In Silico Optimization of SARS-CoV-2 Spike Specific Nanobodies.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide, caused a global pandemic, and killed millions of people. The spike protein embedded in the viral membrane is essential for recognizing human receptors and invading host cells. Many nanobodies have been designed to block the interaction between spike and other proteins. However, the constantly emerging viral variants limit the effectiveness of these therapeutic nanobodies. Therefore, it is necessary to find a prospective antibody designing and optimization approach to deal with existing or future viral variants. METHODS: We attempted to optimize nanobody sequences based on the understanding of molecular details by using computational approaches. First, we employed a coarse-grained (CG) model to learn the energetic mechanism of the spike protein activation. Next, we analyzed the binding modes of several representative nanobodies with the spike protein and identified the key residues on their interfaces. Then, we performed saturated mutagenesis of these key residue sites and employed the CG model to calculate the binding energies. RESULTS: Based on analysis of the folding energy of the angiotensin-converting enzyme 2 (ACE2) -spike complex, we constructed a detailed free energy profile of the activation process of the spike protein which provided a clear mechanistic explanation. In addition, by analyzing the results of binding free energy changes following mutations, we determined how the mutations can improve the complementarity with the nanobodies on spike protein. Then we chose 7KSG nanobody as a template for further optimization and designed four potent nanobodies. Finally, based on the results of the single-site saturated mutagenesis in complementarity determining regions (CDRs), combinations of mutations were performed. We designed four novel, potent nanobodies, all exhibiting higher binding affinity to the spike protein than the original ones. CONCLUSIONS: These results provide a molecular basis for the interactions between spike protein and antibodies and promote the development of new specific neutralizing nanobodies.

A systematic study on the binding affinity of SARS-CoV-2 spike protein to antibodies.

The COVID-19 pandemic has caused a worldwide health crisis and economic recession. Effective prevention and treatment methods are urgently required to control the pandemic. However, the emergence of novel SARS-CoV-2 variants challenges the effectiveness of currently available vaccines and therapeutic antibodies. In this study, through the assessment of binding free energies, we analyzed the mutational effects on the binding affinity of the coronavirus spike protein to neutralizing antibodies, patient-derived antibodies, and artificially designed antibody mimics. We designed a scoring method to assess the immune evasion ability of viral variants. We also evaluated the differences between several targeting sites on the spike protein of antibodies. The results presented herein might prove helpful in the development of more effective therapies in the future.

Stunting among kindergarten children in China in the context of COVID-19: A cross-sectional study

Background The impact of COVID-19 has most likely increased the prevalence of stunting. The study aimed to determine the prevalence of stunting among kindergarten children in the context of coronavirus disease 2019 (COVID-19) in Longgang District, Shenzhen, China, and its risk factors. Methods A cross-sectional study was conducted to identify children from 11 sub districts of 481 kindergartens in the Longgang District of Shenzhen City from May to July 2021. In the context of COVID-19, an online survey was conducted to gather demographic information, height, birth information, and lifestyle. The prevalence of stunting was calculated, and the risk factors were analyzed using binary logistic regression with three stepwise models. Results A total of 118,404 subjects were included from May to July 2021, with a response and questionnaire effective rates of 85.75% and 95.03%, respectively. The prevalence of stunting and severe stunting were 3.3% and 0.8%, respectively. Model 3 showed that risk factors for stunting were male sex [odds ratio (OR) = 1.07], low birth weight (OR = 2.02), insufficient sleep time (OR = 1.08), less food intake than their peers (OR = 1.66), slower eating than their peers (OR = 1.16), accompanied by grandparents alone or non-lineal relatives (reference: parents accompanying) (OR = 1.23, 1.51), and children induced to eat (OR = 1.17). Protective factors included only-child status (OR = 0.66), reported high activity (OR = 0.37, 0.26, 0.23), parents with high education levels (father: OR = 0.87, 0.69;mother: OR = 0.69, 0.58), high monthly income per capita of the family (OR = 0.88, 0.74, 0.68), and allowing children to make food choices (OR = 0.82). Conclusion The stunting rate of children in kindergartens in Longgang District is 3.3%, close to the level of developed countries but higher than the average level of developed cities in China. The relatively high stunting rate in children under 3 years old in 2021 may be associated with the influence of COVID-19. Appropriate policies should be formulated for individuals and families with children to help children establish good living habits and reduce stunting.

