Accurately and reliably
predicting fragment binding modes would be preferrable to doing messy,
expensive, and sometimes tedious experimental work, but we’re not there yet.
One of the biggest problems is that, because fragments usually bind weakly to
proteins, it is hard to tell which of several possible binding modes is most
favorable. In an open-access J. Chem. Inf. Model. paper published earlier
this year, Stefano Moro and colleagues at University of Padova report progress.
Their approach, called Thermal
Titration Molecular Dynamics (TTMD), analyzes short molecular dynamics
simulations across increasing temperatures; if the ligand remains bound to the
protein, this indicates a more stable binding mode. (It seems a bit like the
dynamic undocking we wrote about here.) The researchers had previously reported
good results for larger, drug-sized molecules, but not for four
fragment-protein complexes.
Recognizing the low affinities of
fragments, the researchers decided to lower the (virtual) temperatures. Rather
than heating from 300 to 450 K, they heated from 73 to 233 K; ie, from just
below the boiling point of liquid nitrogen to a moderately cold winter’s day in
Minnesota. They first docked fragments using PLANTS-ChemPLP, which is free for
academics, and chose the five best-scoring poses for evaluation.
Next, the researchers performed
TTMD. There are several different ways to assess how well the ligand remains
bound to the protein over the course of a molecular dynamics simulation, and
four different scoring methods were chosen. When TTMD was tested on the four
fragment-protein complexes that had previously failed, at least two of the
scoring methods correctly identified the crystallographic binding mode for
three of the fragments.
Thus encouraged, the researchers
tested ten more compounds bound to six new proteins. The results were quite
encouraging, with up to 86% of crystallographic binding modes being correctly
identified by at least one of the scoring functions in TTMD vs 50% for docking
alone. Impressively, two of the examples were MiniFrag-sized, with just 6 or 7
non-hydrogen atoms, yet the crystallographic pose was identified as the lowest
energy in all four TTMD scores.
This is nice work, but the
question arises how these specific ligands and proteins were chosen. Several
years ago we highlighted a curated set of 93 protein-ligand structures that
were used to benchmark other virtual approaches, and it would be nice to
see how TTMD performs on these. Still, TTMD’s performance on its chosen
examples is encouraging, and laudably the researchers have made their code
freely available. If you try it out, please let us know how it works in your
hands.
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