Discovery of novel inhibitors of SARS-CoV-2 main protease.

Corona Virus Disease 2019 (COVID-19), referred to as 'New Coronary Pneumonia', is a type of acute infectious disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Mpro is one of the main targets for treating COVID-19. The current research on Mpro mainly focuses on the repurposing of old drugs, and there are only a few novel ligands that inhibit Mpro. In this research, we used computational free energy calculation to screen a compound library against Mpro, and discovered four novel compounds with the two best compounds (AG-690/13507628 and AG-690/13507724) having experimental measured IC50 of just under 3 µM and low cell toxicity. Detailed decomposition of the interactions between the inhibitors and Mpro reveals key interacting residues and interactions that determine the activity. The results from this study should provide a basis for further development of anti-SARS-CoV-2 drugs.Communicated by Ramaswamy H. Sarma.

Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening.

Many bioactive peptides demonstrated therapeutic effects over complicated diseases, such as antiviral, antibacterial, anticancer, etc. It is possible to generate a large number of potentially bioactive peptides using deep learning in a manner analogous to the generation of de novo chemical compounds using the acquired bioactive peptides as a training set. Such generative techniques would be significant for drug development since peptides are much easier and cheaper to synthesize than compounds. Despite the limited availability of deep learning-based peptide-generating models, we have built an LSTM model (called LSTM_Pep) to generate de novo peptides and fine-tuned the model to generate de novo peptides with specific prospective therapeutic benefits. Remarkably, the Antimicrobial Peptide Database has been effectively utilized to generate various kinds of potential active de novo peptides. We proposed a pipeline for screening those generated peptides for a given target and used the main protease of SARS-COV-2 as a proof-of-concept. Moreover, we have developed a deep learning-based protein-peptide prediction model (DeepPep) for rapid screening of the generated peptides for the given targets. Together with the generating model, we have demonstrated that iteratively fine-tuning training, generating, and screening peptides for higher-predicted binding affinity peptides can be achieved. Our work sheds light on developing deep learning-based methods and pipelines to effectively generate and obtain bioactive peptides with a specific therapeutic effect and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target.

Molecular Mechanism of the Non-Covalent Orally Targeted SARS-CoV-2 Mpro Inhibitor S-217622 and Computational Assessment of Its Effectiveness against Mainstream Variants.

Convenient and efficient therapeutic agents are urgently needed to block the continued spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, the mechanism for the novel orally targeted SARS-CoV-2 main protease (Mpro) inhibitor S-217622 is revealed through a molecular dynamics simulation. The difference in the movement modes of the S-217622-Mpro complex and apo-Mpro suggested S-217622 could inhibit the motility intensity of Mpro, thus maintaining their stable binding. Subsequent energy calculations showed that the P2 pharmacophore possessed the highest energy contribution among the three pharmacophores of S-217622. Additionally, hot-spot residues H41, M165, C145, E166, and H163 have strong interactions with S-217622. To further investigate the resistance of S-217622 to six mainstream variants, the binding modes of S-217622 with these variants were elucidated. The subtle differences in energy compared to that of the wild type implied that the binding patterns of these systems were similar, and S-217622 still inhibited these variants. We hope this work will provide theoretical insights for optimizing novel targeted Mpro drugs.

An Efficient Modern Strategy to Screen Drug Candidates Targeting RdRp of SARS-CoV-2 With Potentially High Selectivity and Specificity

