Binding free energies in the SAMPL5 octa-acid host–guest challenge calculated with DFT-D3 and CCSD(T)

Abstract

We have tried to calculate the free energy for the binding of six small ligands to two variants of the octa-acid deep cavitand host in the SAMPL5 blind challenge. We employed structures minimised with dispersion-corrected density-functional theory with small basis sets and energies were calculated using large basis sets. Solvation energies were calculated with continuum methods and thermostatistical corrections were obtained from frequencies calculated at the HF-3c level. Care was taken to minimise the effects of the flexibility of the host by keeping the complexes as symmetric and similar as possible. In some calculations, the large net charge of the host was reduced by removing the propionate and benzoate groups. In addition, the effect of a restricted molecular dynamics sampling of structures was tested. Finally, we tried to improve the energies by using the DLPNO–CCSD(T) approach. Unfortunately, results of quite poor quality were obtained, with no correlation to the experimental data, systematically too positive affinities (by ~50 kJ/mol) and a mean absolute error (after removal of the systematic error) of 11–16 kJ/mol. DLPNO–CCSD(T) did not improve the results, so the accuracy is not limited by the energy function. Instead, four likely sources of errors were identified: first, the minimised structures were often incorrect, owing to the omission of explicit solvent. They could be partly improved by performing the minimisations in a continuum solvent with four water molecules around the charged groups of the ligands. Second, some ligands could bind in several different conformations, requiring sampling of reasonable structures. Third, there is an indication the continuum-solvation model has problems to accurately describe the binding of both the negatively and positively charged guest molecules. Fourth, different methods to calculate the thermostatistical corrections gave results that differed by up to 30 kJ/mol and there is an indication that HF-3c overestimates the entropy term. In conclusion, it is a challenge to calculate binding affinities for this octa-acid system with quantum–mechanical methods.Electronic supplementary materialThe online version of this article (doi:10.1007/s10822-016-9957-5) contains supplementary material, which is available to authorized users.

Overview of the SAMPL5 host–guest challenge: Are we doing better?

“…For the coupled-cluster methods, the prior offset RMSE, R 2 and regression slopes were, respectively, 6.1 kcal/mol, 0.4 and 3.3, while the current values are 7.0 kcal/mol, 0.5, and 3.3. However, as mentioned above, the quantum submissions showed essentially zero correlation on the mixed OAH/OAMe set after the faulty configuration of OAMe-G4 was replaced with a more proper one [55]. Given this adjustment, the quantum methods performed worse in SAMPL5 compared with SAMPL4.…”

Overview of the SAMPL6 host–guest binding affinity prediction challenge

“…Finally, it seems appropriate to compare the performance of DFT(TPSS)-D3 on OA/TEMOA to DFT/TPSS- c [21] in SAMPL5 and RRHO-551 [74] in SAMPL4 [86]. DFT(TPSS)-D3 an DFT/TPSS-c are very similar in that they both use the DFT-D3 approach to include dispersion correction, but while DFT(TPSS)-D3 generated an ensemble of configurations with MD, DFT/TPSS-c estimated the binding free energy from a single minimized structure.…”

Overview of the SAMPL6 host-guest binding affinity prediction challenge

“…This is 531 similar to the solution adopted in DDM-GAFF in SAMPL6, which also showed a relatively low R 2 compared to 532 the other free energy submissions in the same round of the challenge so it is natural to suspect that it may 533 be particularly challenging to treat this class of host-guest systems with this type of restraint in alchemical 534 calculations. 535 An improvement can also be observed for the movable type method, which was applied to the OA/TEMOA- c [21] in SAMPL5 and RRHO-551 [74] in SAMPL4 [86]. DFT(TPSS)-D3 an DFT/TPSS-c are very similar in that 549 they both use the DFT-D3 approach to include dispersion correction, but while DFT(TPSS)-D3 generated an 550 ensemble of configurations with MD, DFT/TPSS-c estimated the binding free energy from a single minimized 551 structure.…”

“…Unfortunately, the process of assessing the 17 accuracy of current computational approaches to affinity prediction against binding data to biological macro-18 molecules is frustrated by several challenges, such as slow conformational dynamics, multiple titratable 19 groups, and the lack of high-quality blinded datasets. Over the last several SAMPL blind challenge exercises, 20 host-guest systems have emerged as a practical and effective way to circumvent these challenges in assessing 21 the predictive performance of current-generation quantitative modeling tools, while still providing systems 22 36 the correlation obtained by the affinity predictions for OA and TEMOA systems, but a surprising lack of 37 improvement regarding root mean square error over the past several challenge rounds. The data suggests 38 that further refinement of force field parameters, as well as improved treatment of chemical effects (e.g., 39 buffer salt conditions, protonation states) may be required to further enhance predictive accuracy.…”