Atomistic-Level Description of the Covalent Inhibition of SARS-CoV-2 Papain-like Protease

From X-ray crystallographic structure to intrinsic thermodynamics of protein-ligand binding using carbonic anhydrase isozymes as a model system

Carbonic anhydrase (CA) was among the first proteins whose X-ray crystal structure was solved to atomic resolution. CA proteins have essentially the same fold and similar active centers that differ in only several amino acids. Primary sulfonamides are well defined, strong and specific binders of CA. However, minor variations in chemical structure can significantly alter their binding properties. Over 1000 sulfonamides have been designed, synthesized and evaluated to understand the correlations between the structure and thermodynamics of their binding to the human CA isozyme family. Compound binding was determined by several binding assays: fluorescence-based thermal shift assay, stopped-flow enzyme activity inhibition assay, isothermal titration calorimetry and competition assay for enzyme expressed on cancer cell surfaces. All assays have advantages and limitations but are necessary for deeper characterization of these protein-ligand interactions. Here, the concept and importance of intrinsic binding thermodynamics is emphasized and the role of structure-thermodynamics correlations for the novel inhibitors of CA IX is discussed - an isozyme that is overexpressed in solid hypoxic tumors, and thus these inhibitors may serve as anticancer drugs. The abundant structural and thermodynamic data are assembled into the Protein-Ligand Binding Database to understand general protein-ligand recognition principles that could be used in drug discovery.

Identification of Phytochemicals from Arabian Peninsula Medicinal Plants as Strong Binders to SARS-CoV-2 Proteases (3CLPro and PLPro) by Molecular Docking and Dynamic Simulation Studies

We provide promising computational (in silico) data on phytochemicals (compounds 1-10) from Arabian Peninsula medicinal plants as strong binders, targeting 3-chymotrypsin-like protease (3CLPro) and papain-like proteases (PLPro) of SARS-CoV-2. Compounds 1-10 followed the Lipinski rules of five (RO5) and ADMET analysis, exhibiting drug-like characters. Non-covalent (reversible) docking of compounds 1-10 demonstrated their binding with the catalytic dyad (CYS145 and HIS41) of 3CLPro and catalytic triad (CYS111, HIS272, and ASP286) of PLPro. Moreover, the implementation of the covalent (irreversible) docking protocol revealed that only compounds 7, 8, and 9 possess covalent warheads, which allowed the formation of the covalent bond with the catalytic dyad (CYS145) in 3CLPro and the catalytic triad (CYS111) in PLPro. Root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and radius of gyration (Rg) analysis from molecular dynamic (MD) simulations revealed that complexation between ligands (compounds 7, 8, and 9) and 3CLPro and PLPro was stable, and there was less deviation of ligands. Overall, the in silico data on the inherent properties of the above phytochemicals unravel the fact that they can act as reversible inhibitors for 3CLPro and PLPro. Moreover, compounds 7, 8, and 9 also showed their novel properties to inhibit dual targets by irreversible inhibition, indicating their effectiveness for possibly developing future drugs against SARS-CoV-2. Nonetheless, to confirm the theoretical findings here, the effectiveness of the above compounds as inhibitors of 3CLPro and PLPro warrants future investigations using suitable in vitro and in vivo tests.

Studies on the selectivity of the SARS-CoV-2 papain-like protease reveal the importance of the P2' proline of the viral polyprotein

The SARS-CoV-2 papain-like protease (PLpro) is an antiviral drug target that catalyzes the hydrolysis of the viral polyproteins pp1a/1ab, so releasing the non-structural proteins (nsps) 1-3 that are essential for the coronavirus lifecycle. The LXGG↓X motif in pp1a/1ab is crucial for recognition and cleavage by PLpro. We describe molecular dynamics, docking, and quantum mechanics/molecular mechanics (QM/MM) calculations to investigate how oligopeptide substrates derived from the viral polyprotein bind to PLpro. The results reveal how the substrate sequence affects the efficiency of PLpro-catalyzed hydrolysis. In particular, a proline at the P2' position promotes catalysis, as validated by residue substitutions and mass spectrometry-based analyses. Analysis of PLpro catalyzed hydrolysis of LXGG motif-containing oligopeptides derived from human proteins suggests that factors beyond the LXGG motif and the presence of a proline residue at P2' contribute to catalytic efficiency, possibly reflecting the promiscuity of PLpro. The results will help in identifying PLpro substrates and guiding inhibitor design.

