Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening.

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

Targeting autophagy regulation in NLRP3 inflammasome-mediated lung inflammation in COVID-19.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Emerging evidence indicates that the NOD-, LRR- and pyrin domain-containing protein 3 (NLRP3) inflammasome is activated, which results in a cytokine storm at the late stage of COVID-19. Autophagy regulation is involved in the infection and replication of SARS-CoV-2 at the early stage and the inhibition of NLRP3 inflammasome-mediated lung inflammation at the late stage of COVID-19. Here, we discuss the autophagy regulation at different stages of COVID-19. Specifically, we highlight the therapeutic potential of autophagy activators in COVID-19 by inhibiting the NLRP3 inflammasome, thereby avoiding the cytokine storm. We hope this review provides enlightenment for the use of autophagy activators targeting the inhibition of the NLRP3 inflammasome, specifically the combinational therapy of autophagy modulators with the inhibitors of the NLRP3 inflammasome, antiviral drugs, or anti-inflammatory drugs in the fight against COVID-19.

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

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

Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks.

The understanding of therapeutic properties is important in drug repositioning and drug discovery. However, chemical or clinical trials are expensive and inefficient to characterize the therapeutic properties of drugs. Recently, artificial intelligence (AI)-assisted algorithms have received extensive attention for discovering the potential therapeutic properties of drugs and speeding up drug development. In this study, we propose a new method based on GraphSAGE and clustering constraints (DRGCC) to investigate the potential therapeutic properties of drugs for drug repositioning. First, the drug structure features and disease symptom features are extracted. Second, the drug-drug interaction network and disease similarity network are constructed according to the drug-gene and disease-gene relationships. Matrix factorization is adopted to extract the clustering features of networks. Then, all the features are fed to the GraphSAGE to predict new associations between existing drugs and diseases. Benchmark comparisons on two different datasets show that our method has reliable predictive performance and outperforms other six competing. We have also conducted case studies on existing drugs and diseases and aimed to predict drugs that may be effective for the novel coronavirus disease 2019 (COVID-19). Among the predicted anti-COVID-19 drug candidates, some drugs are being clinically studied by pharmacologists, and their binding sites to COVID-19-related protein receptors have been found via the molecular docking technology.

SARS-CoV-2-specific T cells in unexposed adults display broad trafficking potential and cross-react with commensal antigens.

The baseline composition of T cells directly affects later response to pathogens, but the complexity of precursor states remains poorly defined. Here, we examined the baseline state of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific T cells in unexposed individuals. SARS-CoV-2-specific CD4+ T cells were identified in prepandemic blood samples by major histocompatibility complex (MHC) class II tetramer staining and enrichment. Our data revealed a substantial number of SARS-CoV-2-specific T cells that expressed memory phenotype markers. Integrated phenotypic analyses demonstrated diverse preexisting memory states that included cells with distinct polarization features and trafficking potential to barrier tissues. T cell clones generated from tetramer-labeled cells cross-reacted with antigens from commensal bacteria in the skin and gastrointestinal tract. Direct ex vivo tetramer staining for one spike-specific population showed a similar level of cross-reactivity to sequences from endemic coronavirus and commensal bacteria. These data highlight the complexity of precursor T cell repertoire and implicate noninfectious exposures to common microbes as a key factor that shapes human preexisting immunity to SARS-CoV-2.

COVID-19 Vaccine Provider Availability and Vaccination Coverage Among Children Aged 5-11 Years - United States, November 1, 2021-April 25, 2022.

