Hybrid of tiered screening workflow involving alpha spheres volume derived fastROCS queries, in-house fragment flooding pharmacophore analysis, followed by OpenEye docking and FEP

We will identify the most conserved residues of the Nsp3 Mac1 adenine binding cavity and the proximal ribose site where there are co-crystalized fragments (54 and 9 PBD submissions respectively) and lead-like small molecules (i.e. Gahbauer et al., bioRxiv. 2022). This will be done by performing multiple sequence alignment (MSA) with the Kalign algorithm on approximately 200,000 SARS-CoV-2 Nsp3 sequences from the NCBI. We will determine the amino acids close to these fragments/molecules that can form interactions with the predicted hit molecules. Preliminary analysis found multiple conserved amino acids (D226, N244, F336, G252, S332-G337 and L247-G252) in the binding sites not only across SARS-CoV-2 but also in MERS-CoV (YP_009047231.1) and SARS-CoV-1 (YP_009944368.1) sequences. At the end of the hit generation phase, we will determine if the hit molecules make interactions with the most conserved residues in the binding pocket. This will support our hypothesis that the predicted hits will be resistant to mutations in the Nsp3 Mac1 binding site and possibly also bind to the Nsp3 Mac1 of different CoVs.

We will implement the following tiered virtual screening (VS) strategy, the majority of which we developed for the recent Nsp13 CACHE challenge:

 

Stage 1 (fastROCS). We will use MOE SiteFinder to locate Alpha-Spheres dummy atoms in the Nsp3 Mac1 domain adenine binding cavity and the proximal ribose site. The available Nsp3-ligand co-crystallised structures will be studied. These dummy atoms reflect hydrophobic/hydrophilic cavity points which will be used to generate volume queries by assigning carbon/oxygen atoms to the hydrophobic and hydrophilic dummy atoms respectively. Different fastROCS volume/feature query sub-sets will be created out of the dummy atoms. An active/decoy haystack of known Mac1 domain active compounds will be created to elucidate the selectivity of the different queries/X-ray structures and determine the best performing one for VS utility. This will be input to the first stage of screening the Enamine databases – using fastROCS due to its incomparable throughput. We have already generated 5 conformations of the HAC 6-21, 22-23 and 24 HAC Enamine databases using OpenEye OMEGA. Compounds with unspecified stereochemistry and unknown names were removed.  The output from this stage will be 500,000 best ranked compounds from Enamine.

 

Stage 2 (MoPBS). In order to sample the binding preference of the Nsp3 Mac1 binding sites in more detail, we will use our recently published in-house MoPBS algorithm to overlay each protein-ligand complex, flood each protein binding site with fragments (HBA, HBD, Aro and Hyd), combine the fragment output for k-means clustering and assignment of pharmacophore features within the binding site. Multiple MOE pharmacophores containing 5-10 features will be created and will be validated against the Mac1 domain haystack created in Stage 1 and the best performing pharmacophores will be used to query the output from the fastROCS screening stage. The stringency of the pharmacophores will be tailored to select 50,000 molecules from each Enamine dataset.

 

Stage 3 (FRED Docking). 150,000 compounds will enter this stage. OpenEye’s MakeReceptor will prepare all the available protein-ligand X-ray structures for docking. Initially the Mac1 haystack will be used to determine which of the X-ray structures give the best docking enrichment and are most appropriate for VS. Docking studies of the fastROCS/pharmacophore hits will be performed on the five best ranking X-ray structures’ binding sites using OpenEye FRED. An in-house software integration platform, DataPype, will streamline dataset processing, docking calculations (manuscript under preparation) and reporting. DataPype is python software designed to seamlessly integrate each step of a VS process: from ligand and protein preparation, through to tiered or consensus screening with multiple VS algorithms and metrics reporting. Only ligands that interact with previously identified conserved amino acids will be considered. 

 

Stage 4 (FEP). Using GROMACS, we will calculate the absolute binding free energy (ABFE) for protein-ligand complexes – 200 complexes from which 150 with the best ABFE will be selected.

 

Stage 5 (Clustering). Finally, we will perform ECFP clustering, using Pipeline Pilot, of the hit molecules into 150 clusters and sort each cluster by docking score. This selection will be sent to the CACHE Team who will return a list of compound prices which will enable us to select 100 compounds for purchase. We will visually examine mapping at each stage of each candidate compound for purchase, prior to confirming the choice of one compound from each cluster.

