posted on 2022-01-12, 18:35authored byMihaela D. Smilova, Peter R. Curran, Chris J. Radoux, Frank von Delft, Jason C. Cole, Anthony R. Bradley, Brian D. Marsden
Selectivity is a
crucial property in small molecule development.
Binding site comparisons within a protein family are a key piece of
information when aiming to modulate the selectivity profile of a compound.
Binding site differences can be exploited to confer selectivity for
a specific target, while shared areas can provide insights into polypharmacology.
As the quantity of structural data grows, automated methods are needed
to process, summarize, and present these data to users. We present
a computational method that provides quantitative and data-driven
summaries of the available binding site information from an ensemble
of structures of the same protein. The resulting ensemble maps identify
the key interactions important for ligand binding in the ensemble.
The comparison of ensemble maps of related proteins enables the identification
of selectivity-determining regions within a protein family. We applied
the method to three examples from the well-researched human bromodomain
and kinase families, demonstrating that the method is able to identify
selectivity-determining regions that have been used to introduce selectivity
in past drug discovery campaigns. We then illustrate how the resulting
maps can be used to automate comparisons across a target protein family.