Binding
free energy (ΔGbind) computation can play an important role in prioritizing compounds
to be evaluated experimentally on their affinity for target proteins,
yet fast and accurate ΔGbind calculation remains an elusive task. In this study, we compare
the performance of two popular end-point methods, i.e., linear interaction
energy (LIE) and molecular mechanics/Poisson–Boltzmann surface
area (MM/PBSA), with respect to their ability to correlate calculated
binding affinities of 27 thieno[3,2-d]pyrimidine-6-carboxamide-derived
sirtuin 1 (SIRT1) inhibitors with experimental data. Compared with
the standard single-trajectory setup of MM/PBSA, our study elucidates
that LIE allows to obtain direct (“absolute”) values
for SIRT1 binding free energies with lower compute requirements, while
the accuracy in calculating relative values for ΔGbind is comparable (Pearson’s r = 0.72 and 0.64 for LIE and MM/PBSA, respectively). We
also investigate the potential of combining multiple docking poses
in iterative LIE models and find that Boltzmann-like weighting of
outcomes of simulations starting from different poses can retrieve
appropriate binding orientations. In addition, we find that in this
particular case study the LIE and MM/PBSA models can be optimized
by neglecting the contributions from electrostatic and polar interactions
to the ΔGbind calculations.