COVID-19 can lead to severe outcomes in children, including multisystem inflammatory syndrome, hospitalization, and death (1,2). On November 2, 2021, the Advisory Committee on Immunization Practices issued an interim recommendation for use of the BNT162b2 (Pfizer-BioNTech) vaccine in children aged 5-11 years for the prevention of COVID-19; however, vaccination coverage in this age group remains low (3). As of June 7, 2022, 36.0% of children aged 5-11 years in the United States had received ≥1 of COVID-19 vaccine (3). Among factors that might influence vaccination coverage is the availability of vaccine providers (4). To better understand how provider availability has affected COVID-19 vaccination coverage among children aged 5-11 years, CDC analyzed data on active COVID-19 vaccine providers and county-level vaccine administration data during November 1, 2021-April 25, 2022. Among 2,586 U.S. counties included in the analysis, 87.5% had at least one active COVID-19 vaccine provider serving children aged 5-11 years. Among the five assessed active provider types, most counties had at least one pharmacy (69.1%) or public health clinic (61.3%), whereas fewer counties had at least one pediatric clinic (29.7%), family medicine clinic (29.0%), or federally qualified health center (FQHC)* (22.8%). Median county-level vaccination coverage was 14.5% (IQR = 8.9%-23.6%). After adjusting for social vulnerability index (SVI)† and urbanicity, the analysis found that vaccination coverage among children aged 5-11 years was higher in counties with at least one active COVID-19 vaccine provider than in counties with no active providers (adjusted rate ratio [aRR] = 1.66). For each provider type, presence of at least one provider in the county was associated with higher coverage; the largest difference in vaccination coverage was observed between counties with and without pediatric clinics (aRR = 1.37). Ensuring broad access to COVID-19 vaccines, in addition to other strategies to address vaccination barriers, could help increase vaccination coverage among children aged 5-11 years.

Potential natural products that target the SARS-CoV-2 spike protein identified by structure-based virtual screening, isothermal titration calorimetry and lentivirus particles pseudotyped (Vpp) infection assay.

BACKGROUND AND AIM: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters cells through the binding of the viral spike protein with human angiotensin-converting enzyme 2 (ACE2), resulting in the development of coronavirus disease 2019 (COVID-19). To date, few antiviral drugs are available that can effectively block viral infection. This study aimed to identify potential natural products from Taiwan Database of Extracts and Compounds (TDEC) that may prevent the binding of viral spike proteins with human ACE2 proteins. METHODS: The structure-based virtual screening was performed using the AutoDock Vina program within PyRX software, the binding affinities of compounds were verified using isothermal titration calorimetry (ITC), the inhibitions of SARS-CoV-2 viral infection efficacy were examined by lentivirus particles pseudotyped (Vpp) infection assay, and the cell viability was tested by 293T cell in MTT assay. RESULTS AND CONCLUSION: We identified 39 natural products targeting the viral receptor-binding domain (RBD) of the SARS-CoV-2 spike protein in silico. In ITC binding assay, dioscin, celastrol, saikosaponin C, epimedin C, torvoside K, and amentoflavone showed dissociation constant (K d) = 0.468 µM, 1.712 µM, 6.650 µM, 2.86 µM, 3.761 µM and 4.27 µM, respectively. In Vpp infection assay, the compounds have significantly and consistently inhibition with the 50-90% inhibition of viral infection efficacy. In cell viability, torvoside K, epimedin, amentoflavone, and saikosaponin C showed IC50 > 100 µM; dioscin and celastrol showed IC50 = 1.5625 µM and 0.9866 µM, respectively. These natural products may bind to the viral spike protein, preventing SARS-CoV-2 from entering cells. SECTION 1: Natural Products. TAXONOMY CLASSIFICATION BY EVISE: SARS-CoV-2, Structure-Based Virtual Screening, Isothermal Titration Calorimetry and Lentivirus Particles Pseudotyped (Vpp) Infection Assay, in silico and in vitro study.

Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture.

COVID-19 is a contagious infection that has severe effects on the global economy and our daily life. Accurate diagnosis of COVID-19 is of importance for consultants, patients, and radiologists. In this study, we use the deep learning network AlexNet as the backbone, and enhance it with the following two aspects: 1) adding batch normalization to help accelerate the training, reducing the internal covariance shift; 2) replacing the fully connected layer in AlexNet with three classifiers: SNN, ELM, and RVFL. Therefore, we have three novel models from the deep COVID network (DC-Net) framework, which are named DC-Net-S, DC-Net-E, and DC-Net-R, respectively. After comparison, we find the proposed DC-Net-R achieves an average accuracy of 90.91% on a private dataset (available upon email request) comprising of 296 images while the specificity reaches 96.13%, and has the best performance among all three proposed classifiers. In addition, we show that our DC-Net-R also performs much better than other existing algorithms in the literature. Supplementary Information: The online version contains supplementary material available at 10.1007/s11390-020-0679-8.

Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.

Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests and saving critical time for disease management and control. Thus, this review article focuses on cascading numerous deep learning-based COVID-19 computerized tomography (CT) imaging diagnosis research, providing a baseline for future research. Compared to previous review articles on the topic, this study pigeon-holes the collected literature very differently (i.e., its multi-level arrangement). For this purpose, 71 relevant studies were found using a variety of trustworthy databases and search engines, including Google Scholar, IEEE Xplore, Web of Science, PubMed, Science Direct, and Scopus. We classify the selected literature in multi-level machine learning groups, such as supervised and weakly supervised learning. Our review article reveals that weak supervision has been adopted extensively for COVID-19 CT diagnosis compared to supervised learning. Weakly supervised (conventional transfer learning) techniques can be utilized effectively for real-time clinical practices by reusing the sophisticated features rather than over-parameterizing the standard models. Few-shot and self-supervised learning are the recent trends to address data scarcity and model efficacy. The deep learning (artificial intelligence) based models are mainly utilized for disease management and control. Therefore, it is more appropriate for readers to comprehend the related perceptive of deep learning approaches for the in-progress COVID-19 CT diagnosis research.

Blockchain-Based Distributed Information Hiding Framework for Data Privacy Preserving in Medical Supply Chain Systems.

Medical supply chain communication networks engender critical information and data. Notably in the COVID era, inner personal and private information is being shared between healthcare providers regarding the medical supply chain. In recent years, multiple cyber-attacks have targeted medical supply chain communication networks due to their lack of security measures. In the era where cyber-attacks are cheaper and easier due to the computational power and various algorithms available for malicious uses, security, and data privacy requires intensive and higher measures. On the other hand, Information Hiding Techniques (IHT) compromise various advanced methods to hide sensitive information from being disclosed to malicious nodes. Moreover, with the support of Blockchain, IHT can bring higher security and the required privacy levels. In this paper, we propose the implementation of Blockchain and smart contract with the information hiding technique to enhance the security and privacy of data communication in critical systems, such as smart healthcare supply chain communication networks. Results show the feasibility of the framework using Hyperledger smart contract along with the desired security level.

A self-assembling nanoparticle vaccine targeting the conserved epitope of influenza virus hemagglutinin stem elicits a cross-protective immune response.

Various vaccine strategies have been developed to provide broad protection against diverse influenza viruses. The hemagglutinin (HA) stem is the major potential target of these vaccines. Enhancing immunogenicity and eliciting cross-protective immune responses are critical for HA stem-based vaccine designs. In this study, the A helix (Ah) and CD helix (CDh) from the HA stem were fused with ferritin, individually, or in tandem, yielding Ah-f, CDh-f and (A + CD)h-f nanoparticles (NPs), respectively. These NPs were produced through a prokaryotic expression system. After three immunizations with AS03-adjuvanted NPs in BALB/c mice via the subcutaneous route, CDh-f and (A + CD)h-f induced robust humoral and cellular immune responses. Furthermore, CDh-f and (A + CD)h-f conferred complete protection against a lethal challenge of H3N2 virus, while no remarkable immune responses and protective effects were detected in the Ah-f group. These results indicate that the CDh-based nanovaccine represents a promising vaccine platform against influenza, and the epitope-conjugated ferritin NPs may be a potential vaccine platform against other infectious viruses, such as SARS-COV-2.

Coronavirus GenBrowser for monitoring the transmission and evolution of SARS-CoV-2.

