Accurate Evaluation on the Interactions of SARS-CoV-2 with Its Receptor ACE2and Antibodies CR3022/CB6

The spread of the coronavirus disease 2019(COVID-19) caused by severe acute respiratory syndrome coronavirus-2(SARS-CoV-2) has become a global health crisis.The binding affinity of SARS-CoV-2(in particular the receptor binding domain,RBD) to its receptor angiotensin converting enzyme 2(ACE2) and the antibodies is of great importance in understanding the infectivity of COVID-19 and evaluating the candidate therapeutic for COVID-19.We propose a new method based on molecular mechanics/Poisson-Boltzmann surface area(MM/PBSA) to accurately calculate the free energy of SARS-CoV-2 RBD binding to ACE2 and antibodies.The calculated binding free energy of SARS-CoV-2 RBD to ACE2 is-13.3 kcal/mol,and that of SARS-CoV RBD to ACE2 is-11.4 kcal/mol,which agree well with the experimental results of-11.3 kcal/mol and-10.1 kcal/mol,respectively.Moreover,we take two recently reported antibodies as examples,and calculate the free energy of antibodies binding to SARS-CoV-2 RBD,which is also consistent with the experimental findings.Further,within the framework of the modified MM/PBSA,we determine the key residues and the main driving forces for the SARS-CoV-2 RBD/CB6 interaction by the computational alanine scanning method.The present study offers a computationally efficient and numerically reliable method to evaluate the free energy of SARS-CoV-2 binding to other proteins,which may stimulate the development of the therapeutics against the COVID-19 disease in real applications.     

Identification of potential Mpro inhibitors for the treatment of COVID-19 by using systematic virtual screening approach

Molecular Diversity - The Corona virus Disease (COVID-19) is caused because of novel coronavirus (SARS-CoV-2) pathogen detected in China for the first time, and from there it spread across the...     

COVID-19 associated perimyocarditis

Perimyocarditis is a well-known acute inflammation of the pericardium and the underlying myocardium. Most commonly perimyocarditis is of viral aetiology, specifically the coxsackie B virus. However, nowadays SARS-CoV-2 associated with COVID-19 infections has emerged as a potential rare cause of perimyocarditis. This case report will demonstrate a case of a young female with perimyocarditis as diagnosed by magnetic resonance imaging (MRI) accompanied by antigens indicating a past COVID-19 infection. Clinical status as well as Findings at MRI, echocardiography and lab results will be reviewed.     

New insights into the catalytic mechanism of the SARS-CoV-2 main protease: an ONIOM QM/MM approach

Molecular Diversity - SARS-CoV-2 Mpro, also known as the main protease or 3C-like protease, is a key enzyme involved in the replication process of the virus that is causing the COVID-19 pandemic....     

Effect of vaccination against COVID-19 spreading

We continue (Ref. 1: Proc. Jpn. Acad. Ser. B 97, 22–49) to analyze the COVID-19 status. We concentrate on the following issues in this work:1. Effect of vaccination against the spreading of SARS-CoV-2.2. General landscape of the world situation concerning vaccinations.3. Some aspects of the new variants of SARS-CoV-2.Our findings include:1. With vaccinations, it is fair to say that we have entered a new phase in the fight against the virus SARS-CoV-2. We have analyzed some preliminary data to find how vaccinations can be effective against COVID-19 spreading. This analysis is based on, and is a continuation of, our first paper quoted in Ref. 1.2. If Tokyo (or Japan) continues to keep its vaccination schedule (starting in early April, 2021 and finishing it for elderly, 65 or older, in 4 months), it will see a sign of control of the virus in early June, 2021 although we see changes of this status due to new, more contagious variants.3. The strength (parameter β) of a new contagious variant can be estimated based on the initial data on the variant (Section 5).     

