- 90
- FWCI
- 97.47
- Type
- journal article
- Language
- en
- Open access
- closed
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