Predictions of Ligand Selectivity from Absolute Binding Free Energy Calculations

Binding
selectivity is a requirement for the development of a safe
drug, and it is a critical property for chemical probes used in preclinical
target validation. Engineering selectivity adds considerable complexity
to the rational design of new drugs, as it involves the optimization
of multiple binding affinities. Computationally, the prediction of
binding selectivity is a challenge, and generally applicable methodologies
are still not available to the computational and medicinal chemistry
communities. Absolute binding free energy calculations based on alchemical
pathways provide a rigorous framework for affinity predictions and
could thus offer a general approach to the problem. We evaluated the
performance of free energy calculations based on molecular dynamics
for the prediction of selectivity by estimating the affinity profile
of three bromodomain inhibitors across multiple bromodomain families,
and by comparing the results to isothermal titration calorimetry data.
Two case studies were considered. In the first one, the affinities
of two similar ligands for seven bromodomains were calculated and
returned excellent agreement with experiment (mean unsigned error
of 0.81 kcal/mol and Pearson correlation of 0.75). In this test case,
we also show how the preferred binding orientation of a ligand for
different proteins can be estimated via free energy calculations.
In the second case, the affinities of a broad-spectrum inhibitor for
22 bromodomains were calculated and returned a more modest accuracy
(mean unsigned error of 1.76 kcal/mol and Pearson correlation of 0.48);
however, the reparametrization of a sulfonamide moiety improved the
agreement with experiment.