Worsening sleep predicts lower life space mobility during the onset of the COVID-19 pandemic.

BACKGROUND: Poor sleep health is an understudied yet potentially modifiable risk factor for reduced life space mobility (LSM), defined as one's habitual movement throughout a community. The objective of this study was to determine whether recalled changes in sleep traits (e.g., sleep quality, refreshing sleep, sleep problems, and difficulty falling asleep) because of the COVID-19 pandemic were associated with LSM in older adults. METHODS: Data were obtained from a University of Florida-administered study conducted in May and June of 2020 (n = 923). Linear regression models were used to assess the impact of COVID-related change in sleep traits with summary scores from the Life Space Assessment. Analyses were adjusted for demographic, mental, and physical health characteristics, COVID-related avoidant behaviors, and pre-COVID sleep ratings. RESULTS: In unadjusted models, reporting that any sleep trait got "a lot worse" or "a little worse" was associated with a decrease in LSM (all p < 0.05). Results were attenuated when accounting for demographic, mental, and physical health characteristics. In fully adjusted models, reporting that problems with sleep got "a lot worse" or that refreshing sleep got "a little worse" was associated with a lower standardized LSM score (ß = -0.38, 95% CI: -0.74, -0.01, and ß = -0.19, 95% CI: -0.37, -0.00, respectively). CONCLUSIONS: While additional research is needed in diverse people and environments, the results demonstrate an association between sleep traits that worsen in response to a health threat and reduced LSM. This finding suggests that interventions that focus on maintaining sleep health in times of heightened stress could preserve LSM.

Application of Coarse-Grained (CG) Models to Explore Conformational Pathway of Large-Scale Protein Machines.

Protein machines are clusters of protein assemblies that function in order to control the transfer of matter and energy in cells. For a specific protein machine, its working mechanisms are not only determined by the static crystal structures, but also related to the conformational transition dynamics and the corresponding energy profiles. With the rapid development of crystallographic techniques, the spatial scale of resolved structures is reaching up to thousands of residues, and the concomitant conformational changes become more and more complicated, posing a great challenge for computational biology research. Previously, a coarse-grained (CG) model aiming at conformational free energy evaluation was developed and showed excellent ability to reproduce the energy profiles by accurate electrostatic interaction calculations. In this study, we extended the application of the CG model to a series of large-scale protein machine systems. The spike protein trimer of SARS-CoV-2, ATP citrate lyase (ACLY) tetramer, and P4-ATPases systems were carefully studied and discussed as examples. It is indicated that the CG model is effective to depict the energy profiles of the conformational pathway between two endpoint structures, especially for large-scale systems. Both the energy change and energy barrier between endpoint structures provide reasonable mechanism explanations for the associated biological processes, including the opening of receptor binding domain (RBD) of spike protein, the phospholipid transportation of P4-ATPase, and the loop translocation of ACLY. Taken together, the CG model provides a suitable alternative in mechanistic studies related to conformational change in large-scale protein machines.

Hydrophilic rhodamine B-loaded / boronic acid-modified graphene oxide nanocomposite as a substitute of enzyme-labeled second antibody for ultrasensitive detection of antibodies.

