Identification of Darunavir Derivatives for Inhibition of SARS-CoV-2 3CLpro.

The effective antiviral agents that treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are urgently needed around the world. The 3C-like protease (3CLpro) of SARS-CoV-2 plays a pivotal role in virus replication; it also has become an important therapeutic target for the infection of SARS-CoV-2. In this work, we have identified Darunavir derivatives that inhibit the 3CLpro through a high-throughput screening method based on a fluorescence resonance energy transfer (FRET) assay in vitro. We found that the compounds 29# and 50# containing polyphenol and caffeine derivatives as the P2 ligand, respectively, exhibited favorable anti-3CLpro potency with EC50 values of 6.3 µM and 3.5 µM and were shown to bind to SARS-CoV-2 3CLpro in vitro. Moreover, we analyzed the binding mode of the DRV in the 3CLpro through molecular docking. Importantly, 29# and 50# exhibited a similar activity against the protease in Omicron variants. The inhibitory effect of compounds 29# and 50# on the SARS-CoV-2 3CLpro warrants that they are worth being the template to design functionally improved inhibitors for the treatment of COVID-19.

Synthesis and enzymatic inhibition effects of thiazolidinedione 3C-like protease inhibitors

The 3C-like protease (also known as Mpro) plays a key role in SARS-CoV-2 replication and has similar substrates across mutant coronaviruses, making it an ideal drug target. We synthesized 19 thiazolidinedione derivatives via the Knoevenagel condensations and Mitsunobu reactions as potential 3C-like protease inhibitors. The activity of these inhibitors is screened in vitro by employing the enzymatic screening model of 3C-like protease using fluorescence resonance energy transfer. Dithiothreitol is included in the enzymatic reaction system to avoid non-specific enzymatic inhibition. Active inhibitors with diverse activity are found in this series of compounds, and two representative inhibitors with potent inhibitory activity are highlighted. © The Author(s) 2023.

Research progress on mechanism and clinic application of traditional Chinese medicine against coronavirus

Coronaviruses (CoVs) are the largest positive-strand RNA viruses discovered, with high variability and high infectivity. There are seven kinds of CoVs that can infect humans so far. Among them, severe acute respiratory syndrome coronavirus (SARS-CoV) in 2003, middle east respiratory syndrome coronavirus (MERS-CoV) in 2012 and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019 have caused global outbreaks, posing a serious threat to global public health security. Research on CoVs infection has never stopped, and the current treatment methods mainly focus on improving symptoms. Traditional Chinese medicine (TCM) has a long history and rich experience in preventing and treating various diseases. In terms of anti-CoVs, TCM has attracted much attention because of its multi-CoVs and multi-targets, significant antiviral effect and few side effect. TCM extracts or their compounds can exert anti-CoVs effects by directly or indirectly inhibiting the invasion, replication, assembly of CoVs, regulating immunity and inhibiting inflammation. This article systematically reviews the mechanism and clinical application of TCM in anti-CoVs and alleviating virus-induced symptoms, in order to provide theoretical reference for the research and development of anti-coronavirus drugs. © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.

Cytotoxic hexadepsipeptides and anti-coronaviral 4-hydroxy-2-pyridones from an endophytic Fusarium sp.

Three new hexadepsipeptides (1-3), along with beauvericin (4), beauvericin D (5), and four 4-hydroxy-2-pyridone derivatives (6-9) were isolated from the endophytic fungus Fusarium sp. CPCC 400857 that derived from the stem of tea plant. Their structures were determined by extensive 1D and 2D NMR, and HRESIMS analyses. The absolute configuration of hexadepsipeptides were elucidated by the advanced Marfey's method and chiral HPLC analysis. Compounds 4, and 7-9 displayed the cytotoxicity against human pancreatic cancer cell line, AsPC-1 with IC50 values ranging from 3.45 to 29.69 µM, and 7 and 8 also showed the antiviral activity against the coronavirus (HCoV-OC43) with IC50 values of 13.33 and 6.65 µM, respectively.

Synthesis of Novel 1-Oxo-2,3,4-trisubstituted Tetrahydroisoquinoline Derivatives, Bearing Other Heterocyclic Moieties and Comparative Preliminary Study of Anti-Coronavirus Activity of Selected Compounds.

