Despite advances in
crystallography, obtaining structures of fragments bound to proteins is still
often elusive. Computational docking is likely to forever be faster than experimental
methods, but how good is it? A new paper in J. Chem. Inf. Mod. by Laura
Chachulski (Jacobs University Bremen) and Björn Windshügel (Universität
Hamburg) assess four popular methods and also provide a public validation set
for others to use.
When evaluating fragment docking
methods, it is essential to have a well-curated set of experimental structures.
To this end, the researchers started by combing the PDB for high quality, high
resolution (< 2 Å) structures of protein-fragment complexes. They used
automated methods to remove structures with poor electron density, close contacts
with other ligands, and various other complications. Further manual curation yielded
93 protein-ligand complex structures. The fragments span a relaxed rule-of-three,
with 7 to 22 non-hydrogen atoms (averaging 13) and ClogP ranging from -4.1to
3.5 (averaging 1.1). I confess that some choices are rather odd, including
oxidized dithiothreitol, benzaldehyde, and γ-aminobutyric acid. The researchers
might have saved themselves some effort, and obtained a more pharmaceutically
attractive set, by starting with previous efforts such as this one.
Having built their benchmark data
set, called LEADS-FRAG, the researchers next tested AutoDock, AutoDock Vina,
FlexX, and GOLD to see how well they would be able to recapitulate reality. The
results? Let’s just say that crystallographers look likely to have job security
for some time.
Only 13 of the 93 protein-fragment
complexes were correctly reproduced as the top hit using all four methods (even
with a reasonably generous RMSD cutoff criterion of < 1.5 Å).There were 18
complexes that none of the methods predicted successfully. Across the four methods,
the top-ranked poses were “correct” 33-54% of the time. Docking methods usually
provide multiple different poses with different scores; up to 30 were
considered here. Looking at lower-ranked poses increased the number of
successes to 27 of the 93 fragments, while only three failed in all methods. Overall,
the correct structure was present among the poses in 53-86% of cases. Changing
the scoring function sometimes led to further improvements.
Why were some fragments more
successfully docked than others? Fragments that were more buried within the
protein (lower solvent-accessible surface area, or SASA) yielded better
predictions than those that were more solvent-exposed. The researchers did not report
on the effect of rotatable bonds; intuitively, one might think that a more
flexible fragment would be harder to dock. A study we highlighted nearly ten
years ago found that fragments with higher ligand efficiency also had higher docking
scores, and it would be interesting to know if that reproduced with this larger
data set.
The researchers conclude by noting
that “these programs do not represent the optimal solution for fragment
docking.” I think this is a fair assessment. And as the researchers acknowledge,
the bar was set low: compounds were docked against the crystal structure of the
protein with ligand computationally removed. In the real world, proteins often
change conformation upon ligand-binding, which would make docking even more difficult.
In addition to trying to determine
how a specific fragment binds, it can also be valuable to computationally screen
large numbers of fragments. The programs used here took between 10 seconds and
42 minutes per ligand, but as we highlighted last year speed continues to
increase.
Most importantly, the public availability
of LEADS-FRAG will allow others to assess their own computational approaches. It
will be fun to revisit this topic in a few years to see how much things have
improved.
No comments:
Post a Comment