Enzyme-labeled secondary antibody is often used to amplify the output signal in the process of antibody detection. However, its preparation process is complex and time-consuming. Herein, we fabricated an innovative hydrophilic rhodamine B-loaded / boronic acid-modified graphene oxide (HRBGO) nanocomposite, used as a substitute of enzyme-labeled second antibody. The synthetic HRBGO was loaded with generous rhodamine B and modified with boronic acid. Therefore, the HRBGO could selectively label the carbohydrate chains of Fc fragment of primary antibody through specific boronate affinity recognition, and then perform signal output and amplification by releasing rhodamine B. To verify the practicability of HRBGO, trastuzumab as a humanized monoclonal antibody targeting human epidermal growth factor receptor-2 (HER2) was selected as model antibody. A glycosylation site-blocked / HER2-immobilized magnetic nanoparticles (GHMN) was also prepared for selectively capturing trastuzumab from complex samples via specific immunoaffinity. Because the glycosylation sites of HER2 can also be labeled with the HRBGO by boronate affinity recognition, these sites were blocked by a masking agent to minimize the background signal. For specific and ultrasensitive detection of trastuzumab, the integration of GHMN and HRBGO was proposed and optimized in detail. Trastuzumab detection based on HRBGO consisted of three steps: specific capture, selective labeling, and output signal. The proposed strategy provided ultrahigh sensitivity with limit of detection of 0.35 fg mL-1 and was successfully applied in the detection of trastuzumab in spiked serum sample with recovery and relative standard deviation in the range of 98.7-103.8% and 3.8-6.0%, respectively. To assess universal applicability, the HRBGO was also successfully used for the determination of anti-SARS-COV2 RBD antibody in human serum sample.

Predicting Mutational Effects on Receptor Binding of the Spike Protein of SARS-CoV-2 Variants.

The pandemic caused by SARS-CoV-2 has cost millions of lives and tremendous social/financial loss. The virus continues to evolve and mutate. In particular, the recently emerged "UK", "South Africa", and Delta variants show higher infectivity and spreading speed. Thus, the relationship between the mutations of certain amino acids and the spreading speed of the virus is a problem of great importance. In this respect, understanding the mutational mechanism is crucial for surveillance and prediction of future mutations as well as antibody/vaccine development. In this work, we used a coarse-grained model (that was used previously in predicting the importance of mutations of N501) to calculate the free energy change of various types of single-site or combined-site mutations. This was done for the UK, South Africa, and Delta mutants. We investigated the underlying mechanisms of the binding affinity changes for mutations at different spike protein domains of SARS-CoV-2 and provided the energy basis for the resistance of the E484 mutant to the antibody m396. Other potential mutation sites were also predicted. Furthermore, the in silico predictions were assessed by functional experiments. The results establish that the faster spreading of recently observed mutants is strongly correlated with the binding-affinity enhancement between virus and human receptor as well as with the reduction of the binding to the m396 antibody. Significantly, the current approach offers a way to predict new variants and to assess the effectiveness of different antibodies toward such variants.

Using Smartwatches to Detect Face Touching.

Frequent spontaneous facial self-touches, predominantly during outbreaks, have the theoretical potential to be a mechanism of contracting and transmitting diseases. Despite the recent advent of vaccines, behavioral approaches remain an integral part of reducing the spread of COVID-19 and other respiratory illnesses. The aim of this study was to utilize the functionality and the spread of smartwatches to develop a smartwatch application to identify motion signatures that are mapped accurately to face touching. Participants (n = 10, five women, aged 20-83) performed 10 physical activities classified into face touching (FT) and non-face touching (NFT) categories in a standardized laboratory setting. We developed a smartwatch application on Samsung Galaxy Watch to collect raw accelerometer data from participants. Data features were extracted from consecutive non-overlapping windows varying from 2 to 16 s. We examined the performance of state-of-the-art machine learning methods on face-touching movement recognition (FT vs. NFT) and individual activity recognition (IAR): logistic regression, support vector machine, decision trees, and random forest. While all machine learning models were accurate in recognizing FT categories, logistic regression achieved the best performance across all metrics (accuracy: 0.93 ± 0.08, recall: 0.89 ± 0.16, precision: 0.93 ± 0.08, F1-score: 0.90 ± 0.11, AUC: 0.95 ± 0.07) at the window size of 5 s. IAR models resulted in lower performance, where the random forest classifier achieved the best performance across all metrics (accuracy: 0.70 ± 0.14, recall: 0.70 ± 0.14, precision: 0.70 ± 0.16, F1-score: 0.67 ± 0.15) at the window size of 9 s. In conclusion, wearable devices, powered by machine learning, are effective in detecting facial touches. This is highly significant during respiratory infection outbreaks as it has the potential to limit face touching as a transmission vector.

