Binding mode characterization of 13b in the monomeric and dimeric states of SARS-CoV-2 main protease using molecular dynamics simulations

An allosteric mechanism for potent inhibition of SARS-CoV-2 main proteinase

The main proteinase (Mpro) of SARS-CoV-2 plays a critical role in cleaving viral polyproteins into functional proteins required for viral replication and assembly, making it a prime drug target for COVID-19. It is well known that noncompetitive inhibition offers potential therapeutic options for treating COVID-19, which can effectively reduce the likelihood of cross-reactivity with other proteins and increase the selectivity of the drug. Therefore, the discovery of allosteric sites of Mpro has both scientific and practical significance. In this study, we explored the binding characteristics and inhibiting process of Mpro activity by two recently reported allosteric inhibitors, pelitinib and AT7519 which were obtained by the X-ray screening experiments, to probe the allosteric mechanism via molecular dynamic (MD) simulations. We found that pelitinib and AT7519 can stably bind to Mpro far from the active site. The binding affinity is estimated to be -24.37 ± 4.14 and - 26.96 ± 4.05 kcal/mol for pelitinib and AT7519, respectively, which is considerably stable compared with orthosteric drugs. Furthermore, the strong binding caused clear changes in the catalytic site of Mpro, thus decreasing the substrate accessibility. The community network analysis also validated that pelitinib and AT7519 strengthened intra- and inter-domain communication of Mpro dimer, resulting in a rigid Mpro, which could negatively impact substrate binding. In summary, our findings provide the detailed working mechanism for the two experimentally observed allosteric sites of Mpro. These allosteric sites greatly enhance the 'druggability' of Mpro and represent attractive targets for the development of new Mpro inhibitors.

Elevated glycosylation of CD36 in platelets is a risk factor for oxLDL-mediated platelet activation in type 2 diabetes

Platelet activation and related cardiovascular complications are the hallmarks of type 2 diabetes (T2D). We investigated the mechanism of platelet activation in T2D using MS-based identification of differentially expressed platelet proteins with a focus on glycosylated forms. Glycosylation is considered one of the common post-translational modifications in T2D, and N/O-linked glycosylation of glycoproteins (GPs)/integrins is known to play crucial roles in platelet activation. Our platelet proteome data revealed elevated levels of GPs GPIbα, GPIIbIIIa, GPIV (CD36), GPV and integrins in T2D patients. T2D platelets had elevated N-linked glycosylation of CD36 at asparagine (Asn)408,417 . Enrichment analysis revealed a close association of glycosylated CD36 with thrombospondin-1, fibrinogen and SERPINA1 in T2D platelets. The glycosylation of CD36 has previously been reported to increase cellular uptake of long-chain fatty acids. Our in silico molecular docking data also showed a favorable binding of cholesterol with glycosylated Asn417 CD36 compared to the non-glycosylated form. We further investigated the CD36:LDL cholesterol axis in T2D. Elevated levels of oxidized-low density lipoprotein (oxLDL) were found to cause significant platelet activation via CD36-mediated stimulation of Lyn-JNK signaling. Sulfo-N-succinimidyl oleate, an inhibitor of CD36, effectively inhibited oxLDL-mediated platelet activation and adhesion in vitro. Our study suggests increased glycosylation of CD36 in T2D platelets as a potential route for oxLDL-mediated platelet activation. The oxLDL:CD36 axis may thus be exploited as a prospective target to develop therapeutics against thrombosis in T2D.

A data-driven machine learning approach for discovering potent LasR inhibitors

The rampant spread of multidrug-resistant Pseudomonas aeruginosa strains severely threatens global health. This severity is compounded against the backdrop of a stagnating antibiotics development pipeline. Moreover, with many promising therapeutics falling short of expectations in clinical trials, targeting the las quorum sensing (QS) system remains an attractive therapeutic strategy to combat P. aeruginosa infection. Thus, our primary goal was to develop a drug prediction algorithm using machine learning to identify potent LasR inhibitors. In this work, we demonstrated using a Multilayer Perceptron (MLP) algorithm boosted with AdaBoostM1 to discriminate between active and inactive LasR inhibitors. The optimal model performance was evaluated using 5-fold cross-validation and test sets. Our best model achieved a 90.7% accuracy in distinguishing active from inactive LasR inhibitors, an area under the Receiver Operating Characteristic Curve value of 0.95, and a Matthews correlation coefficient value of 0.81 when evaluated using test sets. Subsequently, we deployed the model against the Enamine database. The top-ranked compounds were further evaluated for their target engagement activity using molecular docking studies, Molecular Dynamics simulations, MM-GBSA analysis, and Free Energy Landscape analysis. Our data indicate that several of our chosen top hits showed better ligand-binding affinities than naringenin, a competitive LasR inhibitor. Among the six top hits, five of these compounds were predicted to be LasR inhibitors that could be used to treat P. aeruginosa-associated infections. To our knowledge, this study provides the first assessment of using an MLP-based QSAR model for discovering potent LasR inhibitors to attenuate P. aeruginosa infections.

Bench-to-bedside: Innovation of small molecule anti-SARS-CoV-2 drugs in China

The ongoing COVID-19 pandemic has resulted in millions of deaths globally, highlighting the need to develop potent prophylactic and therapeutic strategies against SARS-CoV-2. Small molecule inhibitors (remdesivir, Paxlovid, and molnupiravir) are essential complements to vaccines and play important roles in clinical treatment of SARS-CoV-2. Many advances have been made in development of anti-SARS-CoV-2 inhibitors in China, but progress in discovery and characterization of pharmacological activity, antiviral mechanisms, and clinical efficacy are limited. We review development of small molecule anti-SARS-CoV-2 drugs (azvudine [approved by the NMPA of China on July 25, 2022], VV116 [approved by the NMPA of China on January 29, 2023], FB2001, WPV01, pentarlandir, and cepharanthine) in China and summarize their pharmacological activity, potential mechanisms of action, clinical trials and use, and important milestones in their discovery. The role of structural biology in drug development is also reviewed. Future studies should focus on development of diverse second-generation inhibitors with excellent oral bioavailability, superior plasma half-life, increased antiviral activity against SARS-CoV-2 and its variants, high target specificity, minimal side effects, reduced drug-drug interactions, and improved lung histopathology.

