Bioactive compounds from Huashi Baidu decoction possess both antiviral and anti-inflammatory effects against COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic is an ongoing global health concern, and effective antiviral reagents are urgently needed. Traditional Chinese medicine theory-driven natural drug research and development (TCMT-NDRD) is a feasible method to address this issue as the traditional Chinese medicine formulae have been shown effective in the treatment of COVID-19. Huashi Baidu decoction (Q-14) is a clinically approved formula for COVID-19 therapy with antiviral and anti-inflammatory effects. Here, an integrative pharmacological strategy was applied to identify the antiviral and anti-inflammatory bioactive compounds from Q-14. Overall, a total of 343 chemical compounds were initially characterized, and 60 prototype compounds in Q-14 were subsequently traced in plasma using ultrahigh-performance liquid chromatography with quadrupole time-of-flight mass spectrometry. Among the 60 compounds, six compounds (magnolol, glycyrrhisoflavone, licoisoflavone A, emodin, echinatin, and quercetin) were identified showing a dose-dependent inhibition effect on the SARS-CoV-2 infection, including two inhibitors (echinatin and quercetin) of the main protease (Mpro), as well as two inhibitors (glycyrrhisoflavone and licoisoflavone A) of the RNA-dependent RNA polymerase (RdRp). Meanwhile, three anti-inflammatory components, including licochalcone B, echinatin, and glycyrrhisoflavone, were identified in a SARS-CoV-2-infected inflammatory cell model. In addition, glycyrrhisoflavone and licoisoflavone A also displayed strong inhibitory activities against cAMP-specific 3',5'-cyclic phosphodiesterase 4 (PDE4). Crystal structures of PDE4 in complex with glycyrrhisoflavone or licoisoflavone A were determined at resolutions of 1.54 Å and 1.65 Å, respectively, and both compounds bind in the active site of PDE4 with similar interactions. These findings will greatly stimulate the study of TCMT-NDRD against COVID-19.

Parthenolide reveals an allosteric mode to inhibit the deISGylation activity of SARS-CoV­2 papain-like protease.

The coronavirus papain-like protease (PLpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for viral polypeptide cleavage and the deISGylation of interferon-stimulated gene 15 (ISG15), which enable it to participate in virus replication and host innate immune pathways. Therefore, PLpro is considered an attractive antiviral drug target. Here, we show that parthenolide, a germacrane sesquiterpene lactone, has SARS-CoV-2 PLpro inhibitory activity. Parthenolide covalently binds to Cys-191 or Cys-194 of the PLpro protein, but not the Cys-111 at the PLpro catalytic site. Mutation of Cys-191 or Cys-194 reduces the activity of PLpro. Molecular docking studies show that parthenolide may also form hydrogen bonds with Lys-192, Thr-193, and Gln-231. Furthermore, parthenolide inhibits the deISGylation but not the deubiquitinating activity of PLpro in vitro. These results reveal that parthenolide inhibits PLpro activity by allosteric regulation.

D3AI-Spike: A deep learning platform for predicting binding affinity between SARS-CoV-2 spike receptor binding domain with multiple amino acid mutations and human angiotensin-converting enzyme 2.

The number of SARS-CoV-2 spike Receptor Binding Domain (RBD) with multiple amino acid mutations is huge due to random mutations and combinatorial explosions, making it almost impossible to experimentally determine their binding affinities to human angiotensin-converting enzyme 2 (hACE2). Although computational prediction is an alternative way, there is still no online platform to predict the mutation effect of RBD on the hACE2 binding affinity until now. In this study, we developed a free online platform based on deep learning models, namely D3AI-Spike, for quickly predicting binding affinity between spike RBD mutants and hACE2. The models based on CNN and CNN-RNN methods have the concordance index of around 0.8. Overall, the test results of the models are in agreement with the experimental data. To further evaluate the prediction power of D3AI-Spike, we predicted and experimentally determined the binding affinity of a VUM (variants under monitoring) variant IHU (B.1.640.2), which has fourteen amino acid substitutions, including N501Y and E484K, and 9 deletions located in the spike protein. The predicted average affinity score for wild-type RBD and IHU to hACE2 are 0.483 and 0.438, while the determined Kaff values are 5.39 ± 0.38 × 107 L/mol and 1.02 ± 0.47 × 107 L/mol, respectively, demonstrating the strong predictive power of D3AI-Spike. We think D3AI-Spike will be helpful to the viral transmission prediction for the new emerging SARS-CoV-2 variants. D3AI-Spike is now available free of charge at https://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-Spike/index.php.

