Small-molecule docking remains one of the most valuable computational techniques for the structure prediction of protein–small-molecule complexes. It allows us to study the interactions between compounds and the protein receptors they target at atomic detail in a timely and efficient manner.
Here, we present a new protocol in HADDOCK (High Ambiguity Driven DOCKing), our integrative modeling platform, which incorporates homology information for both receptor and compounds.
It makes use of HADDOCK’s unique ability to integrate information in the simulation to drive it toward conformations, which agree with the provided data. The focal point is the use of shape restraints derived from homologous compounds bound to the target receptors.
We have developed two protocols: in the first, the shape is composed of dummy atom beads based on the position of the heavy atoms of the homologous template compound, whereas in the second, the shape is additionally annotated with pharmacophore data for some or all beads.
For both protocols, ambiguous distance restraints are subsequently defined between those beads and the heavy atoms of the ligand to be docked.
We have benchmarked the performance of these protocols with a fully unbound version of the widely used DUD-E (Database of Useful Decoys-Enhanced) dataset. In this unbound docking scenario, our template/shape-based docking protocol reaches an overall success rate of 81% when a reliable template can be identified (which was the case for 99 out of 102 complexes in the DUD-E dataset), which is close to the best results reported for bound docking on the DUD-E dataset.[maxbutton id=”4″ url=”https://doi.org/10.1021/acs.jcim.1c00796″ text=”Read more” linktitle=”Journal of Chemical Information and Modeling: Shape-Restrained Modeling of Protein–Small-Molecule Complexes with High Ambiguity Driven DOCKing” ]
Panagiotis I. Koukos, Manon Réau, Alexandre M. J. J. Bonvin (2021):
Shape-Restrained Modeling of Protein–Small-Molecule Complexes with High Ambiguity Driven DOCKing.
Journal of Chemical Information and Modeling 61(9)