Mechanistic investigation of SARS-CoV-2 main protease to accelerate design of covalent inhibitors

Abstract

Targeted covalent inhibition represents one possible strategy to block the function of SARS-CoV-2 Main Protease (MPRO), an enzyme that plays a critical role in the replication of the novel SARS-CoV-2. Toward the design of covalent inhibitors, we built a covalent inhibitor dataset using deep learning models followed by high throughput virtual screening of these candidates against MPRO. Two top-ranking inhibitors were selected for mechanistic investigations—one with an activated ester warhead that has a piperazine core and the other with an acrylamide warhead. Specifically, we performed a detailed analysis of the free energetics of covalent inhibition by hybrid quantum mechanics/molecular mechanics simulations. Cleavage of a fragment of the non-structured protein (NSP) from the SARS-CoV-2 genome was also simulated for reference. Simulations show that both candidates form more stable enzyme-inhibitor (E-I) complexes than the chosen NSP. It was found that both the NSP fragment and the activated ester inhibitor react with CYS145 of MPRO in a concerted manner, whereas the acrylamide inhibitor follows a stepwise mechanism. Most importantly, the reversible reaction and the subsequent hydrolysis reaction from E-I complexes are less probable when compared to the reactions with an NSP fragment, showing promise for these candidates to be the base for efficient MPRO inhibitors.

Multiscale In Silico Study of the Mechanism of Activation of the RtcB Ligase by the PTP1B Phosphatase

“…Exchange–correlation density functional theory (DFT) with the Perdew–Burke–Ernzerhof (PBE) generalized gradient approximation and DFT-D3­(BJ) Grimme dispersion correction , with a double-ζ valence plus polarization (DVZP) basis set and Goedecker, Teter, and Hutter (GTH) pseudopotentials was applied to the QM subsystem. This level of theory was successfully employed in studying various enzymatic reactions. − It has been shown that the DFT-D3­(BJ) Grimme dispersion correction provides better results for nonbonded interactions and more clear effects of intramolecular dispersion forces . The MM subsystem was treated by the side chain and backbone-modified AMBER14 force field (ff14SB) and the TIP3P water model .…”

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2

“…Generated molecules were examined for validity uniqueness and novelty. The molecules are then screened based on the synthetic accessibility score, quantitative estimation of drug-likeness, and the partition coefficient (log P ) properties before being used for subsequent docking evaluations . We further examined these molecules for desired binding activity against the target protein with docking and using MD simulations to narrow down top candidates.…”

“…A number of studies have focused on developing antiviral non-covalent inhibitors against M pro based on high-throughput virtual screening (HTVS) using a docking workflow. − However, the design of covalent inhibitors has been less common for the SARS-CoV-2 M pro due to the lack of mechanistic knowledge and electrophilic warhead design strategies. , To this end, we could discover highly selective covalent inhibitors of M pro by prioritizing noncovalent interactions with poorly conserved active site amino acid residues that position the molecule to react with a catalytic Cys145 residue in the active site (Figure A) . In seeking to apply our novel covalent ligand discovery approach based on a deep learning (DL) model to a SARS-CoV-2 protein target, we examined several steps that play a pivotal role in infection and replication, which offered many opportunities for us to apply an automated covalent inhibitor design approach (Figure B).…”

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

“…For this advantage, numerous computational studies have been conducted to elucidate the chemical mechanism underlying the covalent bond formation (Figure ). ,− However, despite this recent interest in the development of covalent drugs, the lack of effective strategies remains a current technical challenge to the widespread development of covalent inhibitors. ,,,,− …”

“…For example, docking can be performed with restraints that position the warheads in proximity to the target residue and predict the docked poses driven by noncovalent interactions. ,,, The approach can then be switched to a QM/MM potential to optimize the docked poses in the presence of a covalent bond between the warhead and the protein. ,,, However, the actual binding of covalent drugs involves reaction transition states, whose barriers influence the time scale of the reaction. Understanding this process is therefore necessary to gain insight into the reactivity of the warheads and the reversibility of the covalently bound drugs. ,,− In this regard, knowledge gained from the study of enzyme catalysis can be applied to identify potential binding poses and subsequent reactions with target proteins. ,,, Furthermore, given the shared principles between enzyme catalysis and covalent drug binding, the multiscale computational approaches developed to study enzyme mechanisms (Figure ) can be effectively applied to understand both covalent and noncovalent drug binding. For a similar reason, rational covalent drug design shares the same challenges as enzyme design.…”