Computational protocol: Identification of NovelAdenosine A2A ReceptorAntagonists by Virtual Screening

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[…] Homology models were constructed from the avian β1 adrenergic GPCR crystal structure bound to cyanopindolol (PDB code 2VT4)., Owing to the relatively low percentage identity between the two proteins (25% overall, <20% around the ligand binding site), two initial homology models of the adenosine A2A receptor were generated, using different methods. This provided a means to assess consistency in the alignments, the variability within the built structures, and which regions of the models had higher and lower confidence associated with them. One model was constructed using MODELLER,, while the other was constructed using MOE with manual readjustment of the ClustalW alignment where necessary. The alignment in each case was checked to ensure consistency with known GPCR conserved motifs and particularly the conserved disulfide bond, common to family A GPCRs, which is located between the top of helix 3 and the extracellular loop 2. Apart from the extracellular loop 2, the rest of the modeled structures showed good agreement and in the MOE model this loop was not modeled beyond the first few residues up to and including Phe168 because of the very poor alignment in this region. The two homology models were then further evaluated using two different approaches. First, SDM data, both from the literature and in-house, were mapped onto the modeled protein structures. The majority of these residues lined the anticipated ligand binding site in each of the models. The mutation sites showed good consistency in the locations of the residues when comparing the two structures. Second, both models were used to dock a small number of known A2A antagonists, including ZM241385, into each of the structures using Glide as the docking engine., This was done to explore the potential docking modes that could be achieved and to assist in the development of conditions and protocols for use in analysis of the virtual screen, with similarly sized decoys also being used in the studies. It was decided to use the two models in parallel in the virtual screen.Ligand data sets were drawn from CAP, a collection of vendor catalogues giving details of screening samples for purchase. A subset of the BioFocus SoftFocus library collections were also screened after excluding compounds designed to target GPCRs. The compounds from CAP were prefiltered to remove those molecules containing unwanted chemical functionality. Physicochemical profiles for the data set were biased toward a CNS-like profile based on the recommendations in Pajouhesh et al. and the properties of a set of literature A2A antagonists. 545K compounds were prepared for screening, and all or a subset from more stringent prefiltering and clustering docked into each of the models using the SP algorithm within the Schrödinger Glide software, running on a 28 CPU Linux cluster. Details of the workflows, screening compound numbers, and filters used for the virtual screening and postprocessing analyses with each homology model are detailed in the (Figures S2–S4). The protein preparation and docking experiments were done within the Schrödinger Maestro package. The grid generation necessary for docking was done within Glide. The residues highlighted in SDM experiments (in-house and external) were used to further define the cavity of the grid. However, no constraints were added in the grid generation to ensure that subsequent dockings were not biased in any way. As standard, up to 3 poses per molecular structure were stored for analysis. For some compound subsets, Glide XP docking was carried out on the ligands with 10 poses per ligand being stored. A selection of 372 virtual hits was finally prioritized for purchasing, following manual inspection and subsequent triaging by medicinal chemistry of the most promising docking solutions.Subsequent docking experiments on the hits from the radioligand binding assay and also on analogues of the two hit chemotypes derived from 1 and 5 were carried out. They were guided by ligand SAR, an iterative process of assessing SDM data, and also by designing our own BPM mutants to confirm or rule out possible binding modes, as previously described. As part of this, more detailed modeling work was carried out, including the use of induced fit docking and restrained minimization work. For the more active compounds, the MOE derived model gave more plausible results, and therefore, this was used as the basis for further improvement and validation work. In particular, validation and improvement of the homology models for docking were conducted, focused on ZM241385, because of the wealth of SAR for this series and the amount of SDM data available for the ligand at the adenosine A2A receptor., The induced fit docking (IFD) protocol was used within Maestro with an autogenerated box size around the residues highlighted by SDM as having a large effect on antagonist binding, namely, Ile662.64, Val843.32, Leu853.33, Glu151ECL2, Leu167ECL2, Glu169ECL2, Asn1815.42, Phe1825.43, His2506.52, Asn2536.55, Phe2576.59, Tyr2717.36, Ile2747.39, and His2787.43. […]

Pipeline specifications

Software tools MODELLER, Clustal W
Application Protein structure analysis
Chemicals Adenosine