A series of novel 1-oxo-2,3,4-trisubstituted tetrahydroisoquinoline (THIQ) derivatives bearing other heterocyclic moieties in their structure were synthesized based on the reaction between homophthalic anhydride and imines. Initial studies were carried out to establish the anti-coronavirus activity of some of the newly obtained THIQ-derivatives against two strains of human coronavirus-229E and OC-43. Their antiviral activity was compared with that of their close analogues, piperidinones and thiomorpholinones, previously synthesized in our group, with aim to expand the range of the tested representative sample and to obtain valuable preliminary information about biological properties of a wider variety of compounds.

Identifying the Anti-MERS-CoV and Anti-HcoV-229E Potential Drugs from the Ginkgo biloba Leaves Extract and Its Eco-Friendly Synthesis of Silver Nanoparticles.

The present study aimed to estimate the antiviral activities of Ginkgo biloba (GB) leaves extract and eco-friendly free silver nanoparticles (Ag NPs) against the MERS-CoV (Middle East respiratory syndrome-coronavirus) and HCoV-229E (human coronavirus 229E), as well as isolation and identification of phytochemicals from GB. Different solvents and high-performance liquid chromatography (HPLC) were used to extract and identify flavonoids and phenolic compounds from GB leaves. The green, silver nanoparticle synthesis was synthesized from GB leaves aqueous extract and investigated for their possible effects as anti-coronaviruses MERS-CoV and HCoV-229E using MTT assay protocol. To verify the synthesis of Ag NPs, several techniques were employed, including X-ray diffraction (XRD), scan, transmission electron microscopy, FT-IR, and UV-visible spectroscopy. The highest contents of flavonoids and phenolic compounds were recorded for acetone, methanol, and ethanol as mixtures with water, in addition to pure water. HPLC flavonoids were detected as apegenin, luteolin, myricetin, and catechin, while HPLC phenolic compounds were pyrogallol, caffeic acid, gallic acid, and ellagic acid. In addition, our results revealed that Ag NPs were produced through the shift from yellow to dark brown. TEM examination of Ag NPs revealed spherical nanoparticles with mean sizes ranging from 5.46 to 19.40 nm and an average particle diameter of 11.81 nm. A UV-visible spectrophotometric investigation revealed an absorption peak at λ max of 441.56 nm. MTT protocol signified the use of GB leaves extract as an anti-coronavirus to be best from Ag NPs because GB extract had moderate anti-MERS-CoV with SI = 8.94, while had promising anti-HCov-229E, with an SI of 21.71. On the other hand, Ag NPs had a mild anti-MERS-CoV with SI = 4.23, and a moderate anti-HCoV-229E, with an SI of 7.51.

Research progress on mechanism and clinic application of traditional Chinese medicine against coronavirus

Coronaviruses (CoVs) are the largest positive-strand RNA viruses discovered, with high variability and high infectivity. There are seven kinds of CoVs that can infect humans so far. Among them, severe acute respiratory syndrome coronavirus (SARS-CoV) in 2003, middle east respiratory syndrome coronavirus (MERS-CoV) in 2012 and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019 have caused global outbreaks, posing a serious threat to global public health security. Research on CoVs infection has never stopped, and the current treatment methods mainly focus on improving symptoms. Traditional Chinese medicine (TCM) has a long history and rich experience in preventing and treating various diseases. In terms of anti-CoVs, TCM has attracted much attention because of its multi-CoVs and multi-targets, significant antiviral effect and few side effect. TCM extracts or their compounds can exert anti-CoVs effects by directly or indirectly inhibiting the invasion, replication, assembly of CoVs, regulating immunity and inhibiting inflammation. This article systematically reviews the mechanism and clinical application of TCM in anti-CoVs and alleviating virus-induced symptoms, in order to provide theoretical reference for the research and development of anti-coronavirus drugs. Copyright © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.

