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Protein–ligand free energies of binding from full-protein DFT calculations: convergence and choice of exchange–correlation functional

Protein–ligand free energies of binding from full-protein DFT calculations: convergence and choice of exchange–correlation functional
Protein–ligand free energies of binding from full-protein DFT calculations: convergence and choice of exchange–correlation functional
The accurate prediction of protein–ligand binding free energies with tractable computational methods has the potential to revolutionize drug discovery. Modeling the protein–ligand interaction at a quantum mechanical level, instead of relying on empirical classical-mechanics methods, is an important step toward this goal. In this study, we explore the QM-PBSA method to calculate the free energies of binding of seven ligands to the T4-lysozyme L99A/M102Q mutant using linear-scaling density functional theory on the whole protein–ligand complex. By leveraging modern high-performance computing we perform over 2900 full-protein (2600 atoms) DFT calculations providing new insights into the convergence, precision and reproducibility of the QM-PBSA method. We find that even at moderate sampling over 50 snapshots, the convergence of QM-PBSA is similar to traditional MM-PBSA and that the DFT-based energy evaluations are very reproducible. We show that in the QM-PBSA framework, the physically-motivated GGA exchange–correlation functional PBE outperforms the more modern, dispersion-including non-local and meta-GGA-nonlocal functionals VV10 and B97M-rV. Different empirical dispersion corrections perform similarly well but the three-body dispersion term, as included in Grimme's D3 dispersion, is significant and improves results slightly. Inclusion of an entropy correction term sampled over less than 25 snapshots is detrimental while an entropy correction sampled over the same 50 or 100 snapshots as the enthalpies improves the accuracy of the QM-PBSA method. As full-protein DFT calculations can now be performed on modest computational resources our study demonstrates that they can be a useful addition to the toolbox of free energy calculations.
1463-9076
Gundelach, Lennart
a091b82a-bae9-416e-b3dd-c046eda60a38
Skylaris, Chris-Kriton
8f593d13-3ace-4558-ba08-04e48211af61
Fox, Thomas
7cc3d2c5-1278-46b1-99e0-602108f14114
Tautermann, Christofer S.
ec0dd073-8649-4c3f-b1a5-1b310a1a5993
Gundelach, Lennart
a091b82a-bae9-416e-b3dd-c046eda60a38
Skylaris, Chris-Kriton
8f593d13-3ace-4558-ba08-04e48211af61
Fox, Thomas
7cc3d2c5-1278-46b1-99e0-602108f14114
Tautermann, Christofer S.
ec0dd073-8649-4c3f-b1a5-1b310a1a5993

Gundelach, Lennart, Skylaris, Chris-Kriton, Fox, Thomas and Tautermann, Christofer S. (2021) Protein–ligand free energies of binding from full-protein DFT calculations: convergence and choice of exchange–correlation functional. Physical Chemistry Chemical Physics. (doi:10.1039/D1CP00206F).

Record type: Article

Abstract

The accurate prediction of protein–ligand binding free energies with tractable computational methods has the potential to revolutionize drug discovery. Modeling the protein–ligand interaction at a quantum mechanical level, instead of relying on empirical classical-mechanics methods, is an important step toward this goal. In this study, we explore the QM-PBSA method to calculate the free energies of binding of seven ligands to the T4-lysozyme L99A/M102Q mutant using linear-scaling density functional theory on the whole protein–ligand complex. By leveraging modern high-performance computing we perform over 2900 full-protein (2600 atoms) DFT calculations providing new insights into the convergence, precision and reproducibility of the QM-PBSA method. We find that even at moderate sampling over 50 snapshots, the convergence of QM-PBSA is similar to traditional MM-PBSA and that the DFT-based energy evaluations are very reproducible. We show that in the QM-PBSA framework, the physically-motivated GGA exchange–correlation functional PBE outperforms the more modern, dispersion-including non-local and meta-GGA-nonlocal functionals VV10 and B97M-rV. Different empirical dispersion corrections perform similarly well but the three-body dispersion term, as included in Grimme's D3 dispersion, is significant and improves results slightly. Inclusion of an entropy correction term sampled over less than 25 snapshots is detrimental while an entropy correction sampled over the same 50 or 100 snapshots as the enthalpies improves the accuracy of the QM-PBSA method. As full-protein DFT calculations can now be performed on modest computational resources our study demonstrates that they can be a useful addition to the toolbox of free energy calculations.

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More information

Accepted/In Press date: 25 March 2021
e-pub ahead of print date: 30 March 2021

Identifiers

Local EPrints ID: 448729
URI: http://eprints.soton.ac.uk/id/eprint/448729
ISSN: 1463-9076
PURE UUID: 7ca75750-0ea8-44ea-a06c-3f12541ae01c
ORCID for Chris-Kriton Skylaris: ORCID iD orcid.org/0000-0003-0258-3433

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Date deposited: 04 May 2021 16:37
Last modified: 05 May 2021 01:41

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Contributors

Author: Lennart Gundelach
Author: Thomas Fox
Author: Christofer S. Tautermann

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