Computational screening of 645 antiviral peptides against the receptor-binding domain of the spike protein in SARS-CoV-2

Recent advances in application of computer-aided drug design in anti-COVID-19 Virials Drug Discovery

Corona Virus Disease 2019 (COVID-19) is a global pandemic epidemic caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which poses a serious threat to human health worldwide and results in significant economic losses. With the continuous emergence of new virus strains, small molecule drugs remain the most effective treatment for COVID-19. The traditional drug development process usually requires several years; however, the development of computer-aided drug design (CADD) offers the opportunity to develop innovative drugs quickly and efficiently. The literature review describes the general process of CADD, the viral proteins that play essential roles in the life cycle of SARS-CoV-2 and can serve as therapeutic targets, and examples of drug screening of viral target proteins by applying CADD methods. Finally, the potential of CADD in COVID-19 therapy, the deficiency, and the possible future development direction are discussed.

Antiviral peptides inhibiting the main protease of SARS-CoV-2 investigated by computational screening and in vitro protease assay

The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) plays an important role in viral replication and transcription and received great attention as a vital target for drug/peptide development. Therapeutic agents such as small-molecule drugs or peptides that interact with the Cys-His present in the catalytic site of Mpro are an efficient way to inhibit the protease. Although several emergency-approved vaccines showed good efficacy and drastically dropped the infection rate, evolving variants are still infecting and killing millions of people globally. While a small-molecule drug (Paxlovid) received emergency approval, small-molecule drugs have low target specificity and higher toxicity. Besides small-molecule drugs, peptide therapeutics are thus gaining increasing popularity as they are easy to synthesize and highly selective and have limited side effects. In this study, we investigated the therapeutic value of 67 peptides targeting Mpro using molecular docking. Subsequently, molecular dynamics (MD) simulations were implemented on eight protein-peptide complexes to obtain molecular-level information on the interaction between these peptides and the Mpro active site, which revealed that temporin L, indolicidin, and lymphocytic choriomeningitis virus (LCMV) GP1 are the best candidates in terms of stability, interaction, and structural compactness. These peptides were synthesized using the solid-phase peptide synthesis protocol, purified by reversed-phase high-performance liquid chromatography (RP-HPLC), and authenticated by mass spectrometry (MS). The in vitro fluorometric Mpro activity assay was used to validate the computational results, where temporin L and indolicidin were observed to be very active against SARS-CoV-2 Mpro with IC50 values of 38.80 and 87.23 μM, respectively. A liquid chromatography-MS (LC-MS) assay was developed, and the IC50 value of temporin L was measured at 23.8 μM. The solution-state nuclear magnetic resonance (NMR) structure of temporin L was determined in the absence of sodium dodecyl sulfate (SDS) micelles and was compared to previous temporin structures. This combined investigation provides critical insights and assists us to further develop peptide inhibitors of SARS-CoV-2 Mpro through structural guided investigation.

A New Advanced Approach: Design and Screening of Affinity Peptide Ligands Using Computer Simulation Techniques

Peptides acquire target affinity based on the combination of residues in their sequences and the conformation formed by their flexible folding, an ability that makes them very attractive biomaterials in therapeutic, diagnostic, and assay fields. With the development of computer technology, computer-aided design and screening of affinity peptides has become a more efficient and faster method. This review summarizes successful cases of computer-aided design and screening of affinity peptide ligands in recent years and lists the computer programs and online servers used in the process. In particular, the characteristics of different design and screening methods are summarized and categorized to help researchers choose between different methods. In addition, experimentally validated sequences are listed, and their applications are described, providing directions for the future development and application of computational peptide screening and design.

Characterization of lignin and hemicellulose degrading bacteria isolated from cow rumen and forest soil: Unveiling a novel enzymatic model for rice straw deconstruction

Application of greener pretreatment technology using robust ligninolytic bacteria for short duration to deconstruct rice straw and enhance bioethanol production is currently lacking. The objective of this study is to characterize three bacterial strains isolated from the milieux of cow rumen and forest soil and explore their capabilities of breaking down lignocellulose - an essential process in bioethanol production. Using biochemical and genomic analyses these strains were identified as Bacillus sp. HSTU-bmb18, Bacillus sp. HSTU-bmb19, and Citrobacter sp. HSTU-bmb20. Genomic analysis of the strains unveiled validated model hemicellulases, multicopper oxidases, and pectate lyases. These enzymes exhibited interactions with distinct lignocellulose substrates, further affirmed by their stability in molecular dynamic simulations. A comprehensive expression of ligninolytic pathways, including β-ketoadipate, phenyl acetate, and benzoate, was observed within the HSTU-bmb20 genome. The strains secreted approximately 75-82 U/mL of cellulase, xylase, pectinase, and lignin peroxidase. FT-IR analysis of the bacterial treated rice straw fibers revealed that the intensity of lignin-related peaks decreased, while cellulose-related peaks sharpened. The values of crystallinity index for the untreated control and the treated rice straw with either HSTU-bmb18, or HSTU-bmb19, or HSTU-bmb20 were recorded to be 34.48, 28.49, 29.36, 31.75, respectively, which are much higher than that of 13.53 noted for those treated with the bacterial consortium. The ratio of fermentable cellulose in rice straw increased by 1.25-, 1.79-, 1.93- and 2.17-fold following treatments with HSTU-bmb18, HSTU-bmb20, HSTU-bmb19, and a mixed consortium of these three strains, respectively. These aggregative results suggested a novel model for rice straw deconstruction utilizing hydrolytic enzymes of the consortium, revealing superior efficacy compared to individual strains, and advancing cost-effective, affordable, and sustainable green technology.

