Last week we highlighted how NMR-based
fragment screening was able to identify cryptic pockets on a small protein.
Experimental methods are (still) the gold standard, but they take considerable
effort and resources. In a new open-access J. Am. Chem. Soc. paper, Neha
Vithani, David LeBard, and collaborators at OpenEye, Bayer, Genentech, and Khartis
provide a computational approach.
The researchers defined six
categories of cryptic pockets: 1) side-chain motion, 2) loop motion, 3) secondary-structure
motion, 4) secondary-structure change, 5) interdomain motion, and 6) pre-existing
deep pockets with no access to the surface of the protein. They collected a set
of 21 proteins with 26 cryptic pockets overall (some proteins contain more than
one) whose apo (unbound) and holo (ligand-bound) structures were available in
the Protein Data Bank. This “Encore Dataset” contains all six types of cryptic
pockets, though some are represented by only two or three examples.
To computationally identify
cryptic pockets, the researchers used weighted ensemble molecular dynamics (WEMD)
simulations on the apo structures. The details are quite technical but involve
focusing on “normal modes” of protein movement. To find pockets, xenon is
included in the simulation; these large atoms will fill pockets that open up during
the course of the simulation, so detecting bound xenon reveals pockets.
WEMD successfully identified 24
of the 26 cryptic pockets in the Encore Dataset. Interestingly, both failures were
for the same protein, hepatitis C polymerase, one a preexisting deep pocket and
the other a secondary-structure change from order to disorder. (This latter
category is exemplified in one of the cryptic pockets we discussed last week,
in which crystallography revealed that a helix became disordered upon ligand
binding.) The researchers hypothesize that these types of order-to-disorder transitions
may be particularly difficult to detect computationally because the models are
designed to identify secondary structures, thus overstabilizing them.
So WEMD is able to correctly
predict most cryptic pockets in the Encore Dataset, but does it also predict
cryptic pockets that don’t exist? Paul Samulsen famously quipped that economists
had “predicted nine out of the last five recessions,” which actually looks pretty
good compared to WEMD, which predicted more than 30 potential cryptic pockets on
the protein biotin carboxylase. For comparison, the software program fpocket,
which we wrote about last year, predicts 13 for this protein.
To rank which pockets actually
exist, the researchers turned to another computational technique called Target
X, which was described in this Research Square article earlier this year. This proved
reasonably successful at ranking experimentally observed pockets near the top,
with the top two predicted biotin carboxylase pockets corresponding to known
pockets.
This is an interesting paper, but
I am left with more questions. First, while the Encore Dataset is nice, why
didn’t the researchers (also) use CryptoSite, a previously published set of 93
proteins with cryptic sites, which we discussed in 2024? More
importantly, how can drug hunters decide whether to target a specific cryptic
pocket? For instance, does WEMD and Target X allow one to assess whether
cryptic pockets can support high-affinity ligands? These would be interesting questions
for a follow-up paper.
Posts