Review on the potential action mechanisms of Chinese medicines in treating Coronavirus Disease 2019 (COVID-19).

The Coronavirus Disease 2019 (COVID-19) has been declared as a global pandemic, but specific medicines and vaccines are still being developed. In China, interventional therapies with traditional Chinese medicine for COVID-19 have achieved significant clinical efficacies, but the underlying pharmacological mechanisms are still unclear. This article reviewed the etiology of COVID-19 and clinical efficacy. Both network pharmacological study and literature search were used to demonstrate the possible action mechanisms of Chinese medicines in treating COVID-19. We found that Chinese medicines played the role of antivirus, anti-inflammation and immunoregulation, and target organs protection in the management of COVID-19 by multiple components acting on multiple targets at multiple pathways. AEC2 and 3CL protein could be the direct targets for inhibiting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Quercetin, kaempferol, luteolin, isorhamnetin, baicalein, naringenin, and wogonin could be the main active ingredients of Chinese medicines for the management of COVID-19 by targeting on AEC2 and 3CL protein and inhibiting inflammatory mediators, regulating immunity, and eliminating free radicals through COX-2, CASP3, IL-6, MAPK1, MAPK14, MAPK8, and REAL in the signaling pathways of IL-17, arachidonic acid, HIF-1, NF-κB, Ras, and TNF. This study may provide meaningful and useful information on further research to investigate the action mechanisms of Chinese medicines against SARS-CoV-2 and also provide a basis for sharing the "China scheme" for COVID-19 treatment.

Chemical composition and pharmacological mechanism of Qingfei Paidu Decoction and Ma Xing Shi Gan Decoction against Coronavirus Disease 2019 (COVID-19): In silico and experimental study.

The Coronavirus Disease 2019 (COVID-19) pandemic has become a huge threaten to global health, which raise urgent demand of developing efficient therapeutic strategy. The aim of the present study is to dissect the chemical composition and the pharmacological mechanism of Qingfei Paidu Decoction (QFPD), a clinically used Chinese medicine for treating COVID-19 patients in China. Through comprehensive analysis by liquid chromatography coupled with high resolution mass spectrometry (MS), a total of 129 compounds of QFPD were putatively identified. We also constructed molecular networking of mass spectrometry data to classify these compounds into 14 main clusters, in which exhibited specific patterns of flavonoids (45 %), glycosides (15 %), carboxylic acids (10 %), and saponins (5 %). The target network model of QFPD, established by predicting and collecting the targets of identified compounds, indicated a pivotal role of Ma Xing Shi Gan Decoction (MXSG) in the therapeutic efficacy of QFPD. Supportively, through transcriptomic analysis of gene expression after MXSG administration in rat model of LPS-induced pneumonia, the thrombin and Toll-like receptor (TLR) signaling pathway were suggested to be essential pathways for MXSG mediated anti-inflammatory effects. Besides, changes in content of major compounds in MXSG during decoction were found by the chemical analysis. We also validate that one major compound in MXSG, i.e. glycyrrhizic acid, inhibited TLR agonists induced IL-6 production in macrophage. In conclusion, the integration of in silico and experimental results indicated that the therapeutic effects of QFPD against COVID-19 may be attributed to the anti-inflammatory effects of MXSG, which supports the rationality of the compatibility of TCM.