Discovery of non-nucleoside oxindole derivatives as potent inhibitors against dengue RNA-dependent RNA polymerase

A series of thiazole linked Oxindole-5-Sulfonamide (OSA) derivatives were designed as inhibitors of RNA-dependent RNA polymerase (RdRp) activity of Dengue virus. These were synthesized and then evaluated for their efficacy in ex-vivo virus replication assay using human cell lines. Among 20 primary compounds in the series, OSA-15 was identified as a hit. A series of analogues were synthesized by replacing the difluoro benzyl group of OSA-15 with different substituted benzyl groups. The efficacy of OSA-15derivatives was less than that of the parent compound, except OSA-15-17, which has shown improved efficacy than OSA-15. The further optimization was carried out by adding dimethyl (DM) groups to both the sulfonamide and oxindole NH's to produce OSA-15-DM and OSA-15-17-DM. These two compounds were showing no detectable cytotoxicity and the latter was more efficacious. Further, both these compounds were tested for inhibition in all the serotypes of the Dengue virus using an ex-vivo assay. The EC₅₀ of OSA-15-17-DM was observed in a low micromolar range between 2.5 and 5.0 µg/ml. Computation docking and molecular dynamics simulation studies confirmed the binding of identified hits to DENV RdRp. OSA15-17-DM blocks the RNA entrance and elongation site for their biological activity with high binding affinity. Overall, the identified oxindole derivatives are novel compounds that can inhibit Dengue replication, working as non-nucleoside inhibitors (NNI) to explore as anti-viral RdRp activity.

Novel covalent and non-covalent complex-based pharmacophore models of SARS-CoV-2 main protease (Mpro) elucidated by microsecond MD simulations

As the world enters its second year of the pandemic caused by SARS-CoV-2, intense efforts have been directed to develop an effective diagnosis, prevention, and treatment strategies. One promising drug target to design COVID-19 treatments is the SARS-CoV-2 M^pro. To date, a comparative understanding of M^pro dynamic stereoelectronic interactions with either covalent or non-covalent inhibitors (depending on their interaction with a pocket called S1' or oxyanion hole) has not been still achieved. In this study, we seek to fill this knowledge gap using a cascade in silico protocol of docking, molecular dynamics simulations, and MM/PBSA in order to elucidate pharmacophore models for both types of inhibitors. After docking and MD analysis, a set of complex-based pharmacophore models was elucidated for covalent and non-covalent categories making use of the residue bonding point feature. The highest ranked models exhibited ROC-AUC values of 0.93 and 0.73, respectively for each category. Interestingly, we observed that the active site region of M^pro protein-ligand complex undergoes large conformational changes, especially within the S2 and S4 subsites. The results reported in this article may be helpful in virtual screening (VS) campaigns to guide the design and discovery of novel small-molecule therapeutic agents against SARS-CoV-2 M^pro protein.

Conformational Characterization of the Co-Activator Binding Site Revealed the Mechanism to Achieve the Bioactive State of FXR

FXR bioactive states are responsible for the regulation of metabolic pathways, which are modulated by agonists and co-activators. The synergy between agonist binding and ‘co-activator’ recruitment is highly conformationally driven. The characterization of conformational dynamics is essential for mechanistic and therapeutic understanding. To shed light on the conformational ensembles, dynamics, and structural determinants that govern the activation process of FXR, molecular dynamic (MD) simulation is employed. Atomic insights into the ligand binding domain (LBD) of FXR revealed significant differences in inter/intra molecular bonding patterns, leading to structural anomalies in different systems of FXR. The sole presence of an agonist or ‘co-activator’ fails to achieve the essential bioactive conformation of FXR. However, the presence of both establishes the bioactive conformation of FXR as they modulate the internal wiring of key residues that coordinate allosteric structural transitions and their activity. We provide a precise description of critical residue positioning during conformational changes that elucidate the synergy between its binding partners to achieve an FXR activation state. Our study offers insights into the associated modulation occurring in FXR at bound and unbound forms. Thereafter, we also identified hot-spots that are critical to arrest the activation mechanism of FXR that would be helpful for the rational design of its agonists.

Co-crystallization and structure determination: An effective direction for anti-SARS-CoV-2 drug discovery

Safer and more-effective drugs are urgently needed to counter infections with the highly pathogenic SARS-CoV-2, cause of the COVID-19 pandemic. Identification of efficient inhibitors to treat and prevent SARS-CoV-2 infection is a predominant focus. Encouragingly, using X-ray crystal structures of therapeutically relevant drug targets (PLpro, Mpro, RdRp, and S glycoprotein) offers a valuable direction for anti-SARS-CoV-2 drug discovery and lead optimization through direct visualization of interactions. Computational analyses based primarily on MMPBSA calculations have also been proposed for assessing the binding stability of biomolecular structures involving the ligand and receptor. In this study, we focused on state-of-the-art X-ray co-crystal structures of the abovementioned targets complexed with newly identified small-molecule inhibitors (natural products, FDA-approved drugs, candidate drugs, and their analogues) with the assistance of computational analyses to support the precision design and screening of anti-SARS-CoV-2 drugs.