Genomic epidemiology is important to study the COVID-19 pandemic, and more than two million severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic sequences were deposited into public databases. However, the exponential increase of sequences invokes unprecedented bioinformatic challenges. Here, we present the Coronavirus GenBrowser (CGB) based on a highly efficient analysis framework and a node-picking rendering strategy. In total, 1,002,739 high-quality genomic sequences with the transmission-related metadata were analyzed and visualized. The size of the core data file is only 12.20 MB, highly efficient for clean data sharing. Quick visualization modules and rich interactive operations are provided to explore the annotated SARS-CoV-2 evolutionary tree. CGB binary nomenclature is proposed to name each internal lineage. The pre-analyzed data can be filtered out according to the user-defined criteria to explore the transmission of SARS-CoV-2. Different evolutionary analyses can also be easily performed, such as the detection of accelerated evolution and ongoing positive selection. Moreover, the 75 genomic spots conserved in SARS-CoV-2 but non-conserved in other coronaviruses were identified, which may indicate the functional elements specifically important for SARS-CoV-2. The CGB was written in Java and JavaScript. It not only enables users who have no programming skills to analyze millions of genomic sequences, but also offers a panoramic vision of the transmission and evolution of SARS-CoV-2.

A randomized trial of icosapent ethyl in ambulatory patients with COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic remains a source of considerable morbidity and mortality throughout the world. Therapeutic options to reduce symptoms, inflammatory response, or disease progression are limited. This randomized open-label trial enrolled 100 ambulatory patients with symptomatic COVID-19 in Toronto, Canada. Results indicate that icosapent ethyl (8 g daily for 3 days followed by 4 g daily for 11 days) significantly reduced high-sensitivity C-reactive protein (hs-CRP) and improved symptomatology compared with patients assigned to usual care. Specifically, the primary biomarker endpoint, change in hs-CRP, was significantly reduced by 25% among treated patients (-0.5 mg/L, interquartile range [IQR] [-6.9,0.4], within-group p = 0.011). Conversely, a non-significant 5.6% reduction was observed among usual care patients (-0.1 mg/L, IQR [-3.2,1.7], within-group p = 0.51). An unadjusted between-group primary biomarker analysis was non-significant (p = 0.082). Overall, this report provides evidence of an early anti-inflammatory effect of icosapent ethyl in a modest sample, including an initial well-tolerated loading dose, in symptomatic outpatients with COVID-19. ClinicalTrials.gov Identifier: NCT04412018.

Apocalyptic Spirituality and Advent in a Time of Corona Pandemic

The World Health Organization (WHO) first declared on January 12, 2020 an outbreak of novel coronavirus, later known as COVID-19, in Wuhan, China,5 and on January 30 a Public Health Emergency of International Concern (PHEIC), after having received reports of 7,794 confirmed cases in 19 countries including China,6 and on March 11 a pandemic.7 By then, there were already 118,319 confirmed cases and 4,292 deaths in 114 countries/territories. Within a year, there are now more than 75 million cases, over 1.65 million deaths in 191 countries/territories.8 APOCALYPTIC EVENT In the early months after the announcement by WHO of an outbreak of novel coronavirus in China, in spite of stern warnings from medical experts, many thought it was only a little more dangerous than seasonal influenza, having compared superficially the novel coronavirus with seasonal influenza viruses and the SARS (Severe Acute Respiratory Syndrome) in 2003.9 President Trump, for instance, was reported saying on March 9, 2020 that "So last year 37,000 Americans died from the common Flu. See PDF ] On October 13, 2020, a joint statement issued by ILO (International Labour Organization), FAO (The Food and Agriculture Organization), IFAD (International Fund for Agricultural Development) and WHO highlighted "the intertwined health and social and economic impacts of the pandemic." The word apocalypse (apokálypsis) in Greek means literally an uncovering, a revelation or disclosure of something. [...]to reflect on the corona pandemic as an apocalyptic event is a spiritual discernment of its revealing function in uncovering deeper truth and reality.

