To identify central cavity binders of WD40 repeat (WDR) domain of LRRK2, we will follow a multi-strategy approach to increase the probability of finding potent hits. The current release of Enamine-REAL database with ~4B compounds will be screened using each strategy and top-ranking compounds will be selected. Specifically, we propose to employ 4 strategies and approximately 25 compounds from each strategy will be selected. (1) Hybrid LB-SBVS: In this strategy, Enamine-REAL database will be screened for compounds with shape and electrostatic potential similarity with compounds known to bind central-pocket of WD40 repeat domain containing proteins such as WDR5, EED. Some highly potent compounds include OICR-9429, DDO-2213, EED-A-395, EED226 etc. Compounds with similarity above a chosen cutoff would be docked to central cavity of LRRK2 WD40 repeat domain. All compounds will be ranked based on docking scores and 25 compounds will be selected. (2) Lean-docking: Here, we will utilize our recently developed approach to screen ultra-large compound libraries. Briefly, we will dock only a small subset of Enamine-REAL database to central cavity of WDR domain. The docking scores for this subset will be then used to train regressors capable of predicting docking scores of full Enamine-REAL library. The top 100K compounds will be then redocked and best 25 compounds will be selected. (3) Covalent docking: In this strategy, we will follow covalent-docking approach to identify covalent binders of cysteine residues (C2154, C2201, C2247, C2250, C2302 and C2418) lining the central cavity. Initially, Enamine-REAL database will be filtered for compounds with cysteine-focused warhead such as Acrylamides, Chloroacetamides, Acrylonitriles, Disulfides etc. Resulting cysteine-focused library will be covalently docked to WDR domain of LRRK2 targeting one cysteine at a time. All docking scores will be merged, and 25 best compounds will be selected. (4) LB-SBVS: Here, a focused library of putative WDR binders will be created. We will first gather all known central-cavity binders of the WD40 repeat domain containing proteins (N ligands). We will add to this set of ligands a "random chemical background" of size 20*N; randomly drawn from ChEMBL and excluding the previous N ligands. Molecules will be encoded using a counted atom pairs fingerprint and a L2-regularized logistic regressor will be trained. We will use this classifier to predict possible binders of the WD40 domain. Compounds in this library will be prioritized by docking them to central cavity of LRRK2 WDR and 25 compounds will be selected.
OpenEye Software, Schrodinger Suite
RDKit, FTMap, MayaChemTools, PyMol, R program
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