Structure-based virtual screening,in silico docking,ADME properties prediction and molecular dynamics studies for the identification of potential inhibitors against SARS-CoV-2 Mpro

COVID-19 is a viral pandemic caused by SARS-CoV-2. Due to its highly contagious nature, millions of people are getting affected worldwide knocking down the delicate global socio-economic equilibrium. According to the World Health Organization, COVID-19 has affected over 186 million people with a mortality of around 4 million as of July 09, 2021. Currently, there are few therapeutic options available for COVID-19 control. The rapid mutations in SARS-CoV-2 genome and development of new virulent strains with increased infection and mortality among COVID-19 patients, there is a great need to discover more potential drugs for SARS-CoV-2 on a priority basis. One of the key viral enzymes responsible for the replication and maturation of SARS-CoV-2 is M^pro protein. In the current study, structure-based virtual screening was used to identify four potential ligands against SARS-CoV-2 M^pro from a set of 8,722 ASINEX library compounds. These four compounds were evaluated using ADME filter to check their ADME profile and druggability, and all the four compounds were found to be within the current pharmacological acceptable range. They were individually docked to SARS-CoV-2 M^pro protein to assess their molecular interactions. Further, molecular dynamics (MD) simulations was carried out on protein–ligand complex using Desmond at 100 ns to explore their binding conformational stability. Based on RMSD, RMSF and hydrogen bond interactions, it was found that the stability of protein–ligand complex was maintained throughout the entire 100 ns simulations for all the four compounds. Some of the key ligand amino acid residues participated in stabilizing the protein–ligand interactions includes GLN 189, SER 10, GLU 166, ASN 142 with PHE 66 and TRP 132 of SARS-CoV-2 M^pro. Further optimization of these compounds could lead to promising drug candidates for SARS-CoV-2 M^pro target.

     

基于深度学习的冠状病毒刺突蛋白拉曼特征峰的理论研究

持续一年的新冠疫情对全球的经济造成了巨大破坏,为了有效控制新冠疫情,快速检测新冠病毒(SARS-CoV-2)是一个急需解决的问题。新冠病毒的刺突蛋白(spikeprotein)是拉曼光谱技术检测新冠病毒的检测点,构建刺突蛋白拉曼特征峰模型对于发展拉曼检测技术快速检测新冠病毒具有重要作用。基于简化的激子模型,利用深度神经网络技术,构建了刺突蛋白的酰胺Ⅰ、Ⅲ特征峰模型,并结合已知可以感染人类的七种冠状病毒(HCoV-229E, HCoV-HKU1, HCoV-NL63, HCoV-OC43, MERS-CoV, SARS-CoV和SARS-CoV-2)刺突蛋白的实验结构,分析了七种冠状病毒刺突蛋白酰胺Ⅰ、Ⅲ特征峰的区别。计算结果表明,七种冠状病毒可以根据毒刺突蛋白的酰胺Ⅰ、Ⅲ特征峰划分为四个组：SARS-CoV-2, SARS-CoV, MERS-CoV形成一个组;HCoV-HKU1, HCoV-NL63形成一个组;HCoV-229E和HCoV-OC43各自独立形成一个组。相同组的冠状病毒刺突蛋白酰胺Ⅰ、Ⅲ峰频率较为接近,通过酰胺Ⅰ、Ⅲ峰的频率较难区分刺突蛋白;不同组的冠状病毒刺突蛋白酰胺...     

COVID-19: inflammatory responses,structure-based drug design and potential therapeutics

Molecular Diversity - The COVID-19 pandemic caused by SARS-CoV-2 is responsible for the global health emergency. Here, we explore the diverse mechanisms of SARS-CoV-induced inflammation. We presume...     

Tackling COVID-19 Using Antiviral Nanocoating's—Recent Progress and Future Challenges

In the current situation of the global coronavirus disease 2019 (COVID-19) pandemic, there is a worldwide demand for the protection of regular handling surfaces from viral transmission to restrict the spread of COVID-19 infection. To tackle this challenge, researchers and scientists are continuously working on novel antiviral nanocoatings to make various substrates capable of arresting the spread of such pathogens. These nanocoatings systems include metal/metal oxide nanoparticles, electrospun antiviral polymer nanofibers, antiviral polymer nanoparticles, graphene family nanomaterials, and etched nanostructures. The antiviral mechanism of these systems involves depletion of the spike glycoprotein that anchors to surfaces by the nanocoating and makes the spike glycoprotein and viral nucleotides inactive; however, the nature of the interaction between the spike proteins and virus depends on the type of nanostructure and a surface charge over the coating surface. In this article, the current scenario of COVID-19 and how it can be tackled using antiviral nanocoatings from the further transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), along with their different mode of action, are discussed. Additionally, it is also highlighted different types of nanocoatings developed for various substrates to encounter transmission of SARS-CoV-2, future research areas along with the current challenges related to it, and how these challenges can be resolved.     