Deciphering the Pharmacological Mechanisms of Ma Xing Shi Gan Decoction against COVID-19 through Integrating Network Pharmacology and Experimental Exploration.

The outbreak of new infectious pneumonia caused by SARS-CoV-2 has posed a significant threat to public health, but specific medicines and vaccines are still being developed. Traditional Chinese medicine (TCM) has thousands of years of experience in facing the epidemic disease, such as influenza and viral pneumonia. In this study, we revealed the efficacy and pharmacological mechanism of Ma Xing Shi Gan (MXSG) Decoction against COVID-19. First, we used liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) to analyze the chemical components in MXSG and identified a total of 97 components from MXSG. Then, the intervention pathway of MXSG based on these components was analyzed with network pharmacology, and it was found that the pathways related to the virus infection process were enriched in some of MXSG component targets. Simultaneously, through literature research, it was preliminarily determined that MXSG, which is an essential prescription for treating COVID-19, shared the feature of antiviral, improving clinical symptoms, regulating immune inflammation, and inhibiting lung injury. The regulatory mechanisms associated with its treatment of COVID-19 were proposed. That MXSG might directly inhibit the adsorption and replication of SARS-CoV-2 at the viral entry step. Besides, MXSG might play a critical role in inflammation and immune regulatory, that is, to prevent cytokine storm and relieve lung injury through toll-like receptors signaling pathway. Next, in this study, the regulatory effect of MXSG on inflammatory lung injury was validated through transcriptome results. In summary, MXSG is a relatively active and safe treatment for influenza and viral pneumonia, and its therapeutic effect may be attributed to its antiviral and anti-inflammatory effects.

Impact of cardiovascular and metabolic diseases on the severity of COVID-19: a systematic review and meta-analysis.

We examined the effects of coronary heart disease (CHD), hypertension and diabetes on the development of severe COVID-19. We performed a comprehensive, systematic literature search for studies published between December 2019 and July 5, 2020 in five databases. The prevalence of severe COVID-19 in patients with CHD, hypertension and diabetes was evaluated through a meta-analysis. Thirty-five articles with 8,170 patients were included, and all the available studies were case series. The pooled odds ratio for the development of severe COVID-19 was 3.21 for patients with CHD (fixed-effects model, 95% CI: 2.58-3.99), 2.27 for patients with hypertension (random-effects model, 95% CI: 1.79-2.90) and 2.34 for patients with diabetes (random-effects model, 95% CI: 1.79-3.05). The heterogeneity of the studies was moderate for the effect of CHD on COVID-19 severity, but was high for the effects of diabetes and hypertension. Funnel plots and Egger's tests revealed no publication bias in the CHD and hypertension analyses, but suggested publication bias in the diabetes analysis. This bias was corrected using the trim-and-fill method, and was ultimately found to have no effect on the results. Our findings suggest patients with CHD, hypertension and diabetes are at greater risk for developing severe COVID-19 than those without these conditions.

Critical Differences between the Binding Features of the Spike Proteins of SARS-CoV-2 and SARS-CoV.

The COVID-19 caused by SARS-CoV-2 has spread globally and caused tremendous loss of lives and properties, and it is of utmost urgency to understand its propagation process and to find ways to slow down the epidemic. In this work, we used a coarse-grained model to calculate the binding free energy of SARS-CoV-2 or SARS-CoV to their human receptor ACE2. The investigation of the free energy contribution of the interacting residues indicates that the residues located outside the receptor binding domain are the source of the stronger binding of the novel virus. Thus, the current results suggest that the essential evolution of SARS-CoV-2 happens remotely from the binding domain at the spike protein trimeric body. Such evolution may facilitate the conformational change and the infection process that occurs after the virus is bound to ACE2. By studying the binding pattern between SARS-CoV antibody m396 and SARS-CoV-2, it is found that the remote energetic contribution is missing, which might explain the absence of cross-reactivity of such antibodies.