Desired drug candidates should have both a high potential binding chance and high specificity. Recently, many drug screening strategies have been developed to screen compounds with high possible binding chances or high binding affinity. However, there is still no good solution to detect whether those selected compounds possess high specificity. Here, we developed a reverse DFCNN (Dense Fully Connected Neural Network) and a reverse docking protocol to check a given compound’s ability to bind diversified targets and estimate its specificity with homemade formulas. We used the RNA-dependent RNA polymerase (RdRp) target as a proof-of-concept example to identify drug candidates with high selectivity and high specificity. We first used a previously developed hybrid screening method to find drug candidates from an 8888-size compound database. The hybrid screening method takes advantage of the deep learning-based method, traditional molecular docking, molecular dynamics simulation, and binding free energy calculated by metadynamics, which should be powerful in selecting high binding affinity candidates. Also, we integrated the reverse DFCNN and reversed docking against a diversified 102 proteins to the pipeline for assessing the specificity of those selected candidates, and finally got compounds that have both predicted selectivity and specificity. Among the eight selected candidates, Platycodin D and Tubeimoside III were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC50 values of 619.5 and 265.5 nM, respectively. Our study discovered that Tubeimoside III could inhibit SARS-CoV-2 replication potently for the first time. Furthermore, the underlying mechanisms of Platycodin D and Tubeimoside III inhibiting SARS-CoV-2 are highly possible by blocking the RdRp cavity according to our screening procedure. In addition, the careful analysis predicted common critical residues involved in the binding with active inhibitors Platycodin D and Tubeimoside III, Azithromycin, and Pralatrexate, which hopefully promote the development of non-covalent binding inhibitors against RdRp.

Immune Escape Mechanisms of SARS-CoV-2 Delta and Omicron Variants against Two Monoclonal Antibodies That Received Emergency Use Authorization.

Multiple-site mutated SARS-CoV-2 Delta and Omicron variants may trigger immune escape against existing monoclonal antibodies. Here, molecular dynamics simulations combined with the interaction entropy method reveal the escape mechanism of Delta/Omicron variants to Bamlanivimab/Etesevimab. The result shows the significantly reduced binding affinity of the Omicron variant for both antibodies, due to the introduction of positively charged residues that greatly weaken their electrostatic interactions. Meanwhile, significant structural deflection induces fewer atomic contacts and an unstable binding mode. As for the Delta variant, the reduced binding affinity for Bamlanivimab is owing to the alienation of the receptor-binding domain to the main part of this antibody, and the binding mode of the Delta variant to Etesevimab is similar to that of the wild type, suggesting that Etesevimab could still be effective against the Delta variant. We hope this work will provide timely theoretical insights into developing antibodies to prevalent and possible future variants of SARS-CoV-2.

Plasmonic nanosensors for point-of-care biomarker detection.

Advancement of materials along with their fascinating properties play increasingly important role in facilitating the rapid progress in medicine. An excellent example is the recent development of biosensors based on nanomaterials that induce surface plasmon effect for screening biomarkers of various diseases ranging from cancer to Covid-19. The recent global pandemic re-confirmed the trend of real-time diagnosis in public health to be in point-of-care (POC) settings that can screen interested biomarkers at home, or literally anywhere else, at any time. Plasmonic biosensors, thanks to its versatile designs and extraordinary sensitivities, can be scaled into small and portable devices for POC diagnostic tools. In the meantime, efforts are being made to speed up, simplify and lower the cost of the signal readout process including converting the conventional heavy laboratory instruments into lightweight handheld devices. This article reviews the recent progress on the design of plasmonic nanomaterial-based biosensors for biomarker detection with a perspective of POC applications. After briefly introducing the plasmonic detection working mechanisms and devices, the selected highlights in the field focusing on the technology's design including nanomaterials development, structure assembly, and target applications are presented and analyzed. In parallel, discussions on the sensor's current or potential applicability in POC diagnosis are provided. Finally, challenges and opportunities in plasmonic biosensor for biomarker detection, such as the current Covid-19 pandemic and its testing using plasmonic biosensor and incorporation of machine learning algorithms are discussed.

Mutational Effect of Some Major COVID-19 Variants on Binding of the S Protein to ACE2.

COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has many variants that accelerated the spread of the virus. In this study, we investigated the quantitative effect of some major mutants of the spike protein of SARS-CoV-2 binding to the human angiotensin-converting enzyme 2 (ACE2). These mutations are directly related to the Variant of Concern (VOC) including Alpha, Beta, Gamma, Delta and Omicron. Our calculations show that five major mutations (N501Y, E484K, L452R, T478K and K417N), first reported in Alpha, Beta, Gamma and Delta variants, all increase the binding of the S protein to ACE2 (except K417N), consistent with the experimental findings. We also studied an additional eight mutations of the Omicron variant that are located on the interface of the receptor binding domain (RDB) and have not been reported in other VOCs. Our study showed that most of these mutations (except Y505H and G446S) enhance the binding of the S protein to ACE2. The computational predictions helped explain why the Omicron variant quickly became dominant worldwide. Finally, comparison of several different computational methods for binding free energy calculation of these mutants was made. The alanine scanning method used in the current calculation helped to elucidate the residue-specific interactions responsible for the enhanced binding affinities of the mutants. The results show that the ASGB (alanine scanning with generalized Born) method is an efficient and reliable method for these binding free energy calculations due to mutations.

