Preventive and therapeutic benefits of nelfinavir in rhesus macaques and human beings infected with SARS-CoV-2.

Effective drugs with broad spectrum safety profile to all people are highly expected to combat COVID-19 caused by SARS-CoV-2. Here we report that nelfinavir, an FDA approved drug for the treatment of HIV infection, is effective against SARS-CoV-2 and COVID-19. Preincubation of nelfinavir could inhibit the activity of the main protease of the SARS-CoV-2 (IC50 = 8.26 µM), while its antiviral activity in Vero E6 cells against a clinical isolate of SARS-CoV-2 was determined to be 2.93 µM (EC50). In comparison with vehicle-treated animals, rhesus macaque prophylactically treated with nelfinavir had significantly lower temperature and significantly reduced virus loads in the nasal and anal swabs of the animals. At necropsy, nelfinavir-treated animals had a significant reduction of the viral replication in the lungs by nearly three orders of magnitude. A prospective clinic study with 37 enrolled treatment-naive patients at Shanghai Public Health Clinical Center, which were randomized (1:1) to nelfinavir and control groups, showed that the nelfinavir treatment could shorten the duration of viral shedding by 5.5 days (9.0 vs. 14.5 days, P = 0.055) and the duration of fever time by 3.8 days (2.8 vs. 6.6 days, P = 0.014) in mild/moderate COVID-19 patients. The antiviral efficiency and clinical benefits in rhesus macaque model and in COVID-19 patients, together with its well-established good safety profile in almost all ages and during pregnancy, indicated that nelfinavir is a highly promising medication with the potential of preventative effect for the treatment of COVID-19.

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.

Chemo- and Site-Selective Lysine Modification of Peptides and Proteins under Native Conditions Using the Water-Soluble Zolinium.

Site-selective lysine modification of peptides and proteins in aqueous solutions or in living cells is still a big challenge today. Here, we report a novel strategy to selectively quinolylate lysine residues of peptides and proteins under native conditions without any catalysts using our newly developed water-soluble zoliniums. The zoliniums could site-selectively quinolylate K350 of bovine serum albumin and inactivate SARS-CoV-2 3CLpro via covalently modifying two highly conserved lysine residues (K5 and K61). In living HepG2 cells, it was demonstrated that the simple zoliniums (5b and 5B) could quinolylate protein lysine residues mainly in the nucleus, cytosol, and cytoplasm, while the zolinium-fluorophore hybrid (8) showed specific lysosome-imaging ability. The specific chemoselectivity of the zoliniums for lysine was validated by a mixture of eight different amino acids, different peptides bearing potential reactive residues, and quantum chemistry calculations. This study offers a new way to design and develop lysine-targeted covalent ligands for specific application.

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.

CoVac501, a self-adjuvanting peptide vaccine conjugated with TLR7 agonists, against SARS-CoV-2 induces protective immunity.

Safe, effective, and economical vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are needed to achieve adequate herd immunity and end the pandemic. We constructed a novel SARS-CoV-2 vaccine, CoVac501, which is a self-adjuvanting peptide vaccine conjugated with Toll-like receptor 7 (TLR7) agonists. The vaccine contains immunodominant peptides screened from the receptor-binding domain (RBD) and is fully chemically synthesized. It has been formulated in an optimized nanoemulsion formulation and is stable at 40 °C for 1 month. In non-human primates (NHPs), CoVac501 elicited high and persistent titers of protective neutralizing antibodies against multiple RBD mutations, SARS-CoV-2 original strain, and variants (B.1.1.7 and B.1.617.2). Specific peptides booster immunization against the B.1.351 variant has also been shown to be effective in improving protection against B.1.351. Meanwhile, CoVac501 elicited the increase of memory T cells, antigen-specific CD8+ T-cell responses, and Th1-biased CD4+ T-cell immune responses in NHPs. Notably, at an extremely high SARS-CoV-2 challenge dose of 1 × 107 TCID50, CoVac501 provided near-complete protection for the upper and lower respiratory tracts of cynomolgus macaques.

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.

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.