Scientists generally want
structural information when a project begins, and ideally that structural information
comes from crystallography. Most of us who have been doing drug discovery for a
while can remember seeing the first structure of a favorite molecule bound to a
target protein and being inspired, reassured, or sometimes confused. But as crystallography
becomes increasingly high throughput, it is now not uncommon to obtain dozens or even hundreds
of structures. What to do with all this bounty? In a recent open-access J. Chem. Inf. Model.
paper, Mihaela Smilova, Brian Marsden, and collaborators at University of Oxford,
the Cambridge Crystallographic Data Centre, and Exscientia describe one
application.
Back in 2016 we wrote about a
computational approach called hotspot mapping, which uses three small fragment
probes (aniline, cyclohexa-2,5-dien-1-one, and toluene) to virtually explore
potential binding sites and map hydrogen bond acceptors, donors, and apolar
interactions. The idea was to predict binding sites and the key interactions
likely to drive affinity. The new paper focuses not just on affinity, but on
selectivity.
The approach starts by taking multiple
structures of the same protein bound to various ligands, especially fragments.
Ligands and water molecules are then removed, and hotspot mapping is conducted
for each structure. Then, all the hotspot maps are combined to generate an “ensemble”
hotspot map, which in theory should give a more complete picture of potential attractive
and repulsive interactions than a single structure.
To assess selectivity, the
ensemble hotspot map of one protein is “subtracted” from that of another. If
the proteins are very closely related, this “selectivity map” might be empty:
all the interactions for one protein would be present in the other. But if there
are differences, they become very apparent.
Several retrospective case studies
are provided. In the first, ensemble hotspot maps were generated from the closely
related bromodomains BRD1 and BRPF1, using 23 and 26 fragment-bound structures,
respectively. The selectivity map clearly shows the potential for a hydrogen
bond donor on a ligand to bind to the backbone amide of a serine in BRD1; the corresponding
residue in BRPF1 is a proline, incapable of making this interaction. And
indeed, an examination of the literature revealed that this interaction had previously
been used to generate inhibitors of BRD1 that were 15-fold selective over
BRPF1.
The kinases p38α and ERK2 are also
closely related, but selectivity maps generated from five p38α structures and
17 ERK2 structures revealed a hydrophobic pocket in the former but not in the
latter. This pocket had previously been used to generate selective inhibitors of
p38α. Similarly, 28 structures of CK2α and 32 structures of PIM1 were used to
generate a selectivity map that also revealed a hydrophobic pocket that can form
in the former protein and had been used to generate selective inhibitors.
Generally, the more structures
available, the more informative the selectivity maps are. The researchers note
that though they only used five p38α structures, the fragments were chosen to
be diverse (and interestingly all of them made interactions in the hydrophobic
pocket). Also, while some protein flexibility can improve the maps, too much is
a problem. (For the kinases, only DFG-in structures were used, for example.)
This method is a nice synthesis
of experimental and computational techniques. A skeptic might argue that it
doesn’t provide fundamentally new information: in the examples provided, the
selectivity features had already been found and exploited by medicinal
chemists. But the automated process and the clear output may speed things up,
especially for newer targets, and indeed the researchers note that it is being
applied in-house at Exscientia.
Perhaps most importantly, if you’d
like to try it yourself, the code is freely available here. Happy mapping!
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