A review of SARS-CoV-2 variants and vaccines: Viral properties, mutations, vaccine efficacy, and safety

The severe acute respiratory syndrome coronavirus disease 2 instigated by coronavirus disease of 2019 (COVID-19) has delivered an unfathomable obstruction that has touched all sectors worldwide. Despite new vaccine technologies and mass administration of booster doses, the virus persists, and unknown the ending of the pandemic for new variants and sub-variants. Moreover, whether leaning on home medications or using plant extracts is sufficient often to combat the virus has generated tremendous interest in the scientific fraternity. Different databases including PubMed, Scopus, Web of Science, and Google Scholar used to find published articles linked with related topics. Currently, COVID-19 third and fourth shots of vaccines are progressively administered worldwide, where some countries trail others by a significant margin. Many proteins related to viral activity have changed, possibly boosting the virus infectivity and making antibodies ineffective. This study will reminisce the viral genome, associated pathways for viral protein functions, variants, and their mutations. The current, comprehensive review will also provide information on vaccine technologies developed by several biotech companies and the efficacy of their doses, costs including boosters on a mass level. As no vaccine is working to protect fully against all the variants, the new proactive vaccine research needs to be conducted based on all variants, their sub-lineage, and mutations.

Exploring the binding capacity of lactic acid bacteria derived bacteriocins against RBD of SARS-CoV-2 Omicron variant by molecular simulations

The changes in the SARS-CoV-2 genome have resulted in the emergence of new variants. Some of the variants have been classified as variants of concern (VOC). These strains have higher transmission rate and improved fitness. One of the prevalent were the Omicron variant. Unlike previous VOCs, the Omicron possesses fifteen mutations on the spike protein's receptor binding domain (RBD). The modifications of spike protein's key amino acid residues facilitate the virus' binding capability against ACE2, resulting in an increase in the infectiousness of Omicron variant. Consequently, investigating the prevention and treatment of the Omicron variant is crucial. In the present study, we aim to explore the binding capacity of twenty-two bacteriocins derived from Lactic Acid Bacteria (LAB) against the Omicron variant by using protein-peptidedocking and molecular dynamics (MD) simulations. The Omicron variant RBD was prepared by introducing fifteen mutations using PyMol. The protein-peptide complexes were obtained using HADDOCK v2.4 docking webserver. Top scoring complexes obtained from HADDOCK webserver were retrieved and submitted to the PRODIGY server for the prediction of binding energies. RBD-bacteriocin complexes were subjected to MD simulations. We discovered promising peptide-based therapeutic candidates for the inhibition of Omicron variant for example Salivaricin B, Pediocin PA 1, Plantaricin W, Lactococcin mmfii and Enterocin A. The lead bacteriocins, except Enterocin A, are biosynthesized by food-grade lactic acid bacteria. Our study puts forth a preliminary information regarding potential utilization of food-grade LAB-derived bacteriocins, particularly Salivaricin B and Pediocin PA 1, for Covid-19 treatment and prophylaxis.Communicated by Ramaswamy H. Sarma.

Some common deleterious mutations are shared in SARS-CoV-2 genomes from deceased COVID-19 patients across continents

The identification of deleterious mutations in different variants of SARS-CoV-2 and their roles in the morbidity of COVID-19 patients has yet to be thoroughly investigated. To unravel the spectrum of mutations and their effects within SARS-CoV-2 genomes, we analyzed 5,724 complete genomes from deceased COVID-19 patients sourced from the GISAID database. This analysis was conducted using the Nextstrain platform, applying a generalized time-reversible model for evolutionary phylogeny. These genomes were compared to the reference strain (hCoV-19/Wuhan/WIV04/2019) using MAFFT v7.470. Our findings revealed that SARS-CoV-2 genomes from deceased individuals belonged to 21 Nextstrain clades, with clade 20I (Alpha variant) being the most predominant, followed by clade 20H (Beta variant) and clade 20J (Gamma variant). The majority of SARS-CoV-2 genomes from deceased patients (33.4%) were sequenced in North America, while the lowest percentage (0.98%) came from Africa. The 'G' clade was dominant in the SARS-CoV-2 genomes of Asian, African, and North American regions, while the 'GRY' clade prevailed in Europe. In our analysis, we identified 35,799 nucleotide (NT) mutations throughout the genome, with the highest frequency (11,402 occurrences) found in the spike protein. Notably, we observed 4150 point-specific amino acid (AA) mutations in SARS-CoV-2 genomes, with D614G (20%) and N501Y (14%) identified as the top two deleterious mutations in the spike protein on a global scale. Furthermore, we detected five common deleterious AA mutations, including G18V, W45S, I33T, P30L, and Q418H, which play a key role in defining each clade of SARS-CoV-2. Our novel findings hold potential value for genomic surveillance, enabling the monitoring of the evolving pattern of SARS-CoV-2 infection, its emerging variants, and their impact on the development of effective vaccination and control strategies.