IPFS based storage Authentication and access control model with optimization enabled deep learning for intrusion detection

Network security has benefited from intrusion detection, which may spot unexpected threats from network traffic. Modern methods for detecting network anomalies typically rely on conventional machine learning models. The human construction of traffic features that these systems mainly rely on, which is no longer relevant in the age of big data, results in relatively low accuracy and certain exceptional features. A storage authentication and access control model based on Interplanetary File System (IPFS) and a network intrusion detection system based on Chronological Anticorona Virus Optimization are hence the main goals of this research (CACVO-based DRN).The setup, user registration, initialization, data encryption and storage, authentication, testing, access control, and decryption stages are used here to perform the blockchain authentication and access control. After then, DRN is used to perform network intrusion detection. To do this, the recorded data log file is initially sent to the feature fusion module, which uses Deep Belief Network and hybrid correlation factors (DBN). After the feature fusion is complete, the proposed optimization technique, CACVO, which was recently developed by fusing the Chronological Concept with Anti Corona virus Optimization (ACVO) algorithm, is used to perform intrusion detection utilizing DRN. The experimental outcome shows that, based on the f-measure value of 0.939 and 0.938, respectively, the developed model achieved greater performance.

Function of Anti-CoV Structure Using INH [1-6]- Tyr160-Met161-His162 Complex

Coronaviruses (CoVs), positive-stranded RNA viruses, can infect humans and multiple species of animals, cause enteric, respiratory, and central nervous system diseases in many species, and are attractive targets for anti-CoV drug design through a pivotal role in viral gene expression and replication through the proteolytic processing of replicase polyproteins. In this work, it has been investigated the junction of six inhibitors including N-[[4-(4-methylpiperazin-1-yl)phenyl]methyl]-1,2-oxazole-5-carboxamide (INH1), NSC 158362 (INH2), JMF 1586 (INH3), (N-(2-aminoethyl)-1-1ziridine-ethanamine) (INH4), [(Z)-1-thiophen-2-ylethylideneamino]thiourea (INH5), and Vanillinbananin (INH6) to coronavirus by forming the complexes of inhibitor-CoV through the hydrogen bonding using the physicochemical properties of the heat of formation, Gibbs free energy, electronic energy, the charge distribution of active parts in the hydrogen bonding, NMR estimation of inhibitor jointed to the database amino acids fragment of Tyr-Met-His as the selective zone of the CoV, positive frequency and intensity of different normal modes of these structures. The theoretical calculations were done at various levels of theory to gain more accurate equilibrium geometrical results. A comparison of these structures with two configurations provides new insights for the design of substrate-based inhibitors targeting CoV. This indicates a feasible model for designing wide-spectrum inhibitors against CoV-associated diseases. The structure-based optimization of these structures has yielded two more efficacious lead compounds, N and O atoms, through forming the hydrogen bonding (H-bonding) with potent inhibition against CoV (Tyr160-Met161-His162), which has been abbreviated as TMH in this work.

Repurposing of HIV/HCV protease inhibitors against SARS-CoV-2 3CLpro.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pathogen that caused the global COVID-19 outbreak. The 3C-like protease (3CL^pro) of SARS-CoV-2 plays a key role in virus replication and has become an ideal target for antiviral drug design. In this work, we have employed bioluminescence resonance energy transfer (BRET) technology to establish a cell-based assay for screening inhibitors against SARS-CoV-2 3CL^pro, and then applied the assay to screen a collection of known HIV/HCV protease inhibitors. Our results showed that the assay is capable of quantification of the cleavage efficiency of 3CL^pro with good reproducibility (Z' factor is 0.59). Using the assay, we found that 9 of 26 protease inhibitors effectively inhibited the activity of SARS-CoV-2 3CL^pro in a dose-dependent manner. Among them, four compounds exhibited the ability to bind to 3CL^proin vitro. HCV protease inhibitor simeprevir showed the most potency against 3CL^pro with an EC₅₀ vale of 2.6 µM, bound to the active site pocket of 3CL^pro in a predicted model, and importantly, exhibited a similar activity against the protease containing the mutations P132H in Omicron variants. Taken together, this work demonstrates the feasibility of using the cell-based BRET assay for screening 3CL^pro inhibitors and supports the potential of simeprevir for the development of 3CL^pro inhibitors.