Inactivation of the enveloped virus phi6 with hydrodynamic cavitation

The COVID −19 pandemic reminded us that we need better contingency plans to prevent the spread of infectious agents and the occurrence of epidemics or pandemics. Although the transmissibility of SARS-CoV-2 in water has not been confirmed, there are studies that have reported on the presence of infectious coronaviruses in water and wastewater samples. Since standard water treatments are not designed to eliminate viruses, it is of utmost importance to explore advanced treatment processes that can improve water treatment and help inactivate viruses when needed. This is the first study to investigate the effects of hydrodynamic cavitation on the inactivation of bacteriophage phi6, an enveloped virus used as a SARS-CoV-2 surrogate in many studies. In two series of experiments with increasing and constant sample temperature, virus reduction of up to 6.3 logs was achieved. Inactivation of phi6 at temperatures of 10 and 20 °C occurs predominantly by the mechanical effect of cavitation and results in a reduction of up to 4.5 logs. At 30 °C, the reduction increases to up to 6 logs, where the temperature-induced increased susceptibility of the viral lipid envelope makes the virus more prone to inactivation. Furthermore, the control experiments without cavitation showed that the increased temperature alone is not sufficient to cause inactivation, but that additional mechanical stress is still required. The RNA degradation results confirmed that virus inactivation was due to the disrupted lipid bilayer and not to RNA damage. Hydrodynamic cavitation, therefore, has the potential to inactivate current and potentially emerging enveloped pathogenic viruses in water at lower, environmentally relevant temperatures.     

AC2: An Efficient Protein Sequence Compression Tool Using Artificial Neural Networks and Cache-Hash Models

Recently, the scientific community has witnessed a substantial increase in the generation of protein sequence data, triggering emergent challenges of increasing importance, namely efficient storage and improved data analysis. For both applications, data compression is a straightforward solution. However, in the literature, the number of specific protein sequence compressors is relatively low. Moreover, these specialized compressors marginally improve the compression ratio over the best general-purpose compressors. In this paper, we present AC2, a new lossless data compressor for protein (or amino acid) sequences. AC2 uses a neural network to mix experts with a stacked generalization approach and individual cache-hash memory models to the highest-context orders. Compared to the previous compressor (AC), we show gains of 2–9% and 6–7% in reference-free and reference-based modes, respectively. These gains come at the cost of three times slower computations. AC2 also improves memory usage against AC, with requirements about seven times lower, without being affected by the sequences’ input size. As an analysis application, we use AC2 to measure the similarity between each SARS-CoV-2 protein sequence with each viral protein sequence from the whole UniProt database. The results consistently show higher similarity to the pangolin coronavirus, followed by the bat and human coronaviruses, contributing with critical results to a current controversial subject. AC2 is available for free download under GPLv3 license.     

Multi-scale mechanism of antiviral drug-alike phytoligands from Ayurveda in managing COVID-19 and associated metabolic comorbidities: insights from network pharmacology

The novel coronavirus disease (COVID-19), which emerged in Wuhan, China, is continuously spreading worldwide, creating a huge burden on public health and economy. Ayurveda, the oldest healing schema of Traditional Indian Medicinal (TIM) system, is considered as a promising CAM therapy to combat various diseases/ disorders. To explore the regulatory mechanisms of 3038 Ayurvedic herbs (AHs) against SARS-CoV-2, in this study, multi-targeting and synergistic actions of constituent 34,472 phytochemicals (APCs) are investigated using a comprehensive approach comprising of network pharmacology and molecular docking. Immunomodulatory prospects of antiviral drug-alike potentially effective phytochemicals (PEPs) are presented as a special case study, highlighting the importance of 6 AHs in eliciting the antiviral immunity. By evaluating binding affinity of 292 PEPs against 24 SARS-CoV-2 proteins, we develop and analyze a high-confidence “bi-regulatory network” of 115 PEPs having ability to regulate protein targets in both virus and its host human system. Furthermore, mechanistic actions of PEPs against cardiovascular complications, diabetes mellitus and hypertension are also investigated to address the regulatory potential of AHs in dealing with COVID-19-associated metabolic comorbidities. The study further reports 12 PEPs as promising source of COVID-19 comorbidity regulators.