Insight into vaccination and meteorological factors on daily COVID-19 cases and mortality in Bangladesh

The ongoing COVID-19 contagious disease caused by SARS-CoV-2 has disrupted global public health, businesses, and economies due to widespread infection, with 676.41 million confirmed cases and 6.77 million deaths in 231 countries as of February 07, 2023. To control the rapid spread of SARS-CoV-2, it is crucial to determine the potential determinants such as meteorological factors and their roles. This study examines how COVID-19 cases and deaths changed over time while assessing meteorological characteristics that could impact these disparities from the onset of the pandemic. We used data spanning two years across all eight administrative divisions, this is the first of its kind--showing a connection between meteorological conditions, vaccination, and COVID-19 incidences in Bangladesh. We further employed several techniques including Simple Exponential Smoothing (SES), Auto-Regressive Integrated Moving Average (ARIMA), Auto-Regressive Integrated Moving Average with explanatory variables (ARIMAX), and Automatic forecasting time-series model (Prophet). We further analyzed the effects of COVID-19 vaccination on daily cases and deaths. Data on COVID-19 cases collected include eight administrative divisions of Bangladesh spanning March 8, 2020, to January 31, 2023, from available online servers. The meteorological data include rainfall (mm), relative humidity (%), average temperature (°C), surface pressure (kPa), dew point (°C), and maximum wind speed (m/s). The observed wind speed and surface pressure show a significant negative impact on COVID-19 cases (-0.89, 95% confidence interval (CI): 1.62 to -0.21) and (-1.31, 95%CI: 2.32 to -0.29), respectively. Similarly, the observed wind speed and surface pressure show a significant negative impact on COVID-19 deaths (-0.87, 95% CI: 1.54 to -0.21) and (-3.11, 95%CI: 4.44 to -1.25), respectively. The impact of meteorological factors is almost similar when vaccination information is included in the model. However, the impact of vaccination in both cases and deaths model is significantly negative (for cases: 1.19, 95%CI: 2.35 to -0.38 and for deaths: 1.55, 95%CI: 2.88 to -0.43). Accordingly, vaccination effectively reduces the number of new COVID-19 cases and fatalities in Bangladesh. Thus, these results could assist future researchers and policymakers in the assessment of pandemics, by making thorough efforts that account for COVID-19 vaccinations and meteorological conditions.

Correlation of Dengue and Meteorological Factors in Bangladesh: A Public Health Concern

Dengue virus (DENV) is an enveloped, single-stranded RNA virus, a member of the Flaviviridae family (which causes Dengue fever), and an arthropod-transmitted human viral infection. Bangladesh is well known for having some of Asia's most vulnerable Dengue outbreaks, with climate change, its location, and it's dense population serving as the main contributors. For speculation about DENV outbreak characteristics, it is crucial to determine how meteorological factors correlate with the number of cases. This study used five time series models to observe the trend and forecast Dengue cases. Current data-based research has also applied four statistical models to test the relationship between Dengue-positive cases and meteorological parameters. Datasets were used from NASA for meteorological parameters, and daily DENV cases were obtained from the Directorate General of Health Service (DGHS) open-access websites. During the study period, the mean of DENV cases was 882.26 ± 3993.18, ranging between a minimum of 0 to a maximum of 52,636 daily confirmed cases. The Spearman's rank correlation coefficient between climatic variables and Dengue incidence indicated that no substantial relationship exists between daily Dengue cases and wind speed, temperature, and surface pressure (Spearman's rho; r = -0.007, p > 0.05; r = 0.085, p > 0.05; and r = -0.086, p > 0.05, respectively). Still, a significant relationship exists between daily Dengue cases and dew point, relative humidity, and rainfall (r = 0.158, p < 0.05; r = 0.175, p < 0.05; and r = 0.138, p < 0.05, respectively). Using the ARIMAX and GA models, the relationship for Dengue cases with wind speed is -666.50 [95% CI: -1711.86 to 378.86] and -953.05 [-2403.46 to 497.36], respectively. A similar negative relation between Dengue cases and wind speed was also determined in the GLM model (IRR = 0.98). Dew point and surface pressure also represented a negative correlation in both ARIMAX and GA models, respectively, but the GLM model showed a positive association. Additionally, temperature and relative humidity showed a positive correlation with Dengue cases (105.71 and 57.39, respectively, in the ARIMAX, 633.86, and 200.03 in the GA model). In contrast, both temperature and relative humidity showed negative relation with Dengue cases in the GLM model. In the Poisson regression model, windspeed has a substantial significant negative connection with Dengue cases in all seasons. Temperature and rainfall are significantly and positively associated with Dengue cases in all seasons. The association between meteorological factors and recent outbreak data is the first study where we are aware of the use of maximum time series models in Bangladesh. Taking comprehensive measures against DENV outbreaks in the future can be possible through these findings, which can help fellow researchers and policymakers.

Variant-specific deleterious mutations in the SARS-CoV-2 genome reveal immune responses and potentials for prophylactic vaccine development