An approach for DoS attack detection in cloud computing using sine cosine anti coronavirus optimized deep maxout network

Purpose The Denial of Service (DoS) attack is a category of intrusion that devours various services and resources of the organization by the dispersal of unusable traffic, so that reliable users are not capable of getting benefit from the services. In general, the DoS attackers preserve their independence by collaborating several victim machines and following authentic network traffic, which makes it more complex to detect the attack. Thus, these issues and demerits faced by existing DoS attack recognition schemes in cloud are specified as a major challenge to inventing a new attack recognition method. Design/methodology/approach This paper aims to detect DoS attack detection scheme, termed as sine cosine anti coronavirus optimization (SCACVO)-driven deep maxout network (DMN). The recorded log file is considered in this method for the attack detection process. Significant features are chosen based on Pearson correlation in the feature selection phase. The over sampling scheme is applied in the data augmentation phase, and then the attack detection is done using DMN. The DMN is trained by the SCACVO algorithm, which is formed by combining sine cosine optimization and anti-corona virus optimization techniques. Findings The SCACVO-based DMN offers maximum testing accuracy, true positive rate and true negative rate of 0.9412, 0.9541 and 0.9178, respectively. Originality/value The DoS attack detection using the proposed model is accurate and improves the effectiveness of the detection.

The SARS-CoV-2 main protease (Mpro): Structure, function, and emerging therapies for COVID-19.

The main proteases (Mpro), also termed 3-chymotrypsin-like proteases (3CLpro), are a class of highly conserved cysteine hydrolases in ß-coronaviruses. Increasing evidence has demonstrated that 3CLpros play an indispensable role in viral replication and have been recognized as key targets for preventing and treating coronavirus-caused infectious diseases, including COVID-19. This review is focused on the structural features and biological function of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease Mpro (also known as 3CLpro), as well as recent advances in discovering and developing SARS-CoV-2 3CLpro inhibitors. To better understand the characteristics of SARS-CoV-2 3CLpro inhibitors, the inhibition activities, inhibitory mechanisms, and key structural features of various 3CLpro inhibitors (including marketed drugs, peptidomimetic, and non-peptidomimetic synthetic compounds, as well as natural compounds and their derivatives) are summarized comprehensively. Meanwhile, the challenges in this field are highlighted, while future directions for designing and developing efficacious 3CLpro inhibitors as novel anti-coronavirus therapies are also proposed. Collectively, all information and knowledge presented here are very helpful for understanding the structural features and inhibitory mechanisms of SARS-CoV-2 3CLpro inhibitors, which offers new insights or inspiration to medicinal chemists for designing and developing more efficacious 3CLpro inhibitors as novel anti-coronavirus agents.

Synthesis, Antiviral, and Antibacterial Activity of the Glycyrrhizic Acid and Glycyrrhetinic Acid Derivatives.

Glycyrrhizic acid and its primary metabolite glycyrrhetinic acid, are the main active ingredients in the licorice roots (glycyrrhiza species), which are widely used in several countries of the world, especially in east asian countries (China, Japan). These ingredients and their derivatives play an important role in treating many diseases, especially infectious diseases such as COVID-19 and hepatic infections. This review aims to summarize the different ways of synthesising the amide derivatives of glycyrrhizic acid and the main ways to synthesize the glycyrrhitinic acid derivatives. Also, to determine the main biological and pharmacological activity for these compounds from the previous studies to provide essential data to researchers for future studies. Supplementary Information: The online version contains supplementary material available at 10.1134/S1068162022050132.

Anti-coronavirus optimization algorithm.

This paper introduces a new swarm intelligence strategy, anti-coronavirus optimization (ACVO) algorithm. This algorithm is a multi-agent strategy, in which each agent is a person that tries to stay healthy and slow down the spread of COVID-19 by observing the containment protocols. The algorithm composed of three main steps: social distancing, quarantine, and isolation. In the social distancing phase, the algorithm attempts to maintain a safe physical distance between people and limit close contacts. In the quarantine phase, the algorithm quarantines the suspected people to prevent the spread of disease. Some people who have not followed the health protocols and infected by the virus should be taken care of to get a full recovery. In the isolation phase, the algorithm cared for the infected people to recover their health. The algorithm iteratively applies these operators on the population to find the fittest and healthiest person. The proposed algorithm is evaluated on standard multi-variable single-objective optimization problems and compared with several counterpart algorithms. The results show the superiority of ACVO on most test problems compared with its counterparts.

iACVP: markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model.