Graphical abstract

     

Computational investigation of drug bank compounds against 3C-like protease (3CLpro) of SARS-CoV-2 using deep learning and molecular dynamics simulation

Blocking the main replicating enzyme, 3 Chymotrypsin-like protease (3CL^pro) is the most promising drug development strategy against the SARS-CoV-2 virus, responsible for the current COVID-19 pandemic. In the present work, 9101 drugs obtained from the drug bank database were screened against SARS-CoV-2 3CL^pro prosing deep learning, molecular docking, and molecular dynamics simulation techniques. In the initial stage, 500 drug-screened by deep learning regression model and subjected to molecular docking that resulted in 10 screened compounds with strong binding affinity. Further, five compounds were checked for their binding potential by analyzing molecular dynamics simulation for 100 ns at 300 K. In the final stage, two compounds {4-[(2s,4e)-2-(1,3-Benzothiazol-2-Yl)-2-(1h-1,2,3-Benzotriazol-1-Yl)-5-Phenylpent-4-Enyl]Phenyl}(Difluoro)Methylphosphonic Acid and 1-(3-(2,4-dimethylthiazol-5-yl)-4-oxo-2,4-dihydroindeno[1,2-c]pyrazol-5-yl)-3-(4-methylpiperazin-1-yl)urea were screened as potential hits by analyzing several parameters like RMSD, Rg, RMSF, MMPBSA, and SASA. Thus, our study suggests two potential drugs that can be tested in the experimental conditions to evaluate the efficacy against SARS-CoV-2. Further, such drugs could be modified to develop more potent drugs against COVID-19.

Graphic abstract

     

Semicovariance Coefficient Analysis of Spike Proteins from SARS-CoV-2 and Other Coronaviruses for Viral Evolution and Characteristics Associated with Fatality

Complex modeling has received significant attention in recent years and is increasingly used to explain statistical phenomena with increasing and decreasing fluctuations, such as the similarity or difference of spike protein charge patterns of coronaviruses. Different from the existing covariance or correlation coefficient methods in traditional integer dimension construction, this study proposes a simplified novel fractional dimension derivation with the exact Excel tool algorithm. It involves the fractional center moment extension to covariance, which results in a complex covariance coefficient that is better than the Pearson correlation coefficient, in the sense that the nonlinearity relationship can be further depicted. The spike protein sequences of coronaviruses were obtained from the GenBank and GISAID databases, including the coronaviruses from pangolin, bat, canine, swine (three variants), feline, tiger, SARS-CoV-1, MERS, and SARS-CoV-2 (including the strains from Wuhan, Beijing, New York, German, and the UK variant B.1.1.7) which were used as the representative examples in this study. By examining the values above and below the average/mean based on the positive and negative charge patterns of the amino acid residues of the spike proteins from coronaviruses, the proposed algorithm provides deep insights into the nonlinear evolving trends of spike proteins for understanding the viral evolution and identifying the protein characteristics associated with viral fatality. The calculation results demonstrate that the complex covariance coefficient analyzed by this algorithm is capable of distinguishing the subtle nonlinear differences in the spike protein charge patterns with reference to Wuhan strain SARS-CoV-2, which the Pearson correlation coefficient may overlook. Our analysis reveals the unique convergent (positive correlative) to divergent (negative correlative) domain center positions of each virus. The convergent or conserved region may be critical to the viral stability or viability; while the divergent region is highly variable between coronaviruses, suggesting high frequency of mutations in this region. The analyses show that the conserved center region of SARS-CoV-1 spike protein is located at amino acid residues 900, but shifted to the amino acid residues 700 in MERS spike protein, and then to amino acid residues 600 in SARS-COV-2 spike protein, indicating the evolution of the coronaviruses. Interestingly, the conserved center region of the spike protein in SARS-COV-2 variant B.1.1.7 shifted back to amino acid residues 700, suggesting this variant is more virulent than the original SARS-COV-2 strain. Another important characteristic our study reveals is that the distance between the divergent mean and the maximal divergent point in each of the viruses (MERS > SARS-CoV-1 > SARS-CoV-2) is proportional to viral fatality rate. This algorithm may help to understand and analyze the evolving trends and critical characteristics of SARS-COV-2 variants, other coronaviral proteins and viruses.     