Introduction: Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has had a disastrous effect worldwide during the previous three years due to widespread infections with SARS-CoV-2 and its emerging variations. More than 674 million confirmed cases and over 6.7 million deaths have been attributed to successive waves of SARS-CoV-2 infections as of 29th January 2023. Similar to other RNA viruses, SARS-CoV-2 is more susceptible to genetic evolution and spontaneous mutations over time, resulting in the continual emergence of variants with distinct characteristics. Spontaneous mutations of SARS-CoV-2 variants increase its transmissibility, virulence, and disease severity and diminish the efficacy of therapeutics and vaccines, resulting in vaccine-breakthrough infections and re-infection, leading to high mortality and morbidity rates. Materials and methods: In this study, we evaluated 10,531 whole genome sequences of all reported variants globally through a computational approach to assess the spread and emergence of the mutations in the SARS-CoV-2 genome. The available data sources of NextCladeCLI 2.3.0 (https://clades.nextstrain.org/) and NextStrain (https://nextstrain.org/) were searched for tracking SARS-CoV-2 mutations, analysed using the PROVEAN, Polyphen-2, and Predict SNP mutational analysis tools and validated by Machine Learning models. Result: Compared to the Wuhan-Hu-1 reference strain NC 045512.2, genome-wide annotations showed 16,954 mutations in the SARS-CoV-2 genome. We determined that the Omicron variant had 6,307 mutations (retrieved sequence:1947), including 67.8% unique mutations, more than any other variant evaluated in this study. The spike protein of the Omicron variant harboured 876 mutations, including 443 deleterious mutations. Among these deleterious mutations, 187 were common and 256 were unique non-synonymous mutations. In contrast, after analysing 1,884 sequences of the Delta variant, we discovered 4,468 mutations, of which 66% were unique, and not previously reported in other variants. Mutations affecting spike proteins are mostly found in RBD regions for Omicron, whereas most of the Delta variant mutations drawn to focus on amino acid regions ranging from 911 to 924 in the context of epitope prediction (B cell & T cell) and mutational stability impact analysis protruding that Omicron is more transmissible. Discussion: The pathogenesis of the Omicron variant could be prevented if the deleterious and persistent unique immunosuppressive mutations can be targeted for vaccination or small-molecule inhibitor designing. Thus, our findings will help researchers monitor and track the continuously evolving nature of SARS-CoV-2 strains, the associated genetic variants, and their implications for developing effective control and prophylaxis strategies.

A 30-day follow-up study on the prevalence of SARS-COV-2 genetic markers in wastewater from the residence of COVID-19 patient and comparison with clinical positivity

Wastewater based epidemiology (WBE) is an important tool to fight against COVID-19 as it provides insights into the health status of the targeted population from a small single house to a large municipality in a cost-effective, rapid, and non-invasive way. The implementation of wastewater based surveillance (WBS) could reduce the burden on the public health system, management of pandemics, help to make informed decisions, and protect public health. In this study, a house with COVID-19 patients was targeted for monitoring the prevalence of SARS-CoV-2 genetic markers in wastewater samples (WS) with clinical specimens (CS) for a period of 30 days. RT-qPCR technique was employed to target nonstructural (ORF1ab) and structural-nucleocapsid (N) protein genes of SARS-CoV-2, according to a validated experimental protocol. Physiological, environmental, and biological parameters were also measured following the American Public Health Association (APHA) standard protocols. SARS-CoV-2 viral shedding in wastewater peaked when the highest number of COVID-19 cases were clinically diagnosed. Throughout the study period, 7450 to 23,000 gene copies/1000 mL were detected, where we identified 47 % (57/120) positive samples from WS and 35 % (128/360) from CS. When the COVID-19 patient number was the lowest (2), the highest CT value (39.4; i.e., lowest copy number) was identified from WS. On the other hand, when the COVID-19 patients were the highest (6), the lowest CT value (25.2 i.e., highest copy numbers) was obtained from WS. An advance signal of increased SARS-CoV-2 viral load from the COVID-19 patient was found in WS earlier than in the CS. Using customized primer sets in a traditional PCR approach, we confirmed that all SARS-CoV-2 variants identified in both CS and WS were Delta variants (B.1.617.2). To our knowledge, this is the first follow-up study to determine a temporal relationship between COVID-19 patients and their discharge of SARS-CoV-2 RNA genetic markers in wastewater from a single house including all family members for clinical sampling from a developing country (Bangladesh), where a proper sewage system is lacking. The salient findings of the study indicate that monitoring the genetic markers of the SARS-CoV-2 virus in wastewater could identify COVID-19 cases, which reduces the burden on the public health system during COVID-19 pandemics.

A bibliometric study on Marburg virus research with prevention and control strategies

Marburg virus (MARV) is a pathogenic zoonotic RNA virus etiologic for Marburg virus disease (MVD), a severe hemorrhagic fever. This is a rare disease, with a high fatality rate, that spreads via infected blood or body fluids or indirectly via fomites (contaminated objects and substances such as clothed, beds, personal protective equipment, or medical equipments). A few vaccines to protect against MARV are undergoing clinical trials, but there is not yet an approved vaccine against this disease. Eventually, prevention and control guidelines should be adhered to rigorously to alleviate this infection. This bibliometric analysis aimed to harness narrative evaluation, emphasizing the significance of quantitative approaches and delineating the most thought-provoking concerns for researchers using VOSviewer software (Centre for Science and Technology Studies, Leiden University, the Netherlands). “Marburg Virus” OR “MARV” AND “Diseases” search criteria were used for the analysis of articles published between 1962 and 2022. Co-occurrence analysis was carried out, which characterized different thematic clusters. From this analysis, we found that 1688 published articles, and the number of publications increased across that period annually, with a growth rate of 8.78%. It is also conspicuous that the number of publications in the United States reached its acme during this period (i.e., 714 publications, accounting for 42.29% of the total), and the United States Army Medical Research Institute of Infectious Diseases published the most literature (i.e., 146 papers). Our study found that the three pre-eminent authors of Marburg virus papers were “FELDMANN, HEINZ“ of the National Institute of Allergy and Infectious Diseases, United States, “BECKER, STEPHAN” of the Philipps University of Marburg, Germany, and “GEISBERT, THOMAS W” of the University of Texas Medical Branch, United States. In this study we found that “JOURNAL OF VIROLOGY” has published the most pertinent literature, totaling 88 articles, followed by “The journal of Infectious Diseases”, which published 76 relevant papers, and “VIRUSES”, which published 52 corresponding papers. The most cited paper on the Marburg virus was published in Nature Medicine, with 522 total citations and 29 citations/year. Studies of the changing epidemiology and evolving nature of the virus and its ecological niche are required; breakthrough and implementation of the efficacious vaccine candidate(s), prophylaxis and therapeutic alternatives and supervision strategies, unveiling awareness-raising programs, and developing apposite and timely preparedness, prevention, and proactive control strategies are of utmost importance.