Encephalitis as a neurological complication of COVID-19: A systematic review and meta-analysis of incidence, outcomes, and predictors.

BACKGROUND AND PURPOSE: Although COVID-19 predominantly affects the respiratory system, recent studies have reported the occurrence of neurological disorders such as stroke in relation to COVID-19 infection. Encephalitis is an inflammatory condition of the brain that has been described as a severe neurological complication of COVID-19. Despite a growing number of reported cases, encephalitis related to COVID-19 infection has not been adequately characterised. To address this gap, this systematic review and meta-analysis aims to describe the incidence, clinical course, and outcomes of patients who suffer from encephalitis as a complication of COVID-19. METHODS: All studies published between 1 November 2019 and 24 October 2020 that reported on patients who developed encephalitis as a complication of COVID-19 were included. Only cases with radiological and/or biochemical evidence of encephalitis were included. RESULTS: In this study, 610 studies were screened and 23 studies reporting findings from 129,008 patients, including 138 with encephalitis, were included. The average time from diagnosis of COVID-19 to onset of encephalitis was 14.5 days (range = 10.8-18.2 days). The average incidence of encephalitis as a complication of COVID-19 was 0.215% (95% confidence interval [CI] = 0.056%-0.441%). The average mortality rate of encephalitis in COVID-19 patients was 13.4% (95% CI = 3.8%-25.9%). These patients also had deranged clinical parameters, including raised serum inflammatory markers and cerebrospinal fluid pleocytosis. CONCLUSIONS: Although encephalitis is an uncommon complication of COVID-19, when present, it results in significant morbidity and mortality. Severely ill COVID-19 patients are at higher risk of suffering from encephalitis as a complication of the infection.

Targeting mechanism for SARS-CoV-2 in silico: interaction and key groups of TMPRSS2 toward four potential drugs.

The global dissemination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has seriously endangered human health. The number of confirmed cases is still increasing; however, treatment options are limited. Transmembrane protease serine 2 (TMPRSS2), as a key protease that primes the binding of SARS-CoV-2 spike protein and angiotensin-converting enzyme 2 (ACE2), has become an attractive target and received widespread attention. Thus, four potential drugs (bromhexine, camostat, gabexate, and nafamostat) were used to explore the mechanism of binding with TMPRSS2 in this work. A 65 ns molecular dynamics simulation was performed three times for each drug-TMPRSS2 system for reliable energy calculation and conformational analysis, of which the simulations of nafamostat-TMPRSS2 systems were further extended to 150 ns three times due to the discovery of two binding modes. Through the results of calculating binding free energy by nine methods, the binding affinity of camostat, gabexate, and nafamostat to TMPRSS2 showed great advantages compared with bromhexine, where the nafamostat was surprisingly found to present two reasonable binding conformations (forward and reverse directions). Two negatively charged amino acids (Asp435 and Glu299) can clamp the two positively charged groups (guanidinium group and amidinium group) in either forward or reverse fashion, and the forward one is more stable than the reverse. In addition, compared with gabexate, the dimethylamino group in camostat forms more van der Waals interactions with surrounding hot-spots His296 and Val280, resulting in a stronger affinity to TMPRSS2. For bromhexine, multiple binding sites are displayed in the binding pocket due to its small molecular structure, and van der Waals interactions play the dominant role in the binding process. In particular, six typical hot-spots were identified in the last three serine protease inhibitor systems, i.e., Asp435, Ser436, Gln438, Trp461, Ser463, and Gly464. The guanidinium groups of the drugs have powerful interactions with adjacent residues due to the formation of more hydrogen bonds, suggesting that this may be the critical site for drug design against TMPRSS2. This work provides valuable molecular insight into these four drug-TMPRSS2 binding mechanisms and is helpful for designing and screening drugs targeting TMPRSS2.