Minimizing the Spread of Negative Influence in SNIR Model by Contact Blocking

This paper presents a method to minimize the spread of negative influence on social networks by contact blocking. First, based on the infection-spreading process of COVID-19, the traditional susceptible, infectious, and recovered (SIR) propagation model is extended to the susceptible, non-symptomatic, infectious, and recovered (SNIR) model. Based on this model, we present a method to estimate the number of individuals infected by a virus at any given time. By calculating the reduction in the number of infected individuals after blocking contacts, the method selects the set of contacts to be blocked that can maximally reduce the affected range. The selection of contacts to be blocked is repeated until the number of isolated contacts that need to be blocked is reached or all infection sources are blocked. The experimental results on three real datasets and three synthetic datasets show that the algorithm obtains contact blockings that can achieve a larger reduction in the range of infection than other similar algorithms. This shows that the presented SNIR propagation model can more precisely reflect the diffusion and infection process of viruses in social networks, and can efficiently block virus infections.     

Complexity of COVID-19 Dynamics

With population explosion and globalization, the spread of infectious diseases has been a major concern. In 2019, a newly identified type of Coronavirus caused an outbreak of respiratory illness, popularly known as COVID-19, and became a pandemic. Although enormous efforts have been made to understand the spread of COVID-19, our knowledge of the COVID-19 dynamics still remains limited. The present study employs the concepts of chaos theory to examine the temporal dynamic complexity of COVID-19 around the world. The false nearest neighbor (FNN) method is applied to determine the dimensionality and, hence, the complexity of the COVID-19 dynamics. The methodology involves: (1) reconstruction of a single-variable COVID-19 time series in a multi-dimensional phase space to represent the underlying dynamics; and (2) identification of “false” neighbors in the reconstructed phase space and estimation of the dimension of the COVID-19 series. For implementation, COVID-19 data from 40 countries/regions around the world are studied. Two types of COVID-19 data are analyzed: (1) daily COVID-19 cases; and (2) daily COVID-19 deaths. The results for the 40 countries/regions indicate that: (1) the dynamics of COVID-19 cases exhibit low- to medium-level complexity, with dimensionality in the range 3 to 7; and (2) the dynamics of COVID-19 deaths exhibit complexity anywhere from low to high, with dimensionality ranging from 3 to 13. The results also suggest that the complexity of the dynamics of COVID-19 deaths is greater than or at least equal to that of the dynamics of COVID-19 cases for most (three-fourths) of the countries/regions. These results have important implications for modeling and predicting the spread of COVID-19 (and other infectious diseases), especially in the identification of the appropriate complexity of models.     

Oleuropein as a Potent Compound against Neurological Complications Linked with COVID-19: A Computational Biology Approach

The association of COVID-19 with neurological complications is a well-known fact, and researchers are endeavoring to investigate the mechanistic perspectives behind it. SARS-CoV-2 can bind to Toll-like receptor 4 (TLR-4) that would eventually lead to α-synuclein aggregation in neurons and stimulation of neurodegeneration pathways. Olive leaves have been reported as a promising phytotherapy or co-therapy against COVID-19, and oleuropein is one of the major active components of olive leaves. In the current study, oleuropein was investigated against SARS-CoV-2 target (main protease 3CL^pro), TLR-4 and Prolyl Oligopeptidases (POP), to explore oleuropein potency against the neurological complications associated with COVID-19. Docking experiments, docking validation, interaction analysis, and molecular dynamic simulation analysis were performed to provide insight into the binding pattern of oleuropein with the three target proteins. Interaction analysis revealed strong bonding between oleuropein and the active site amino acid residues of the target proteins. Results were further compared with positive control lopinavir (3CL^pro), resatorvid (TLR-4), and berberine (POP). Moreover, molecular dynamic simulation was performed using YASARA structure tool, and AMBER14 force field was applied to examine an 100 ns trajectory run. For each target protein-oleuropein complex, RMSD, RoG, and total potential energy were estimated, and 400 snapshots were obtained after each 250 ps. Docking analyses showed binding energy as −7.8, −8.3, and −8.5 kcal/mol for oleuropein-3CL^pro, oleuropein-TLR4, and oleuropein-POP interactions, respectively. Importantly, target protein-oleuropein complexes were stable during the 100 ns simulation run. However, an experimental in vitro study of the binding of oleuropein to the purified targets would be necessary to confirm the present study outcomes.     

