Assessing the performance of the MM/PBSA and MM/GBSA methods: I. The accuracy of binding free energy calculations based on molecular dynamics simulations

By Tingjun Hou, Junmei Wang, Youyong Li, Wei Wang. Journal of Chemical Information and Modeling, 2010

2,526 citations recorded by the source corpus.

The Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) and the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) methods calculate binding free energies for macromolecules by combining molecular mechanics calculations and continuum solvation models. To systematically evaluate the performance of these methods, we report here an extensive study of 59 ligands interacting with six different proteins. First, we explored the effects of the length of the molecular dynamics (MD) simulation, ranging from 400 to 4800 ps, and the solute dielectric constant (1, 2 or 4) to the binding free energies predicted by MM/PBSA. The following three important conclusions could be observed: (1). MD simulation lengths have obvious impact on the predictions, and longer MD simulations are not always necessary to achieve better predictions; (2). The predictions are quite sensitive to solute dielectric constant, and this parameter should be carefully determined according to the characteristics of the protein/ligand binding interface; (3). Conformational entropy showed large fluctuations in MD trajectories and a large number of snapshots are necessary to achieve stable predictions. Next, we evaluated the accuracy of the binding free energies calculated by three Generalized Born (GB) models. We found that the GB model developed by Onufriev and Case was the most successful model in ranking the binding affinities of the studied inhibitors. Finally, we evaluated the performance of MM/GBSA and MM/PBSA in predicting binding free energies. Our results showed that MM/PBSA performed better in calculating absolute, but not necessarily relative, binding free energies than MM/GBSA. Considering its computational efficiency, MM/GBSA can serve as a powerful tool in drug design, where correct ranking of inhibitors is often emphasized.

Subfields: Atomic and Molecular Physics, and Optics, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/8cab44f6abdb7bdaea5fa6daa77ae7e03054040b

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In Atomic and Molecular Physics, and Optics

1. Samuel Genheden
2. Ron O. Dror
3. Ulf Ryde
4. Scott A. Hollingsworth

In Molecular Biology

1. Peng Sang
2. Samuel Genheden
3. Xing Du
4. Jing Liang
5. Yi Li
6. Ron O. Dror
7. Ulf Ryde
8. Xing-lai Ji

In Spectroscopy

1. Samuel Genheden
2. Ron O. Dror
3. Ulf Ryde
4. Scott A. Hollingsworth

Citations in the corpus

• The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities (2015, methodology): “The method has been used in a range of settings, including protein design [9], protein--protein interactions [10,11], conformer stability [12,13] and re-scoring [14,15].”
• Molecular dynamics simulation for all (2018, positive): “In certain applications to ligand and protein design, simulations are used simply as a relatively inexpensive, although rough, filter for binding energy or stability to winnow down a large pool of candidates to a smaller one that can be tested experimentally (Chevalier et al., 2017; Hou et al., 2011”