Abstract
We have studied the binding of 102 ligands to the farnesoid X receptor within the D3R Grand Challenge 2016 blind-prediction competition. First, we employed docking with five different docking software and scoring functions. The selected docked poses gave an average root-mean-squared deviation of 4.2 Å. Consensus scoring gave decent results with a Kendall’s τ of 0.26 ± 0.06 and a Spearman’s ρ of 0.41 ± 0.08. For a subset of 33 ligands, we calculated relative binding free energies with free-energy perturbation. Five transformations between the ligands involved a change of the net charge and we implemented and benchmarked a semi-analytic correction (Rocklin et al., J Chem Phys 139:184103, 2013) for artifacts caused by the periodic boundary conditions and Ewald summation. The results gave a mean absolute deviation of 7.5 kJ/mol compared to the experimental estimates and a correlation coefficient of R2 = 0.1. These results were among the four best in this competition out of 22 submissions. The charge corrections were significant (7–8 kJ/mol) and always improved the results. By employing 23 intermediate states in the free-energy perturbation, there was a proper overlap between all states and the precision was 0.1–0.7 kJ/mol. However, thermodynamic cycles indicate that the sampling was insufficient in some of the perturbations.
Highlights
The increase in computer power and advances in protein crystallography and drug discovery during the latest decades have nourished the dream that drugs one day may be developed by computational methods [1]
We study the binding of 102 inhibitors to the farnesoid X receptor (FXR) [26] from the blind-prediction drug-design data resource (D3R) Grand Challenge 2016 (GC2) [27]
As a part of the D3R Grand Challenge 2016, we have performed a prospective study of the binding of 102 inhibitors to FXR
Summary
The increase in computer power and advances in protein crystallography and drug discovery during the latest decades have nourished the dream that drugs one day may be developed by computational methods [1]. One of the best is alchemical free-energy perturbation (FEP) [2,3,4], calculating the energies by exponential averaging, thermodynamic integration, Bennett acceptance ration (BAR), multistate BAR (MBAR) or similar methods [5,6,7,8]. Several recent large-scale retrospective benchmark studies have indicated that relative binding free energies of drug-like molecules to protein targets can be calculated by FEP with a mean absolute deviation (MAD) from experimental affinities of 4–6 kJ/mol [9,10,11,12]. A similar accuracy has been reported for prospective calculations of binding affinities in host–guest systems [13, 14]. For protein systems, prospective predictions have typically been quite poor with MADs of 4–16 kJ/mol [15, 16], probably owing to uncertainties and variations in the binding mode
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