Potential antiviral peptides against the nucleoprotein of SARS-CoV-2

Nucleoprotein is a conserved structural protein of SARS-CoV-2, which is involved in several functions, including replication, packaging, and transcription. In this research, 21 antiviral peptides that are known to have inhibitory function against nucleoprotein in several other viruses, were screened computationally against the nucleoprotein of SARS-CoV-2. The complexes of five best performing peptides (AVP1142, AVP1145, AVP1148, AVP1150, AVP1155) with nucleoprotein were selected for subsequent screening via 5 ns molecular dynamics (MD) simulation. Two peptides, namely AVP1145 and AVP1155, came out as promising candidates and hence were selected for 200 ns MD simulation for further validation, incorporating a DMPC-based membrane environment. In the long MD simulation, both AVP1155 and AVP1145 utilized multiple residues-mainly aromatic, acidic, and nonpolar residues-as interacting points to remain in contact with the nucleoprotein and formed predominantly hydrogen bonds along with hydrophobic and electrostatic interactions. However, AVP1155 proved to be superior to AVP1145 when its complex with nucleoprotein was analyzed in terms of root-mean-square deviation, root-mean-square fluctuation, radius of gyration, solvent accessible surface area and free energy landscape. In a nutshell, the findings of this research may guide future studies in the development of selective peptide inhibitors of SARS-CoV-2 nucleoprotein.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11696-022-02514-4.

Large scale peptide screening against main protease of SARS CoV-2

The COVID-19 pandemic has been a public health emergency, with deadly forms constantly emerging around the world, highlighting the dire need for highly effective antiviral therapeutics. Peptide therapeutics show significant potential for this viral disease due to their efficiency, safety, and specificity. Here, two thousand seven hundred eight antibacterial peptides were screened computationally targeting the Main protease (Mpro) of SARS CoV-2. Six top-ranked peptides according to their binding scores, binding pose were investigated by molecular dynamics to explore the interaction and binding behavior of peptide-Mpro complexes. The structural and energetic characteristics of Mpro-DRAMP01760 and Mpro-DRAMP01808 complexes fluctuated less during a 250 ns MD simulation. In addition, three peptides (DRAMP01760, DRAMP01808, and DRAMP01342) bind strongly to Mpro protein, according to the free energy landscape and principal component analysis. Peptide helicity and secondary structure analysis are in agreement with our findings. Interaction analysis of protein-peptide complexes demonstrated that Mpro's residue CYS145, HIS41, PRO168, GLU166, GLN189, ASN142, MET49, and THR26 play significant contributions in peptide-protein attachment. Binding free energy analysis (MM-PBSA) demonstrated the energy profile of interacting residues of Mpro in peptide-Mpro complexes. To summarize, the peptides DRAMP01808 and DRAMP01760 may be highly Mpro specific, resulting disruption in a viral replication and transcription. The results of this research are expected to assist future research toward the development of antiviral peptide-based therapeutics for Covid-19 treatment.

Potential antiviral peptides targeting the SARS-CoV-2 spike protein

BACKGROUND

The coronavirus disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection became an international pandemic and created a public health crisis. The binding of the viral Spike glycoprotein to the human cell receptor angiotensin-converting enzyme 2 (ACE2) initiates viral infection. The development of efficient treatments to combat coronavirus disease is considered essential.

METHODS

An in silico approach was employed to design amino acid peptide inhibitor against the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. The designed inhibitor (SARS-CoV-2 PEP 49) consists of amino acids with the α1 helix and the β4 - β5 sheets of ACE2. The PEP-FOLD3 web tool was used to create the 3D structures of the peptide amino acids. Analyzing the interaction between ACE2 and the RBD of the Spike protein for three protein data bank entries (6M0J, 7C8D, and 7A95) indicated that the interacting amino acids were contained inside two regions of ACE2: the α1 helical protease domain (PD) and the β4 - β5 sheets.

RESULTS

Molecular docking analysis of the designed inhibitor demonstrated that SARS-CoV-2 PEP 49 attaches directly to the ACE2 binding site of the Spike protein with a binding affinity greater than the ACE2, implying that the SARS-CoV-2 PEP 49 model may be useful as a potential RBD binding blocker.

Association of household fuel with acute respiratory infection (ARI) under-five years children in Bangladesh

In developing countries, acute respiratory infections (ARIs) cause a significant number of deaths among children. According to Bangladesh Demographic and Health Survey (BDHS), about 25% of the deaths in children under-five years are caused by ARI in Bangladesh every year. Low-income families frequently rely on wood, coal, and animal excrement for cooking. However, it is unclear whether using alternative fuels offers a health benefit over solid fuels. To clear this doubt, we conducted a study to investigate the effects of fuel usage on ARI in children. In this study, we used the latest BDHS 2017-18 survey data collected by the Government of Bangladesh (GoB) and estimated the effects of fuel use on ARI by constructing multivariable logistic regression models. From the analysis, we found that the crude (the only type of fuel in the model) odds ratio (OR) for ARI is 1.69 [95% confidence interval (CI): 1.06-2.71]. This suggests that children in families using contaminated fuels are 69.3% more likely to experience an ARI episode than children in households using clean fuels. After adjusting for cooking fuel, type of roof material, child's age (months), and sex of the child-the effect of solid fuels is similar to the adjusted odds ratio (AOR) for ARI (OR: 1.69, 95% CI: 1.05-2.72). This implies that an ARI occurrence is 69.2% more likely when compared to the effect of clean fuel. This study found a statistically significant association between solid fuel consumption and the occurrence of ARI in children in households. The correlation between indoor air pollution and clinical parameters of ARI requires further investigation. Our findings will also help other researchers and policymakers to take comprehensive actions by considering fuel type as a risk factor as well as taking proper steps to solve this issue.

