WIDOCK: a reactive docking protocol for virtual screening of covalent inhibitors

Abstract

Here we present WIDOCK, a virtual screening protocol that supports the selection of diverse electrophiles as covalent inhibitors by incorporating ligand reactivity towards cysteine residues into AutoDock4. WIDOCK applies the reactive docking method (Backus et al. in Nature 534:570–574, 2016) and extends it into a virtual screening tool by introducing facile experimental or computational parametrization and a ligand focused evaluation scheme together with a retrospective and prospective validation against various therapeutically relevant targets. Parameters accounting for ligand reactivity are derived from experimental reaction kinetic data or alternatively from computed reaction barriers. The performance of this docking protocol was first evaluated by investigating compound series with diverse warhead chemotypes against KRASG12C, MurA and cathepsin B. In addition, WIDOCK was challenged on larger electrophilic libraries screened against OTUB2 and NUDT7. These retrospective analyses showed high sensitivity in retrieving experimental actives, by also leading to superior ROC curves, AUC values and better enrichments than the standard covalent docking tool available in AutoDock4 when compound collections with diverse warheads were investigated. Finally, we applied WIDOCK for the prospective identification of covalent human MAO-A inhibitors acting via a new mechanism by binding to Cys323. The inhibitory activity of several predicted compounds was experimentally confirmed and the labelling of Cys323 was proved by subsequent MS/MS measurements. These findings demonstrate the usefulness of WIDOCK as a warhead-sensitive, covalent virtual screening protocol.

Advances in UDP-N-Acetylglucosamine Enolpyruvyl Transferase (MurA) Covalent Inhibition

“…Fosfomycin is the unique clinically available inhibitor of MurA acting competitively against phosphoenolpyruvate. Its mode of action is related to the covalent binding to the thiol group of Cys115 residue in the active site of MurA ( Kahan et al, 1974 ; Falagas et al, 2016 ; Sutanto et al, 2020 ; Scarpino et al, 2021 ). Naturally produced by Streptomyces spp.…”

Chemical Reactivity Prediction: Current Methods and Different Application Areas

“…[52][53][54] Flanagan et al found that calculated reaction barrier heights correlate strongly with GSH reactivity (r 2 = 0.92). [55] Calculated reaction barriers coupled with the docking software AutoDock4 led to development of a virtual screening tool, WIDOCK, to support identification of novel covalent inhibitors; [56] performance of the tool was illustrated on several industrially relevant examples (KRAS, MurA, cathepsin B).…”

“…In vivo chemical reactivity [43] Modeling covalent modification of biological thiols Quantum mechanical/molecular mechanical dynamics simulations (Hirshfeld charges) [44] Predicting covalent warhead reactivity Quantum mechanical modeling of adduct formation [47] Estimating covalent reactivity of acrylamides Quantum mechanical calculations of electrophilicity index [48] Predicting activation energy of acrylamides, 2-chloroacetamides and propargylamides Quantum mechanical calculations of electrophilicity index [49] Assessing organic chemical reactivity Quantum mechanical calculations of Fukui electrophilicity index [51] Predicting the reactivity of covalent inhibitors Quantum mechanical calculations (proton affinity) [52] Automated transition state search Quantum mechanical/molecular mechanical calculations [53] Automated transition state search Quantum mechanical calculations [54] Automated transition state search Quantum mechanical calculations [55] Predicting electrophilic reactivity of covalent inhibitors Quantum mechanical calculations (kinetic reaction barriers) [56] WIDOCK: identifying novel covalent inhibitors Virtual screening, docking [57] BIreactive: estimate covalent warhead reactivity Quantum mechanical calculations, regressor machine learning model predictions. [83,84] The specific conditions necessary for reactions to be amenable for DEL synthesis could be a limiting factor for widespread application of ML reactivity prediction models beyond DELs.…”

Modeling receptor flexibility in the structure-based design of KRASG12C inhibitors

“…For example, CovDock [20,21] is a workflow utilizing the Glide docking protocol [22][23][24] and Prime protein structure refinement [25,26]; DOCKovalent is a variation of DOCK [27]. AutoDock [28] natively supports covalent inhibitor docking, as do Cova-lentDock [29] and WIDOCK [30], which are based on AutoDock.…”

“…There have been several reports of using covalent docking successfully in the virtual screening and lead optimalization [27,[30][31][32]. However, to our knowledge, none of these covalent docking methods has the capability to deal with flexible receptors.…”