Increased Pace of Aging in COVID-Related Mortality.

Identifying prognostic biomarkers and risk stratification for COVID-19 patients is a challenging necessity. One of the core survival factors is patient age. However, chronological age is often severely biased due to dormant conditions and existing comorbidities. In this retrospective cohort study, we analyzed the data from 5315 COVID-19 patients (1689 lethal cases) admitted to 11 public hospitals in New York City from 1 March 2020 to 1 December. We calculated patients' pace of aging with BloodAge-a deep learning aging clock trained on clinical blood tests. We further constructed survival models to explore the prognostic value of biological age compared to that of chronological age. A COVID-19 score was developed to support a practical patient stratification in a clinical setting. Lethal COVID-19 cases had higher predicted age, compared to non-lethal cases (Δ = 0.8-1.6 years). Increased pace of aging was a significant risk factor of COVID-related mortality (hazard ratio = 1.026 per year, 95% CI = 1.001-1.052). According to our logistic regression model, the pace of aging had a greater impact (adjusted odds ratio = 1.09 ± 0.00, per year) than chronological age (1.04 ± 0.00, per year) on the lethal infection outcome. Our results show that a biological age measure, derived from routine clinical blood tests, adds predictive power to COVID-19 survival models.

Anchor-Locker Binding Mechanism of the Coronavirus Spike Protein to Human ACE2: Insights from Computational Analysis.

COVID-19 has emerged as the most serious international pandemic in early 2020 and the lack of comprehensive knowledge in the recognition and transmission mechanisms of this virus hinders the development of suitable therapeutic strategies. The specific recognition during the binding of the spike glycoprotein (S protein) of coronavirus to the angiotensin-converting enzyme 2 (ACE2) in the host cell is widely considered the first step of infection. However, detailed insights on the underlying mechanism of dynamic recognition and binding of these two proteins remain unknown. In this work, molecular dynamics simulation and binding free energy calculation were carried out to systematically compare and analyze the receptor-binding domain (RBD) of six coronavirus' S proteins. We found that affinity and stability of the RBD from SARS-CoV-2 under the binding state with ACE2 are stronger than those of other coronaviruses. The solvent-accessible surface area (SASA) and binding free energy of different RBD subunits indicate an "anchor-locker" recognition mechanism involved in the binding of the S protein to ACE2. Loop 2 (Y473-F490) acts as an anchor for ACE2 recognition, and Loop 3 (G496-V503) locks ACE2 at the other nonanchoring end. Then, the charged or long-chain residues in the ß-sheet 1 (N450-F456) region reinforce this binding. The proposed binding mechanism was supported by umbrella sampling simulation of the dissociation process. The current computational study provides important theoretical insights for the development of new vaccines against SARS-CoV-2.

Early Intubation and Increased Coronavirus Disease 2019 Mortality: A Propensity Score-Matched Retrospective Cohort Study.

OBJECTIVES: There has been controversy about the timing and indications for intubation and mechanical ventilation in novel coronavirus disease 2019. This study assessed the effect of early intubation and mechanical ventilation on all-cause, inhospital mortality for coronavirus disease 2019 patients. DESIGN: Multicenter retrospective cohort study. SETTING: Eleven municipal hospitals in New York City from March 1, 2020, to December 1, 2020. PATIENTS: Adult patients who tested positive for coronavirus disease 2019 in the emergency department were subsequently admitted. Patients with do-not-intubate orders at admission were excluded. INTERVENTIONS: Intubation within 48 hours of triage and intubation at any point during hospital stay. MEASUREMENTS AND MAIN RESULTS: Data from 7,597 coronavirus disease 2019 patients were included; of these, 1,628 (21%) were intubated overall and 807 (11%) were intubated within 48 hours of triage. After controlling for available confounders, intubation rates for coronavirus disease 2019 patients varied significantly across hospitals and decreased steadily as the pandemic progressed. After nearest neighbor propensity score matching, intubation within 48 hours of triage was associated with higher all-cause mortality (hazard ratio, 1.30 [1.15-1.48]; p < 0.0001), as was intubation at any time point (hazard ratio, 1.62 [1.45-1.80]; p < 0.0001). Among intubated patients, intubation within 48 hours of triage was not significantly associated with differences in mortality (hazard ratio, 1.09 [0.94-1.26]; p = 0.26). These results remained robust to multiple sensitivity analyses. CONCLUSIONS: Intubation within 48 hours of triage, as well as at any time point in the hospital course, was associated with increased mortality in coronavirus disease 2019 patients in this observational study.