Association between Global Monkeypox Cases and Meteorological Factors

The emergence of an outbreak of Monkeypox disease (MPXD) is caused by a contagious zoonotic Monkeypox virus (MPXV) that has spread globally. Yet, there is no study investigating the effect of climatic changes on MPXV transmission. Thus, studies on the changing epidemiology, evolving nature of the virus, and ecological niche are highly paramount. Determination of the role of potential meteorological drivers including temperature, precipitation, relative humidity, dew point, wind speed, and surface pressure is beneficial to understand the MPXD outbreak. This study examines the changes in MPXV cases over time while assessing the meteorological characteristics that could impact these disparities from the onset of the global outbreak. To conduct this data-based research, several well-accepted statistical techniques including Simple Exponential Smoothing (SES), Auto-Regressive Integrated Moving Average (ARIMA), Automatic forecasting time-series model (Prophet), and Autoregressive Integrated Moving Average with Explanatory Variables (ARIMAX) were applied to delineate the correlation of the meteorological factors on global daily Monkeypox cases. Data on MPXV cases including affected countries spanning from 6 May 2022, to 9 November 2022, from global databases and meteorological data were used to evaluate the developed models. According to the ARIMAX model, the results showed that temperature, relative humidity, and surface pressure have a positive impact [(51.56, 95% confidence interval (CI): -274.55 to 377.68), (17.32, 95% CI: -83.71 to 118.35) and (23.42, 95% CI: -9.90 to 56.75), respectively] on MPXV cases. In addition, dew/frost point, precipitation, and wind speed show a significant negative impact on MPXD cases. The Prophet model showed a significant correlation with rising MPXD cases, although the trend predicts peak values while the overall trend increases. This underscores the importance of immediate and appropriate preventive measures (timely preparedness and proactive control strategies) with utmost priority against MPXD including awareness-raising programs, the discovery, and formulation of effective vaccine candidate(s), prophylaxis and therapeutic regimes, and management strategies.

IUP-BERT: Identification of Umami Peptides Based on BERT Features

Umami is an important widely-used taste component of food seasoning. Umami peptides are specific structural peptides endowing foods with a favorable umami taste. Laboratory approaches used to identify umami peptides are time-consuming and labor-intensive, which are not feasible for rapid screening. Here, we developed a novel peptide sequence-based umami peptide predictor, namely iUP-BERT, which was based on the deep learning pretrained neural network feature extraction method. After optimization, a single deep representation learning feature encoding method (BERT: bidirectional encoder representations from transformer) in conjugation with the synthetic minority over-sampling technique (SMOTE) and support vector machine (SVM) methods was adopted for model creation to generate predicted probabilistic scores of potential umami peptides. Further extensive empirical experiments on cross-validation and an independent test showed that iUP-BERT outperformed the existing methods with improvements, highlighting its effectiveness and robustness. Finally, an open-access iUP-BERT web server was built. To our knowledge, this is the first efficient sequence-based umami predictor created based on a single deep-learning pretrained neural network feature extraction method. By predicting umami peptides, iUP-BERT can help in further research to improve the palatability of dietary supplements in the future.

Wastewater-based epidemiological surveillance to monitor the prevalence of SARS-CoV-2 in developing countries with onsite sanitation facilities

Wastewater-based epidemiology (WBE) has emerged as a valuable approach for forecasting disease outbreaks in developed countries with a centralized sewage infrastructure. On the other hand, due to the absence of well-defined and systematic sewage networks, WBE is challenging to implement in developing countries like Bangladesh where most people live in rural areas. Identification of appropriate locations for rural Hotspot Based Sampling (HBS) and urban Drain Based Sampling (DBS) are critical to enable WBE based monitoring system. We investigated the best sampling locations from both urban and rural areas in Bangladesh after evaluating the sanitation infrastructure for forecasting COVID-19 prevalence. A total of 168 wastewater samples were collected from 14 districts of Bangladesh during each of the two peak pandemic seasons. RT-qPCR commercial kits were used to target ORF1ab and N genes. The presence of SARS-CoV-2 genetic materials was found in 98% (165/168) and 95% (160/168) wastewater samples in the first and second round sampling, respectively. Although wastewater effluents from both the marketplace and isolation center drains were found with the highest amount of genetic materials according to the mixed model, quantifiable SARS-CoV-2 RNAs were also identified in the other four sampling sites. Hence, wastewater samples of the marketplace in rural areas and isolation centers in urban areas can be considered the appropriate sampling sites to detect contagion hotspots. This is the first complete study to detect SARS-CoV-2 genetic components in wastewater samples collected from rural and urban areas for monitoring the COVID-19 pandemic. The results based on the study revealed a correlation between viral copy numbers in wastewater samples and SARS-CoV-2 positive cases reported by the Directorate General of Health Services (DGHS) as part of the national surveillance program for COVID-19 prevention. The findings of this study will help in setting strategies and guidelines for the selection of appropriate sampling sites, which will facilitate in development of comprehensive wastewater-based epidemiological systems for surveillance of rural and urban areas of low-income countries with inadequate sewage infrastructure.