 

Preliminary target product profile: This will map to the CACHE scoring scheme including IC50 <1 μM, Log D<3, MW<400.

 

Hit optimisation: If hit compounds are identified our models will be updated with the new released activity data. MCS approaches and similarity analyses will be used to select subsets of Enamine for screening through the updated process detailed above.

We will perform the study in 2 Phases. (1) We will use bioinformatics tools to identify the most conserved residues in the binding pocket of SC-2 Nsp3 Mac1. (2) A 5-stage tiered screening approach will be used for predicting inhibitors.  

1. Use volume/shape information of the binding pocket (fastROCS)
2. Use in-house pharmacophore generation software (MoPBS/MOE)
3. Perform docking in the binding pocket and use the in-house DataPype screening platform to streamline and automate VS processing (FRED)
4. Predicting binding free energies (GROMACS)
5. Cluster hits based on structure circular fingerprints, analyse prices, and visually confirm selected hits for purchase (Pipeline Pilot)

Tiered screening incorporating molecular shape, pharmacophore features, docking, FEP and clustering

• Molecular Operating Environment (MOE) by the Chemical Computing Group
• OpenEye- fastROCS (hit identification using shape information), OMEGA (generating conformations), MakeReceptor (preparing binding pocket for docking) and FRED (docking)
• Pipeline Pilot (Biovia)

• Kalign
• In-house MoPBS pharmacophore generation software
• In-house VS streamlining software DataPype
• GROMACS

Dr Fayne has published >40 papers, a book chapter and two patents, the vast majority of which describe computational design approaches for discovering novel small molecules targeting proteins involved in human disease.

Dr Fayne and Ms Kandwal have recently published a paper using pharmacophore queries to propose the mechanism of action and possible repurposing opportunities of approved drugs showing in vitro efficacy against SARS-CoV-2.

Kandwal S. Fayne D. Repurposing drugs for treatment of SARS-CoV-2 infection: computational design insights into mechanisms of action, J Biomol Struct Dyn, 2022, 40(3):1316-1330. Published online Sept 2020. doi: 10.1080/07391102.2020.1825232

 

Ms Kandwal started her PhD with Dr Fayne in Sept 2021 on designing small molecule inhibitors of conserved regions of SARS-CoV-2 nsps.

 

The MoPBS software is described in this recently published paper:

Braun J, Fayne D. Mapping of Protein Binding Sites using clustering algorithms - Development of a pharmacophore based drug discovery tool. J Mol Graph Model. 2022 Sep;115:108228

 

A paper describing DataPype is currently under preparation for submission.

 

Tiered-screening approaches previously developed by the research group are described in the following papers. All of these published approaches have successfully identified hit compounds from commercial vendor databases, primarily by only screening the SPECS library. The proposed work in this study represented a significant increase in complexity and scale from these previous works but we have the expertise, infrastructure and software to ensure a successful completion of the described project.

Nevin, DK, Peters, MB, Carta, G, Fayne, D, Lloyd, DG, Integrated virtual screening for the identification of novel and selective Peroxisome Proliferating Activated Receptor (PPAR) modulators. J. Med. Chem. 2012 55(11):4978-89

McKay, PB, Fayne, D*, Horn, HW, James, T, Peters, MB, Carta, G, Caboni, L, Nevin, DK, Price, T, Bradley, G, Williams, DC, Rice, JE, Lloyd, DG. Consensus computational ligand-based design for the identification of novel modulators of human Estrogen Receptor alpha. Mol Inf. 2012 31(3-4) 246–258. *Corresponding author

Caboni L, Kinsella GK, Blanco F, Fayne D, Jagoe WN, Carr M, Williams DC, Meegan MJ, Lloyd DG. “True” antiandrogens-selective non-ligand-binding pocket disruptors of androgen receptor-coactivator interactions: novel tools for prostate cancer, J Med Chem. 2012 55(4):1635-44

McKay PB, Peters MB, Carta G, Flood CT, Dempsey E, Bell A, Berry C, Lloyd DG, Fayne D. Identification of plasmepsin inhibitors as selective anti-malarial agents using ligand based drug design. Bioorg Med Chem Lett. 2011 1;21(11):3335-41

Yang Y, Carta G, Peters MB, Price T, O’Boyle N, Knox AJS, Fayne D, Williams DC, Meegan MJ, Lloyd DG. tieredScreen – layered virtual screening tool for the identification of novel Estrogen Receptor Alpha modulators. Mol Inf. 2010, 29, 421 – 430