Quantitative analysis of ACE2 binding to coronavirus spike proteins: SARS-CoV-2 vs. SARS-CoV and RaTG13.

The global outbreak of the COVID-19 pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Bat virus RaTG13 and SARS-CoV are also members of the coronavirus family and SARS-CoV caused a world-wide pandemic in 2003. SARS-CoV-2, SARS-CoV and RaTG13 bind to angiotensin-converting enzyme 2 (ACE2) through their receptor-binding domain (RBD) of the spike protein. SARS-CoV-2 binds ACE2 with a higher binding affinity than SARS-CoV and RaTG13. Here we performed molecular dynamics simulation of these binding complexes and calculated their binding free energies using a computational alanine scanning method. Our MD simulation and hotspot residue analysis showed that the lower binding affinity of SARS-CoV to ACE2 vs. SARS-CoV-2 to ACE2 can be explained by different hotspot interactions in these two systems. We also found that the lower binding affinity of RaTG13 to ACE2 is mainly due to a mutated residue (D501) which resulted in a less favorable complex formation for binding. We also calculated an important mutation of N501Y in SARS-CoV-2 using both alanine scanning calculation and a thermodynamic integration (TI) method. Both calculations confirmed a significant increase of the binding affinity of the N501Y mutant to ACE2 and explained its molecular mechanism. The present work provides an important theoretical basis for understanding the molecular mechanism in coronavirus spike protein binding to human ACE2.

Inhibition mechanism and hot-spot prediction of nine potential drugs for SARS-CoV-2 Mpro by large-scale molecular dynamic simulations combined with accurate binding free energy calculations.

Coronavirus disease 2019 (COVID-19), which is caused by a new coronavirus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is spreading around the world. However, a universally effective treatment regimen has not been developed to date. The main protease (Mpro), a key enzyme of SARS-CoV-2, plays a crucial role in the replication and transcription of this virus in cells and has become the ideal target for rational antiviral drug design. In this study, we performed molecular dynamics simulations three times for these complexes of Mpro (monomeric and dimeric) and nine potential drugs that have a certain effect on the treatment of COVID-19 to explore their binding mechanism. In addition, a total of 12 methods for calculating binding free energy were employed to determine the optimal drug. Ritonavir, Arbidol, and Chloroquine consistently showed an outstanding binding ability to monomeric Mpro under various methods. Ritonavir, Arbidol, and Saquinavir presented the best performance when binding to a dimer, which was independent of the protonated state of Hie41 (protonated at Nε) and Hid41 (protonated at Nδ), and these findings suggest that Chloroquine may not effectively inhibit the activity of dimeric Mproin vivo. Furthermore, three common hot-spot residues of Met165, Hie41, and Gln189 of monomeric Mpro systems dominated the binding of Ritonavir, Arbidol, and Chloroquine. In dimeric Mpro, Gln189, Met165, and Met49 contributed significantly to binding with Ritonavir, Arbidol, and Saquinavir; therefore, Gln189 and Met165 might serve as the focus in the discovery and development of anti-COVID-19 drugs. In addition, the van der Waals interaction played a significant role in the binding process, and the benzene ring of the drugs showed an apparent inhibitory effect on the normal function of Mpro. The binding cavity had great flexibility to accommodate these different drugs. The results would be notably helpful for enabling a detailed understanding of the binding mechanisms for these important drug-Mpro interactions and provide valuable guidance for the design of potent inhibitors.

Diagnostic performance of COVID-19 serological assays during early infection: A systematic review and meta-analysis of 11 516 samples.