Exploring the Binding Affinity and Mechanism between ACE2 and the Trimers of Delta and Omicron Spike Proteins by Molecular Dynamics Simulation and Bioassay.

Five major variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have emerged and posed challenges in controlling the pandemic. Among them, the current dominant variant, viz., Omicron, has raised serious concerns about its infectiousness and antibody neutralization. However, few studies pay attention to the effect of the mutations on the dynamic interaction network of Omicron S protein trimers binding to the host angiotensin-converting enzyme 2 (ACE2). In this study, we conducted molecular dynamics (MD) simulations and enzyme linked immunosorbent assay (ELISA) to explore the binding strength and mechanism of wild type (WT), Delta, and Omicron S protein trimers to ACE2. The results showed that the binding capacities of both the two variants' S protein trimers to ACE2 are enhanced in varying degrees, indicating possibly higher cell infectiousness. Energy decomposition and protein-protein interaction network analysis suggested that both the mutational and conserved sites make effects on the increase in the overall affinity through a variety of interactions. The experimentally determined KD values by biolayer interferometry (BLI) and the predicted binding free energies of the RBDs of Delta and Omicron to mAb HLX70 revealed that the two variants may have the high risk of immune evasion from the mAb. These results are not only helpful in understanding the binding strength and mechanism of S protein trimer-ACE2 but also beneficial for drug, especially for antibody development.

D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19.

Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit discovery, lead optimization, drug repurposing and elucidation of the mechanism. Virtual screening can improve the hit rate of drug screening to shorten the cycle of drug discovery and development. Therefore, target prediction and virtual screening are of great importance for developing highly effective drugs against COVID-19. Here we present D3AI-CoV, a platform for target prediction and virtual screening for the discovery of anti-COVID-19 drugs. The platform is composed of three newly developed deep learning-based models i.e., MultiDTI, MPNNs-CNN and MPNNs-CNN-R models. To compare the predictive performance of D3AI-CoV with other methods, an external test set, named Test-78, was prepared, which consists of 39 newly published independent active compounds and 39 inactive compounds from DrugBank. For target prediction, the areas under the receiver operating characteristic curves (AUCs) of MultiDTI and MPNNs-CNN models are 0.93 and 0.91, respectively, whereas the AUCs of the other reported approaches range from 0.51 to 0.74. For virtual screening, the hit rate of D3AI-CoV is also better than other methods. D3AI-CoV is available for free as a web application at http://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-CoV/index.php, which can serve as a rapid online tool for predicting potential targets for active compounds and for identifying active molecules against a specific target protein for COVID-19 treatment.

Conserved protein targets for developing pan-coronavirus drugs based on sequence and 3D structure similarity analyses.

There are 7 known human pathogenic coronaviruses, which are HCoV-229E, HCoV-OC43, HCoV-NL63, HCoV-HKU1, MERS-CoV, SARS-CoV and SARS-CoV-2. While SARS-CoV-2 is currently caused a severe epidemic, experts believe that new pathogenic coronavirus would emerge in the future. Therefore, developing broad-spectrum anti-coronavirus drugs is of great significance. In this study, we performed protein sequence and three-dimensional structure analyses for all the 20 virus-encoded proteins across all the 7 coronaviruses, with the purpose to identify highly conserved proteins and binding sites for developing pan-coronavirus drugs. We found that nsp5, nsp10, nsp12, nsp13, nsp14, and nsp16 are highly conserved both in protein sequences (with average identity percentage higher than 52%, average amino acid conservation scores higher than 5.2) and binding pockets (with average amino acid conservation scores higher than 5.8). We also performed the similarity comparison between these 6 proteins and all the human proteins, and found that all the 6 proteins have similarity less than 25%, indicating that the drugs targeting the 6 proteins should have little interference of human protein function. Accordingly, we suggest that nsp5, nsp10, nsp12, nsp13, nsp14, and nsp16 are potential targets for pan-coronavirus drug development.