The COVID-19 pandemic caused several million deaths worldwide. Development of anti-coronavirus drugs is thus urgent. Unlike conventional non-peptide drugs, antiviral peptide drugs are highly specific, easy to synthesize and modify, and not highly susceptible to drug resistance. To reduce the time and expense involved in screening thousands of peptides and assaying their antiviral activity, computational predictors for identifying anti-coronavirus peptides (ACVPs) are needed. However, few experimentally verified ACVP samples are available, even though a relatively large number of antiviral peptides (AVPs) have been discovered. In this study, we attempted to predict ACVPs using an AVP dataset and a small collection of ACVPs. Using conventional features, a binary profile and a word-embedding word2vec (W2V), we systematically explored five different machine learning methods: Transformer, Convolutional Neural Network, bidirectional Long Short-Term Memory, Random Forest (RF) and Support Vector Machine. Via exhaustive searches, we found that the RF classifier with W2V consistently achieved better performance on different datasets. The two main controlling factors were: (i) the dataset-specific W2V dictionary was generated from the training and independent test datasets instead of the widely used general UniProt proteome and (ii) a systematic search was conducted and determined the optimal k-mer value in W2V, which provides greater discrimination between positive and negative samples. Therefore, our proposed method, named iACVP, consistently provides better prediction performance compared with existing state-of-the-art methods. To assist experimentalists in identifying putative ACVPs, we implemented our model as a web server accessible via the following link: http://kurata35.bio.kyutech.ac.jp/iACVP.

FACVSPO: Fractional anti corona virus student psychology optimization enabled deep residual network and hybrid correlative feature selection for distributed denial-of-service attack detection in cloud using spark architecture

Cloud computing is an emerging standard in modern days for the purpose of sharing huge data, as it affords numerous user friendly behaviors. Cloud computing services offer an extensive range of resource pool in order to maintain huge scale data. Although, cloud computing model is disposed to several cyber-attacks and security problems regarding cloud structure, because of the dynamic and distribute character and exposures in virtualization implementation. Distributed denial-of-service (DDoS) attack is a type of cyber-attack, which disturbs the usual traffic of targeted cloud server. Moreover, DDoS produces malicious traffic in cloud structure, and thus consumes cloud resources. In this paper, an effective DDoS attack detection model, named fractional anti corona virus student psychology optimization-based deep residual network (FACVSPO-based DRN) is implemented using spark architecture. The devised FACVSPO approach is newly designed by incorporating anti coronavirus optimization (ACVO) algorithm, fractional calculus (FC) and student psychology based optimization (SPBO) model. Moreover, the hybrid correlative scheme is designed for extracting significant features for attack detection. The DRN structure is utilized for performing attack recognition, which categorizes the data as normal or attack. In addition, the DRN classifier is trained by the developed FACVSPO approach. The developed attack detection model outperformed other existing techniques in terms of testing accuracy, true negative rate (TNR), true positive rate (TPR) of 0.9236, 0.9141, and 0.9412, respectively. The testing accuracy of the implemented model is 12.02%, 8.92%, 7.27%, 6.30%, 5.68%, and 1.20% better than the existing methods, such as Taylor-elephant herd optimisation based deep belief network (TEHO-DBN), deep learning, deep neural network (DNN), multiple kernel learning, Fuzzy Taylor elephant herd optimisation (EHO)-based DBN, fractional anti corona virus optimization-deep neuro fuzzy network (FACVO-based DNFN), respectively. Similarly, the TNR is 10.14%, 6.88%, 5.94%, 5.46%, 4.25%, and 3.28% and TPR is 12.33%, 9.46%, 8.05%, 7.41%, 6.02%, and 3.04% better than the existing methods.

Plant lectins as potent Anti-coronaviruses, Anti-inflammatory, antinociceptive and antiulcer agents.