新型冠状病毒S蛋白在金纳米粒子中的表面增强拉曼效应

表面增强拉曼光谱技术因其高灵敏度、操作简单、快速检测等优点,被广泛用于病毒检测方面。国内外的病毒拉曼检测研究主要集中在检测病毒核酸以及组成核酸的各种碱基的表面增强拉曼光谱(SERS),但少见对病毒蛋白的SERS检测。以新型冠状病毒（SARS-CoV-2）的S蛋白为检测对象,采用无标记SERS检测方法,对比SARS-CoV-2固态、饱和液态S蛋白的普通拉曼光谱和选用40 nm金纳米粒子为基底的SARS-CoV-2低浓度S蛋白SERS光谱。结果表明,以40 nm金纳米粒子为基底,采用SERS技术检测SARS-CoV-2的S蛋白是完全可行的。SARS-CoV-2的S蛋白分子中的羧基与金纳米粒子发生了分子增强,氨基与金纳米粒子发生了电磁增强,从而使得SARS-CoV-2的S蛋白拉曼效应得到了增强,并使得峰位发生一定移动。实验获得了较好的SARS-CoV-2低浓度S蛋白SERS光谱,为建立敏感、特异、快速的SARS-CoV-2检测新技术提供了一种方法。     

COVID-19’s Impact on International Trade

We analyze how the COVID-19 pandemic affected the trade of products between countries. With this aim, using the United Nations Comtrade database, we perform a Google matrix analysis of the multiproduct World Trade Network (WTN) for the years 2018–2020, comprising the emergence of the COVID-19 as a global pandemic. The applied algorithms—PageRank, CheiRank and the reduced Google matrix—take into account the multiplicity of the WTN links, providing new insights into international trade compared to the usual import–export analysis. These complex networks analysis algorithms establish new rankings and trade balances of countries and products considering all countries on equal grounds, independent of their wealth, and every product on the basis of its relative exchanged volumes. In comparison with the pre-COVID-19 period, significant changes in these metrics occurred for the year 2020, highlighting a major rewiring of the international trade flows induced by the COVID-19 pandemic crisis. We define a new PageRank–CheiRank product trade balance, either export or import-oriented, which is significantly perturbed by the pandemic.     

Piecewise Modeling the Accumulated Daily Growth of COVID-19 Deaths: The Case of the State of São Paulo,Brazil

The pandemic scenery caused by the new coronavirus, called SARS-CoV-2, increased interest in statistical models capable of projecting the evolution of the number of cases (and associated deaths) due to COVID-19 in countries, states and/or cities. This interest is mainly due to the fact that the projections may help the government agencies in making decisions in relation to procedures of prevention of the disease. Since the growth of the number of cases (and deaths) of COVID-19, in general, has presented a heterogeneous evolution over time, it is important that the modeling procedure is capable of identifying periods with different growth rates and proposing an adequate model for each period. Here, we present a modeling procedure based on the fit of a piecewise growth model for the cumulative number of deaths. We opt to focus on the modeling of the cumulative number of deaths because, other than for the number of cases, these values do not depend on the number of diagnostic tests performed. In the proposed approach, the model is updated in the course of the pandemic, and whenever a “new” period of the pandemic is identified, it creates a new sub-dataset composed of the cumulative number of deaths registered from the change point and a new growth model is chosen for that period. Three growth models were fitted for each period: exponential, logistic and Gompertz models. The best model for the cumulative number of deaths recorded is the one with the smallest mean square error and the smallest Akaike information criterion (AIC) and Bayesian information criterion (BIC) values. This approach is illustrated in a case study, in which we model the number of deaths due to COVID-19 recorded in the State of São Paulo, Brazil. The results have shown that the fit of a piecewise model is very effective for explaining the different periods of the pandemic evolution.