Clofazimine derivatives as potent broad-spectrum antiviral agents with dual-target mechanism.

Thirty-two clofazimine derivatives, of which twenty-two were new, were synthesized and evaluated for their antiviral effects against both rabies virus and pseudo-typed SARS-CoV-2, taking clofazimine (1) as the lead. Among them, compound 15f bearing 4-methoxy-2-pyridyl at the N5-position showed superior or comparable antiviral activities to lead 1, with the EC50 values of 1.45 µM and 14.6 µM and the SI values of 223 and 6.1, respectively. Compound 15f inhibited rabies and SARS-CoV-2 by targeting G or S protein to block membrane fusion, as well as binding to L protein or nsp13 to inhibit intracellular biosynthesis respectively, and thus synergistically exerted a broad-spectrum antiviral effect. The results provided useful scientific data for the development of clofazimine derivatives into a new class of broad-spectrum antiviral candidates.

Predicting spike protein NTD mutations of SARS-CoV-2 causing immune evasion by molecular dynamics simulations.

The coronavirus disease 2019 (COVID-19) pandemic was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Among all the potential targets studied for developing drugs and antibodies, the spike (S) protein is the most striking one, which is on the surface of the virus. In contrast with the intensively investigated immunodominant receptor-binding domain (RBD) of the protein, little is known about the neutralizing antibody binding mechanisms of the N-terminal domain (NTD), let alone the effects of NTD mutations on antibody binding and thereby the risk of immune evasion. Based on 400 ns molecular dynamics simulation for 11 NTD-antibody complexes together with other computational approaches in this study, we investigated critical residues for NTD-antibody binding and their detailed mechanisms. The results show that 36 residues on the NTD including R246, Y144, K147, Y248, L249 and P251 are critically involved in the direct interaction of the NTD with many monoclonal antibodies (mAbs), indicating that the viruses harboring these residue mutations may have a high risk of immune evasion. Binding free energy calculations and an interaction mechanism study reveal that R246I, which is present in the Beta (B.1.351/501Y.V2) variant, may have various impacts on current NTD antibodies through abolishing the hydrogen bonds and electrostatic interaction with the antibodies or affecting other interface residues. Therefore, special attention should be paid to the mutations of these key residues in future antibody and vaccine design and development.

Exploring the immune evasion of SARS-CoV-2 variant harboring E484K by molecular dynamics simulations.

Although the current coronavirus disease 2019 (COVID-19) vaccines have been used worldwide to halt spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the emergence of new SARS-CoV-2 variants with E484K mutation shows significant resistance to the neutralization of vaccine sera. To better understand the resistant mechanism, we calculated the binding affinities of 26 antibodies to wild-type (WT) spike protein and to the protein harboring E484K mutation, respectively. The results showed that most antibodies (~85%) have weaker binding affinities to the E484K mutated spike protein than to the WT, indicating the high risk of immune evasion of the mutated virus from most of current antibodies. Binding free energy decomposition revealed that the residue E484 forms attraction with most antibodies, while the K484 has repulsion from most antibodies, which should be the main reason of the weaker binding affinities of E484K mutant to most antibodies. Impressively, a monoclonal antibody (mAb) combination was found to have much stronger binding affinity with E484K mutant than WT, which may work well against the mutated virus. Based on binding free energy decomposition, we predicted that the mutation of four more residues on receptor-binding domain (RBD) of spike protein, viz., F490, V483, G485 and S494, may have high risk of immune evasion, which we should pay close attention on during the development of new mAb therapeutics.

Ligand-based approach for predicting drug targets and for virtual screening against COVID-19.

Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing coronavirus disease 2019 (COVID-19). Protein structure-based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by e.g. various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure-based platform for COVID-19, termed as D3Docking in our previous work, we developed in this study a ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses, viz., severe acute respiratory syndrome coronavirus (SARS), Middle East respiratory syndrome coronavirus (MERS), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), human betacoronavirus 2c EMC/2012 (HCoV-EMC), human CoV 229E (HCoV-229E) and feline infectious peritonitis virus (FIPV), some of which have target or mechanism information but some do not. Based on the two-dimensional (2D) and three-dimensional (3D) similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity by using 2D × 3D value as score for drug discovery and target prediction against COVID-19. The database, which will be updated regularly, is available free of charge at https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php.

Study on the influence of operational and management processes of a water reclamation plant since COVID-19 situation.

Reusing treated wastewater can effectively alleviate water shortages and water contamination problems but depends on ensuring the safety of the reclaimed water that is produced. The operating and management conditions for water reclamation plants in China have been changed since the outbreak of the COVID-19 epidemic in China at the end of 2019 to prevent emerging viruses being spread through wastewater treatment processes and the reclaimed water that is produced. Removal of pathogens and trace organic compounds (e.g., pharmaceuticals and personal care products and endocrine disrupting chemicals) in a real water reclamation plant after the start of COVID-19 epidemic was studied. Disinfection byproduct formation caused by chlorine being added to meet disinfection requirements was also assessed. The pathogenic microorganism concentrations in effluent were <2 (most probable number)/L, and the removal rates for most trace organic compounds were >80% when advanced treatments were performed using ozone, ultraviolet light, and chlorine doses of 2 mg/L, 20.5 mJ/cm2, and 2-3 mg/L, respectively. The main disinfection byproduct produced at a chlorine dose of 2 mg/L and a residence time of 1 h was chloroform (at concentrations <15 µg/L). The results indicated that the water reclamation processes with modified conditions gave high pathogen and trace organic compound removal rates and reasonably well-controlled disinfection byproduct concentrations.

Computational Insights into the Conformational Accessibility and Binding Strength of SARS-CoV-2 Spike Protein to Human Angiotensin-Converting Enzyme 2.

The spike protein of SARS-CoV-2 (CoV-2-S) mediates the virus entry into human cells. Experimental studies have shown the stronger binding affinity of the RBD (receptor binding domain) of CoV-2-S to angiotensin-converting enzyme 2 (ACE2) as compared to that of SARS-CoV spike (CoV-S). However, a similar or weaker binding affinity of CoV-2-S compared to that of CoV-S is observed if entire spikes are used in the bioassay. To explore the underlying mechanism, we calculated the binding affinities of the RBDs to ACE2 and simulated the transitions between ACE2-inaccessible and -accessible conformations. We found that the ACE2-accessible angle of CoV-2-S is 52.2° and that the ACE2 binding strength of CoV-2-S RBD is much stronger than that of CoV-S RBD. However, CoV-2-S has much less of an ACE2-accessible conformation and is much more difficult to shift from ACE2-inaccessible to -accessible than CoV-S, making the binding affinity of the entire protein decrease. Further analysis revealed key interactional residues for strong binding and five potential ligand-binding pockets for drug research.

D3Targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19.

A highly effective medicine is urgently required to cure coronavirus disease 2019 (COVID-19). For the purpose, we developed a molecular docking based webserver, namely D3Targets-2019-nCoV, with two functions, one is for predicting drug targets for drugs or active compounds observed from clinic or in vitro/in vivo studies, the other is for identifying lead compounds against potential drug targets via docking. This server has its unique features, (1) the potential target proteins and their different conformations involving in the whole process from virus infection to replication and release were included as many as possible; (2) all the potential ligand-binding sites with volume larger than 200 Å3 on a protein structure were identified for docking; (3) correlation information among some conformations or binding sites was annotated; (4) it is easy to be updated, and is accessible freely to public (https://www.d3pharma.com/D3Targets-2019-nCoV/index.php). Currently, the webserver contains 42 proteins [20 severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) encoded proteins and 22 human proteins involved in virus infection, replication and release] with 69 different conformations/structures and 557 potential ligand-binding pockets in total. With 6 examples, we demonstrated that the webserver should be useful to medicinal chemists, pharmacologists and clinicians for efficiently discovering or developing effective drugs against the SARS-CoV-2 to cure COVID-19.