Computational protocol: In search of novel ligands using a structure-based approach: a case study on the adenosine A2A receptor

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Protocol publication

[…] All structural modeling was performed using tools in the Schrödinger small-molecule discovery suite. Docking was done with Glide 6.3 [] using the SP scoring function with default settings. We used a previously generated ensemble of A2A receptor models prepared with the Protein Preparation Wizard [], each containing different individual water molecules []. The eMolecules database was prepared using LigPrep with default settings. Protonation and tautomeric states were assigned using Epik [, ]. This resulted in a fully expanded set of ~6.6 M stereoisomeric and tautomeric states from the 2.5 M ligands. The fully expanded set of molecules was docked into the upper node of the decision tree (DT) (i.e. the one that generated the highest enrichment in our previous study). Ligands proceeded through the decision tree according to previously defined rules (Fig. ) based on Glide docking scores. For instance in order to end up in leaf 3, compounds should have a docking score better than -8.865 kcal/mol in the first node, a docking score better than −9.030 kcal/mol in the second and better than −9.115 in the third node of the decision tree. The compounds that ended up in leaf 1, leaf 3, and leaf 5 (5378 total) were considered to be “computational hits” and were subjected to subsequent filters (Fig. ).First we eliminated reactive compounds using the REOS filter (as implemented in KNIME) [, ]. Next, we rescored all poses using the MM-GBSA method [], where we used the VSGB 2.0 implicit solvent model [] and the OPLS2005 force field [] to estimate a binding energy. For the MM-GBSA calculations, explicit water molecules were not considered. All compounds with a MM-GBSA binding energy worse than the mean were eliminated. Since our goal was to find novel scaffolds, we included an explicit similarity filter. This filter was based on all compounds ever tested on the A2AAR for activity from ChEMBL v17 (human, rat, mouse, bovine, guinea pig, sheep, rabbit, and pig), resulting in a total of 12,205 compounds. Tanimoto similarities between all computational hits and all tested compounds were calculated based on Molprint2D [] fingerprints in Canvas [] and computational hits were eliminated if the similarity to any of the compounds in ChEMBL was higher than a defined threshold (Tanimoto >0.25) []. Next, we constructed two different sets: A) 71 compounds from the previous steps, ranked by solvent accessible ligand efficiency (LE2/3) [] and filtered iteratively (based on the rank) by similarity within the selection of compounds (Tanimoto ≤0.25), and B) 8 compounds from the previous steps, prior to the filtering and ranking within the selection of computational hits (before step A), based on both visual inspection and further filtering to ensure a bidentate interaction with Asn2536.55 in the 6th transmembrane helix of the receptor was present. […]

Pipeline specifications

Software tools Glide, Protein Preparation Wizard, eMolecules, LigPrep, Epik, Canvas
Databases ChEMBL
Applications Drug design, Protein interaction analysis
Chemicals Adenosine