Targeting SARS-CoV-2 Non-Structural Proteins

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an enveloped respiratory β coronavirus that causes coronavirus disease (COVID-19), leading to a deadly pandemic that has claimed millions of lives worldwide. Like other coronaviruses, the SARS-CoV-2 genome also codes for non-structural proteins (NSPs). These NSPs are found within open reading frame 1a (ORF1a) and open reading frame 1ab (ORF1ab) of the SARS-CoV-2 genome and encode NSP1 to NSP11 and NSP12 to NSP16, respectively. This study aimed to collect the available literature regarding NSP inhibitors. In addition, we searched the natural product database looking for similar structures. The results showed that similar structures could be tested as potential inhibitors of the NSPs.

Unraveling the catalytic mechanism of SARS-CoV-2 papain-like protease with allosteric modulation of C270 mutation using multiscale computational approaches

Papain-like protease (PL^pro) is a promising therapeutic target against SARS-CoV-2, but its restricted S1/S2 subsites pose an obstacle in developing active site-directed inhibitors. We have recently identified C270 as a novel covalent allosteric site for SARS-CoV-2 PL^pro inhibitors. Here we present a theoretical investigation of the proteolysis reaction catalyzed by the wild-type SARS-CoV-2 PL^pro as well as the C270R mutant. Enhanced sampling MD simulations were first performed to explore the influence of C270R mutation on the protease dynamics, and sampled thermodynamically favorable conformations were then submitted to MM/PBSA and QM/MM MD simulations for thorough characterization of the protease-substrate binding and covalent reactions. The disclosed proteolysis mechanism of PL^pro, as characterized by the occurrence of proton transfer from the catalytic C111 to H272 prior to the substrate binding and with deacylation being the rate-determining step of the whole proteolysis process, is not completely identical to that of the 3C-like protease, another key cysteine protease of coronaviruses. The C270R mutation alters the structural dynamics of the BL2 loop that indirectly impairs the catalytic function of H272 and reduces the binding of the substrate with the protease, ultimately showing an inhibitory effect on PL^pro. Together, these results provide a comprehensive understanding at the atomic level of the key aspects of SARS-CoV-2 PL^pro proteolysis, including the catalytic activity allosterically regulated by C270 modification, which is crucial to the follow-up inhibitor design and development.

Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen

SARS-CoV-2 3CL protease is one of the key targets for drug development against COVID-19. Most known SARS-CoV-2 3CL protease inhibitors act by covalently binding to the active site cysteine. Yet, computational screens against this enzyme were mainly focused on non-covalent inhibitor discovery. Here, we developed a deep learning-based stepwise strategy for selective covalent inhibitor screen. We used a deep learning framework that integrated a directed message passing neural network with a feed-forward neural network to construct two different classifiers for either covalent or non-covalent inhibition activity prediction. These two classifiers were trained on the covalent and non-covalent 3CL protease inhibitors dataset, respectively, which achieved high prediction accuracy. We then successively applied the covalent inhibitor model and the non-covalent inhibitor model to screen a chemical library containing compounds with covalent warheads of cysteine. We experimentally tested the inhibition activity of 32 top-ranking compounds and 12 of them were active, among which 6 showed IC₅₀ values less than 12 μM and the strongest one inhibited SARS-CoV-2 3CL protease with an IC₅₀ of 1.4 μM. Further investigation demonstrated that 5 of the 6 active compounds showed typical covalent inhibition behavior with time-dependent activity. These new covalent inhibitors provide novel scaffolds for developing highly active SARS-CoV-2 3CL covalent inhibitors.