OBJECTIVE: The use of coronavirus disease 2019 (COVID-19) serological testing to diagnose acute infection or determine population seroprevalence relies on understanding assay accuracy during early infection. We aimed to evaluate the diagnostic performance of serological testing in COVID-19 by providing summary sensitivity and specificity estimates with time from symptom onset. METHODS: A systematic search of Ovid MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL) and PubMed was performed up to May 13, 2020. All English language, original peer-reviewed publications reporting the diagnostic performance of serological testing vis-à-vis virologically confirmed SARS-CoV-2 infection were included. RESULTS: Our search yielded 599 unique publications. A total of 39 publications reporting 11 516 samples from 8872 human participants met eligibility criteria for inclusion in our study. Pooled percentages of IgM and IgG seroconversion by Day 7, 14, 21, 28 and after Day 28 were 37.5%, 73.3%, 81.3%, 72.3% and 73.3%, and 35.4%, 80.6%, 93.3%, 84.4% and 98.9%, respectively. By Day 21, summary estimate of IgM sensitivity was 0.872 (95% CI: 0.784-0.928) and specificity 0.973 (95% CI: 0.938-0.988), while IgG sensitivity was 0.913 (95% CI: 0.823-0.959) and specificity 0.960 (95% CI: 0.919-0.980). On meta-regression, IgM and IgG test accuracy was significantly higher at Day 14 using enzyme-linked immunosorbent assay (ELISA) compared to other methods. CONCLUSIONS: Serological assays offer imperfect sensitivity for the diagnosis of acute SARS-CoV-2 infection. Estimates of population seroprevalence during or shortly after an outbreak will need to adjust for the delay between infection, symptom onset and seroconversion.

Stroke as a Neurological Complication of COVID-19: A Systematic Review and Meta-Analysis of Incidence, Outcomes and Predictors.

INTRODUCTION: COVID-19 is a multi-system infection which predominantly affects the respiratory system, but also causes systemic inflammation, endothelialitis and thrombosis. The consequences of this include renal dysfunction, hepatitis and stroke. In this systematic review, we aimed to evaluate the epidemiology, clinical course, and outcomes of patients who suffer from stroke as a complication of COVID-19. METHODS: We conducted a systematic review of all studies published between November 1, 2019 and July 8, 2020 which reported on patients who suffered from stroke as a complication of COVID-19. RESULTS: 326 studies were screened, and 30 studies reporting findings from 55,176 patients including 899 with stroke were included. The average age of patients who suffered from stroke as a complication of COVID-19 was 65.5 (Range: 40.4-76.4 years). The average incidence of stroke as a complication of COVID-19 was 1.74% (95% CI: 1.09% to 2.51%). The average mortality of stroke in COVID-19 patients was 31.76% (95% CI: 17.77% to 47.31%). These patients also had deranged clinical parameters including deranged coagulation profiles, liver function tests, and full blood counts. CONCLUSION: Although stroke is an uncommon complication of COVID-19, when present, it often results in significant morbidity and mortality. In COVID-19 patients, stroke was associated with older age, comorbidities, and severe illness.

Study of Viral Filtration Performance of Residential HVAC Filters

Researchers recently carried out an experimental study to understand the efficacy and effectiveness of residential HVAC filters at removing airborne virus particles in the airstream. It concluded that high-efficiency residential HVAC filters were effective at capturing airborne virus particles in the air passing through the filter. Studies have shown that droplets generated by coughing and sneezing can contain bacteria and virus, which covers a very wide particle size range.1 Small droplets can suspend in the air, then dry to form fine particles, which can stay in the air for hours. SARS-CoV-2, which is the virus responsible for COVID-19, is known to transmit through droplets, contact and aerosols. Recent research2 discovered that SARS-CoV-2 can be widely distributed in the air and on object surfaces. Studies have shown that droplets generated by coughing and sneezing can contain bacteria and virus, which covers a very wide particle size range. Small droplets can suspend in the air, then dry to form fine particles, which can stay in the air for hours. SARS-CoV-2, which is the virus responsible for COVID-19, is known to transmit through droplets, contact and aerosols.