End-Point Affinity Estimation of Galectin Ligands by Classical and Semiempirical Quantum Mechanical Potentials

The use of quantum
mechanical potentials in protein–ligand
affinity prediction is becoming increasingly feasible with growing
computational power. To move forward, validation of such potentials
on real-world challenges is necessary. To this end, we have collated
an extensive set of over a thousand galectin inhibitors with known
affinities and docked them into galectin-3. The docked poses were
then used to systematically evaluate several modern force fields and
semiempirical quantum mechanical (SQM) methods up to the tight-binding
level under consistent computational workflow. Implicit solvation
models available with the tested methods were used to simulate solvation
effects. Overall, the best methods in this study achieved a Pearson
correlation of 0.7–0.8 between the computed and experimental
affinities. There were differences between the tested methods in their
ability to rank ligands across the entire ligand set as well as within
subsets of structurally similar ligands. A major discrepancy was observed
for a subset of ligands that bind to the protein via a halogen bond,
which was clearly challenging for all the tested methods. The inclusion
of an entropic term calculated by the rigid-rotor-harmonic-oscillator
approximation at SQM level slightly worsened correlation with experiment
but brought the calculated affinities closer to experimental values.
We also found that the success of the prediction strongly depended
on the solvation model. Furthermore, we provide an in-depth analysis
of the individual energy terms and their effect on the overall prediction
accuracy.