D3R (Drug Design Data Resource) Grand Challenge is a blinded prediction challenge for the computational chemistry community, with components addressing pose-prediction, affinity ranking, and free energy calculations. Its fourth installment, D3R GC4, was held from October to December 2018.
Our group participated in the pose prediction challenge for the macrocyclic inhibitors for Beta secretase 1 (BACE1). This protein is involved in the generation of beta-amyloid peptides and presents an important target for developing drugs for Alzheimer’s disease. In the stage 1a of the challenge, the organizers presented participants with the apo-structure of the receptor and SMILES strings describing 20 ligands.
According to the official rankings, the template-based method developed in our group scored best, out of 74 total submitted entries, in terms of the Mean RMSD. Our group achieved sub-angstrom mean and median RMSD for this challenge.
Our method relies on finding structures of distant homologs of the target protein that bind similar ligands, and using them at all stages of the protocol: initial pose generation, structure refinement, and the final scoring. More details are available in our paper, published in the Journal of Computer-Aided Molecular Design.
We refined and automated the approach used in this challenge. It is now available for free academic and non-commercial use as a user-friendly web-server, ClusPro LigTBM. This version of the protocol is described in our Journal of Molecular Biology paper.