An opinion on Wastewater-Based Epidemiological Monitoring (WBEM) with Clinical Diagnostic Test (CDT) for detecting high-prevalence areas of community COVID-19 Infections

Wastewater-Based Epidemiological Monitoring (WBEM) is an efficient surveillance tool during the COVID-19 pandemic as it meets all requirements of a complete monitoring system including early warning, tracking the current trend, prevalence of the disease, detection of genetic diversity as well asthe up-surging SARS-CoV-2 new variants with mutations from the wastewater samples. Subsequently, Clinical Diagnostic Test is widely acknowledged as the global gold standard method for disease monitoring, despite several drawbacks such as high diagnosis cost, reporting bias, and the difficulty of tracking asymptomatic patients (silent spreaders of the COVID-19 infection who manifest nosymptoms of the disease). In this current reviewand opinion-based study, we first propose a combined approach) for detecting COVID-19 infection in communities using wastewater and clinical sample testing, which may be feasible and effective as an emerging public health tool for the long-term nationwide surveillance system. The viral concentrations in wastewater samples can be used as indicatorsto monitor ongoing SARS-CoV-2 trends, predict asymptomatic carriers, and detect COVID-19 hotspot areas, while clinical sampleshelp in detecting mostlysymptomaticindividuals for isolating positive cases in communities and validate WBEM protocol for mass vaccination including booster doses for COVID-19.

GC-MS profiling of Bauhinia variegata major phytoconstituents with computational identification of potential lead inhibitors of SARS-CoV-2 Mpro

Severe acute respiratory syndrome coronavirus 2 was originally identified in Wuhan city of China in December 2019 and it spread rapidly throughout the globe, causing a threat to human life. Since targeted therapies are deficient, scientists all over the world have an opportunity to develop novel drug therapies to combat COVID-19. After the declaration of a global medical emergency, it was established that the Food and Drug Administration (FDA) could permit the use of emergency testing, treatments, and vaccines to decrease suffering, and loss of life, and restore the nation's health and security. The FDA has approved the use of remdesivir and its analogs as an antiviral medication, to treat COVID-19. The primary protease of SARS-CoV-2, which has the potential to regulate coronavirus proliferation, has been a viable target for the discovery of medicines against SARS-CoV-2. The present research deals with the in silico technique to screen phytocompounds from a traditional medicinal plant, Bauhinia variegata for potential inhibitors of the SARS-CoV-2 main protease. Dried leaves of the plant B. variegata were used to prepare aqueous and methanol extract and the constituents were analyzed using the GC-MS technique. A total of 57 compounds were retrieved from the aqueous and methanol extract analysis. Among these, three lead compounds (2,5 dimethyl 1-H Pyrrole, 2,3 diphenyl cyclopropyl methyl phenyl sulphoxide, and Benzonitrile m phenethyl) were shown to have the highest binding affinity (−5.719 to −5.580 kcal/mol) towards SARS-CoV-2 M^pro. The post MD simulation results also revealed the favorable confirmation and stability of the selected lead compounds with M^pro as per trajectory analysis. The Prime MM/GBSA binding free energy supports this finding, the top lead compound 2,3 diphenyl cyclopropyl methyl phenyl sulphoxide showed high binding free energy (−64.377 ± 5.24 kcal/mol) towards M^pro which reflects the binding stability of the molecule with M^pro. The binding free energy of the complexes was strongly influenced by His, Gln, and Glu residues. All of the molecules chosen are found to have strong pharmacokinetic characteristics and show drug-likeness properties. The lead compounds present acute toxicity (LD50) values ranging from 670 mg/kg to 2500 mg/kg; with toxicity classifications of 4 and 5 classes. Thus, these compounds could behave as probable lead candidates for treatment against SARS-CoV-2. However further in vitro and in vivo studies are required for the development of medication against SARS-CoV-2.

DNA aptamers inhibit SARS-CoV-2 spike-protein binding to hACE2 by an RBD- independent or dependent approach

Objective: Nobody knows when the COVID-19 pandemic will end or when and where the next coronavirus will outbreak. Therefore, it is still necessary to develop SARS-CoV-2 inhibitors for different variants or even the new coronavirus. Since SARS-CoV-2 uses its surface spike-protein to recognize hACE2, mediating its entry into cells, ligands that can specifically recognize the spike-protein have the potential to prevent infection. Methods: We have recently discovered DNA aptamers against the S2-domain of the WT spike-protein by exploiting the selection process called SELEX. After optimization, among all candidates, the aptamer S2A2C1 has the shortest sequence and the best binding affinity toward the S2-protein. More importantly, the S2A2C1 aptamer does not bind to the RBD of the spike-protein, but it efficiently blocks the spike-protein/hACE2 interaction, suggesting an RBD-independent inhibition approach. To further improve its performance, we conjugated the S2A2C1 aptamer with a reported anti-RBD aptamer, S1B6C3, using various linkers and constructed hetero-bivalent fusion aptamers. Binding affinities of mono and fusion aptamers against the spike-proteins were measured. The inhibition efficacies of mono and fusion aptamers to prevent the hACE2/spike-protein interaction were determined using ELISA. Results: Anti-spike-protein aptamers, including S2A2C1 and S1B6C3-A5-S2A2C1, maintained high binding affinity toward the WT, Delta, and Omicron spike-proteins and high inhibition efficacies to prevent them from binding to hACE2, rendering them well-suited as diagnostic and therapeutic molecular tools to target SARS-CoV-2 and its variants. Conclusions: Overall, we discovered the anti-S2 aptamer, S2A2C1, which inhibits the hACE2/spike-protein interaction via an RBD-independent approach. The anti-S2 and anti-RBD aptamers were conjugated to obtain the fusion aptamer, S1B6C3-A5-S2A2C1, which recognizes the spike-protein by an RBD-dependent approach. Our strategies, which discovered aptamer inhibitors targeting the highly conserved S2-protein, as well as the design of fusion aptamers, can be used to target new coronaviruses as they emerge.