Risk stratification by long non-coding RNAs profiling in COVID-19 patients.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global pandemic worldwide. Long non-coding RNAs (lncRNAs) are a subclass of endogenous, non-protein-coding RNA, which lacks an open reading frame and is more than 200 nucleotides in length. However, the functions for lncRNAs in COVID-19 have not been unravelled. The present study aimed at identifying the related lncRNAs based on RNA sequencing of peripheral blood mononuclear cells from patients with SARS-CoV-2 infection as well as health individuals. Overall, 17 severe, 12 non-severe patients and 10 healthy controls were enrolled in this study. Firstly, we reported some altered lncRNAs between severe, non-severe COVID-19 patients and healthy controls. Next, we developed a 7-lncRNA panel with a good differential ability between severe and non-severe COVID-19 patients using least absolute shrinkage and selection operator regression. Finally, we observed that COVID-19 is a heterogeneous disease among which severe COVID-19 patients have two subtypes with similar risk score and immune score based on lncRNA panel using iCluster algorithm. As the roles of lncRNAs in COVID-19 have not yet been fully identified and understood, our analysis should provide valuable resource and information for the future studies.

CoronaPep: An Anti-Coronavirus Peptide Generation Tool.

The novel coronavirus (COVID-19) infections have adopted the shape of a global pandemic now, demanding an urgent vaccine design. The current work reports contriving an anti-coronavirus peptide scanner tool to discern anti-coronavirus targets in the embodiment of peptides. The proffered CoronaPep tool features the fast fingerprinting of the anti-coronavirus target serving supreme prominence in the current bioinformatics research. The anti-coronavirus target protein sequences reported from the current outbreak are scanned against the anti-coronavirus target data-sets via CORONAPEP which provides precision-based anti-coronavirus peptides. This tool is specifically for the coronavirus data, which can predict peptides from the whole genome, or a gene or protein's list. Besides it is relatively fast, accurate, userfriendly and can generate maximum output from the limited information. The availability of tools like CORONAPEP will immeasurably perquisite researchers in the discipline of oncology and structure-based drug design.

A novel virtual screening procedure identifies Pralatrexate as inhibitor of SARS-CoV-2 RdRp and it reduces viral replication in vitro.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus poses serious threats to the global public health and leads to worldwide crisis. No effective drug or vaccine is readily available. The viral RNA-dependent RNA polymerase (RdRp) is a promising therapeutic target. A hybrid drug screening procedure was proposed and applied to identify potential drug candidates targeting RdRp from 1906 approved drugs. Among the four selected market available drug candidates, Pralatrexate and Azithromycin were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC50 values of 0.008µM and 9.453 µM, respectively. For the first time, our study discovered that Pralatrexate is able to potently inhibit SARS-CoV-2 replication with a stronger inhibitory activity than Remdesivir within the same experimental conditions. The paper demonstrates the feasibility of fast and accurate anti-viral drug screening for inhibitors of SARS-CoV-2 and provides potential therapeutic agents against COVID-19.

Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov.

A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. Although there are some drugs to treat 2019-nCoV, there is no proper scientific evidence about its activity on the virus. It is of high significance to develop a drug that can combat the virus effectively to save valuable human lives. It usually takes a much longer time to develop a drug using traditional methods. For 2019-nCoV, it is now better to rely on some alternative methods such as deep learning to develop drugs that can combat such a disease effectively since 2019-nCoV is highly homologous to SARS-CoV. In the present work, we first collected virus RNA sequences of 18 patients reported to have 2019-nCoV from the public domain database, translated the RNA into protein sequences, and performed multiple sequence alignment. After a careful literature survey and sequence analysis, 3C-like protease is considered to be a major therapeutic target and we built a protein 3D model of 3C-like protease using homology modeling. Relying on the structural model, we used a pipeline to perform large scale virtual screening by using a deep learning based method to accurately rank/identify protein-ligand interacting pairs developed recently in our group. Our model identified potential drugs for 2019-nCoV 3C-like protease by performing drug screening against four chemical compound databases (Chimdiv, Targetmol-Approved_Drug_Library, Targetmol-Natural_Compound_Library, and Targetmol-Bioactive_Compound_Library) and a database of tripeptides. Through this paper, we provided the list of possible chemical ligands (Meglumine, Vidarabine, Adenosine, D-Sorbitol, D-Mannitol, Sodium_gluconate, Ganciclovir and Chlorobutanol) and peptide drugs (combination of isoleucine, lysine and proline) from the databases to guide the experimental scientists and validate the molecules which can combat the virus in a shorter time.