Prospectively Validated Proteochemometric Models for the Prediction of Small-Molecule Binding to Bromodomain Proteins

The
bromodomain-containing proteins are a ligandable family of
epigenetic readers, which play important roles in oncological, cardiovascular,
and inflammatory diseases. Achieving selective inhibition of specific
bromodomains is challenging, due to the limited understanding of compound
and target selectivity features. In this study we build and benchmark
proteochemometric (PCM) classification models on bioactivity data
for 15,350 data points across 31 bromodomains, using both compound
fingerprints and binding site protein descriptors as input variables,
achieving a maximum performance as measured by the Matthew’s
Correlation Coefficient (MCC) of 0.83 on the external test set. We
also find that histone peptide binding data can be used as a target
descriptor to build a high performing PCM model (MCC 0.80), showing
the transferability of peptide interaction information to modeling
small-molecule bioactivity. 1,139 compounds were selected for prospective
experimental testing by performing a virtual screen using model predictions
and implementing conformal prediction, which resulted in 319 correctly
predicted compound-target pair actives and the correct prediction
for certain selectivity profile combinations of the four bromodomains
tested against. We identify that conformal prediction can be used
to fine-tune the balance between hit retrieval and hit structural
diversity in a virtual screening setting. PCM can be applied to future
virtual screening and compound design, including off-target prediction
for bromodomains.