MutCov: A pipeline for evaluating the effect of mutations in spike protein on infectivity and antigenicity of SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing an outbreak of coronavirus disease 2019 (COVID-19), is a major threat to public health worldwide. Previous studies have shown that the spike protein of SARS-CoV-2 determines viral infectivity and major antigenicity. However, the spike protein has been undergoing various mutations, which bring a great challenge to the prevention and treatment of COVID-19. Here we present the MutCov, a pipeline for evaluating the effect of mutations in spike protein on infectivity and antigenicity of SARS-CoV-2 by calculating the binding free energy between spike protein and angiotensin-converting enzyme 2 (ACE2) or neutralizing monoclonal antibody (mAb). The predicted infectivity and antigenicity were highly consistent with biologically experimental results, and demonstrated that the MutCov achieved good prediction performance. In conclusion, the MutCov is of high importance for systematically evaluating the effect of novel mutations and improving the prevention and treatment of COVID-19. The source code and installation instruction of MutCov are freely available at http://jianglab.org.cn/MutCov.

Interference of Chaga mushroom terpenoids with the attachment of SARS-CoV-2; in silico perspective

Finding a potent inhibitor to the pandemic SARS-CoV-2 is indispensable nowadays. Currently, in-silico methods work as expeditious investigators to screen drugs for possible repurposing or design new ones. Targeting one of the possible SARS-CoV-2 attachment and entry receptors, Glucose-regulated protein 78 (GRP78), is an approach of major interest. Recently, GRP78 was reported as a recognized representative in recognition of the latest variants of SARS-CoV-2. In this work, molecular docking and molecular dynamics simulations were performed on the host cell receptor GRP78. With its many terpenoid compounds, Chaga mushroom was tested as a potential therapeutic against the SARS-CoV-2 receptor, GRP78. Results revealed low binding energies (high affinities) toward the GRP78 substrate-binding domain β (SBDβ) of Chaga mushroom terpenoids. Even the highly specific cyclic peptide Pep42, which selectively targeted GRP78 over cancer cells in vivo, showed lower binding affinity against GRP78 SBDβ compared to the binding affinities of terpenoids. These are auspicious results that need to be tested experimentally. Intriguingly, terpenoids work as a double sword as they can be used to interfere with VUI 202,012/01, 501.V2, and B.1.1.248 variants of SARS-CoV-2 spike recognition.

Pseudanabaena galeata CCNP1313—Biological Activity and Peptides Production

Even cyanobacteria from ecosystems of low biodiversity, such as the Baltic Sea, can constitute a rich source of bioactive metabolites. Potent toxins, enzyme inhibitors, and anticancer and antifungal agents were detected in both bloom-forming species and less commonly occurring cyanobacteria. In previous work on the Baltic Pseudanabaena galeata CCNP1313, the induction of apoptosis in the breast cancer cell line MCF-7 was documented. Here, the activity of the strain was further explored using human dermal fibroblasts, African green monkey kidney, cancer cell lines (T47D, HCT-8, and A549ACE2/TMPRSS2) and viruses (SARS-CoV-2, HCoV-OC43, and WNV). In the tests, extracts, chromatographic fractions, and the main components of the P. galeata CCNP1313 fractions were used. The LC-MS/MS analyses of the tested samples led to the detection of forty-five peptides. For fourteen of the new peptides, putative structures were proposed based on MS/MS spectra. Although the complex samples (i.e., extracts and chromatographic fractions) showed potent cytotoxic and antiviral activities, the effects of the isolated compounds were minor. The study confirmed the significance of P. galeata CCNP1313 as a source of metabolites with potent activity. It also illustrated the difficulties in assigning the observed biological effects to specific metabolites, especially when they are produced in minute amounts.

Molecular Docking and Dynamics Studies to Explore Effective Inhibitory Peptides Against the Spike Receptor Binding Domain of SARS-CoV-2

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a pandemic due to the high transmission and mortality rate of this virus. The world health and economic sectors have been severely affected by this deadly virus, exacerbated by the lack of sufficient efficient vaccines. The design of effective drug candidates and their rapid development is necessary to combat this virus. In this study, we selected 23 antimicrobial peptides from the literature and predicted their structure using PEP-FOLD 3.5. In addition, we docked them to the SARS-CoV-2 spike protein receptor-binding domain (RBD) to study their capability to inhibit the RBD, which plays a significant role in virus binding, fusion and entry into the host cell. We used several docking programs including HDOCK, HPEPDOCK, ClusPro, and HawkDock to calculate the binding energy of the protein-peptide complexes. We identified four peptides with high binding free energy and docking scores. The docking results were further verified by molecular dynamics (MD) simulations to characterize the protein-peptide complexes in terms of their root-mean-square fluctuation (RMSF), root-mean-square deviation (RMSD), radius of gyration (Rg), solvent-accessible surface area (SASA), and hydrogen bond formation. Allergenicity and toxicity predictions suggested that the peptides we identified were non-allergenic and non-toxic. This study suggests that these four antimicrobial peptides could inhibit the RBD of SARS-CoV-2. Future in vitro and in vivo studies are necessary to confirm this.