Electrical Detection Assay Based on Programmable Nucleic Acid Probe for Efficient Single-Nucleotide Polymorphism Identification.

The large-scale pandemic and fast evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have triggered an urgent need for an efficient and sensitive on-site nucleic acid testing method with single-nucleotide polymorphism (SNP) identification capability. Here, we report a multiplexed electrical detection assay based on a paperclip-shaped nucleic acid probe (PNprobe) functionalized field-effect transistor (FET) biosensor for highly sensitive and specific detection and discrimination of SARS-CoV-2 variants. The three-stem structure of the PNprobe significantly amplifies the thermodynamic stability difference between variant RNAs that differ in a single-nucleotide mutation. With the assistance of combinatorial FET detection channels, the assay realizes simultaneously the detection and identification of key mutations of seven SARS-CoV-2 variants, including nucleotide substitutions and deletions at single-nucleotide resolution within 15 min. For 70 simulated throat swab samples, the multiplexed electrical detection assay shows an identification accuracy of 97.1% for the discrimination of SARS-CoV-2 variants. Our designed multiplexed electrical detection assay with SNP identification capability provides an efficient tool to achieve scalable pandemic screening.

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.

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.

Alteration of Autonomic Nervous System Is Associated With Severity and Outcomes in Patients With COVID-19.

BACKGROUND: Previous studies suggest that coronavirus disease 2019 (COVID-19) is a systemic infection involving multiple systems, and may cause autonomic dysfunction. OBJECTIVE: To assess autonomic function and relate the findings to the severity and outcomes in COVID-19 patients. METHODS: We included consecutive patients with COVID-19 admitted to the 21st COVID-19 Department of the east campus of Renmin Hospital of Wuhan University from February 6 to March 7, 2020. Clinical data were collected. Heart rate variability (HRV), N-terminal pro-B-type natriuretic peptide (NT-proBNP), D-dimer, and lymphocytes and subsets counts were analysed at two time points: nucleic-acid test positive and negative. Psychological symptoms were assessed after discharge. RESULTS: All patients were divided into a mild group (13) and a severe group (21). The latter was further divided into two categories according to the trend of HRV. Severe patients had a significantly lower standard deviation of the RR intervals (SDNN) (P < 0.001), standard deviation of the averages of NN intervals (SDANN) (P < 0.001), and a higher ratio of low- to high-frequency power (LF/HF) (P = 0.016). Linear correlations were shown among SDNN, SDANN, LF/HF, and laboratory indices (P < 0.05). Immune function, D-dimer, and NT-proBNP showed a consistent trend with HRV in severe patients (P < 0.05), and severe patients without improved HRV parameters needed a longer time to clear the virus and recover (P < 0.05). CONCLUSION: HRV was associated with the severity of COVID-19. The changing trend of HRV was related to the prognosis, indicating that HRV measurements can be used as a non-invasive predictor for clinical outcome.