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Recent Progress in Treating Protein-Ligand Interactions with Quantum-Mechanical Methods

Overview
Journal Int J Mol Sci
Publisher MDPI
Date 2016 May 20
PMID 27196893
Citations 9
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Abstract

We review the first successes and failures of a "new wave" of quantum chemistry-based approaches to the treatment of protein/ligand interactions. These approaches share the use of "enhanced", dispersion (D), and/or hydrogen-bond (H) corrected density functional theory (DFT) or semi-empirical quantum mechanical (SQM) methods, in combination with ensemble weighting techniques of some form to capture entropic effects. Benchmark and model system calculations in comparison to high-level theoretical as well as experimental references have shown that both DFT-D (dispersion-corrected density functional theory) and SQM-DH (dispersion and hydrogen bond-corrected semi-empirical quantum mechanical) perform much more accurately than older DFT and SQM approaches and also standard docking methods. In addition, DFT-D might soon become and SQM-DH already is fast enough to compute a large number of binding modes of comparably large protein/ligand complexes, thus allowing for a more accurate assessment of entropic effects.

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References
1.
Kolar M, Fanfrlik J, Hobza P . Ligand conformational and solvation/desolvation free energy in protein-ligand complex formation. J Phys Chem B. 2011; 115(16):4718-24. DOI: 10.1021/jp2010265. View

2.
Korth M . Empirical hydrogen-bond potential functions--an old hat reconditioned. Chemphyschem. 2011; 12(17):3131-42. DOI: 10.1002/cphc.201100540. View

3.
Paton R, Goodman J . Hydrogen bonding and pi-stacking: how reliable are force fields? A critical evaluation of force field descriptions of nonbonded interactions. J Chem Inf Model. 2009; 49(4):944-55. DOI: 10.1021/ci900009f. View

4.
Cole D, Rajendra E, Roberts-Thomson M, Hardwick B, McKenzie G, Payne M . Interrogation of the protein-protein interactions between human BRCA2 BRC repeats and RAD51 reveals atomistic determinants of affinity. PLoS Comput Biol. 2011; 7(7):e1002096. PMC: 3136434. DOI: 10.1371/journal.pcbi.1002096. View

5.
Muddana H, Sapra N, Fenley A, Gilson M . The electrostatic response of water to neutral polar solutes: implications for continuum solvent modeling. J Chem Phys. 2013; 138(22):224504. PMC: 3695974. DOI: 10.1063/1.4808376. View