Lectins are defined as carbohydrate-binding proteins/glycoproteins of none immune origin, they are ubiquitous in nature, exist from bacteria to human cells. And due to their carbohydrate-binding recognition capacity, they have been a useful biological tool for the purification of glycoproteins and their subsequent characterization. Some plant lectins have also been revealed to own antinociceptive, antiulcer, and anti-inflammatory properties, where these features, in many instances, depending on the lectin carbohydrate-binding site. Coronavirus disease of 2019 (COVID-19) is a respiratory disease that struck the entire world leaving millions of people dead and more infected. Although COVID-19 vaccines have been made available, and quite a large number of world populations have already been immunized, the viral infection rates remained in acceleration, which continues to provoke major concern about the vaccines' efficacy. The belief in the ineffectiveness of the vaccine has been attributed in part to the recurrent mutations that occur in the epitope determinant fragments of the virus. Coronavirus envelope surface is extensively glycosylated being covered by more than sixty N-linked oligomannose, composite, and hybrid glycans with a core of Man3GlcNAc2Asn. In addition some O-linked glycans are also detected. Of these glyco-chains, many have also been exposed to several mutations, and a few remained conserved. Therefore, numerous plant lectins with a specificity directed towards these viral envelope sugars have been found to interact preferentially with them and are suggested to be scrutinized as a possible future tool to combat coronaviruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) through blocking the viral attachment to the host cells. In this review, we will discuss the possible applications of plant lectins as anti-coronaviruses including SARS-CoV-2, antinociceptive, anti-inflammatory, and antiulcer agents with the proposed mechanism of their actions.

Sesquiterpenes and polyphenols with glucose-uptake stimulatory and antioxidant activities from the medicinal mushroom Sanghuangporus sanghuang.

A chemical investigation on the fermentation products of Sanghuangporus sanghuang led to the isolation and identification of fourteen secondary metabolites (1-14) including eight sesquiterpenoids (1-8) and six polyphenols (9-14). Compounds 1-3 were sesquiterpenes with new structures which were elucidated based on NMR spectroscopy, high resolution mass spectrometry (HRMS) and electronic circular dichroism (ECD) data. All the isolates were tested for their stimulation effects on glucose uptake in insulin-resistant HepG2 cells, and cellular antioxidant activity. Compounds 9-12 were subjected to molecular docking experiment to primarily evaluate their anti-coronavirus (SARS-CoV-2) activity. As a result, compounds 9-12 were found to increase the glucose uptake of insulin-resistant HepG2 cells by 18.1%, 62.7%, 33.7% and 21.4% at the dose of 50 µmol·L-1, respectively. Compounds 9-12 also showed good cellular antioxidant activities with CAA50 values of 12.23, 23.11, 5.31 and 16.04 µmol·L-1, respectively. Molecular docking between COVID-19 Mpro and compounds 9-12 indicated potential SARS-CoV-2 inhibitory activity of these four compounds. This work provides new insights for the potential role of the medicinal mushroom S. sanghuang as drugs and functional foods.

In silico screening of the active compounds of Syzygium cumini (L.) Skeels. as anti-coronavirus

Coronavirus disease-19 (Covid-19) has been pandemic since 2019 and the world is still trying to cope with it. Even though there is a new hope with the invention of the vaccine, the virus has rapid mutation rate. Therefore alternative solutions are necessary and one of them is using the herbs with their active compounds. Syzygium cumini (L.) Skeels. is a species of Myrtaceae containing various phytochemical compounds with medicinal activity, such as anti-oxidants, anti-inflammatory, anti-cancer, anti-tumour, anti-diabetes and anti-microbial. Previous studies showed that several compound contained in S. cumini had the potential of having anti-coronavirus activities. This study aimed to determine the phytochemical compounds of S. cumini and to screen their potential as an anti-coronavirus. The method used in this research were literature study and molecular docking. The results showed that S. cumini contained the active compounds of anti-coronavirus, namely betulinic acid, kaempferol, malvidin, myricetin and quercetin. Those compounds are contained in the bark of S. cumini.

Multi-Omics-Guided Discovery of Omicsynins Produced by Streptomyces sp. 1647: Pseudo-Tetrapeptides Active Against Influenza A Viruses and Coronavirus HCoV-229E.

Many microorganisms have mechanisms that protect cells against attack from viruses. The fermentation components of Streptomyces sp. 1647 exhibit potent anti-influenza A virus (IAV) activity. This strain was isolated from soil in southern China in the 1970s, but the chemical nature of its antiviral substance(s) has remained unknown until now. We used an integrated multi-omics strategy to identify the antiviral agents from this streptomycete. The antibiotics and Secondary Metabolite Analysis Shell (antiSMASH) analysis of its genome sequence revealed 38 biosynthetic gene clusters (BGCs) for secondary metabolites, and the target BGCs possibly responsible for the production of antiviral components were narrowed down to three BGCs by bioactivity-guided comparative transcriptomics analysis. Through bioinformatics analysis and genetic manipulation of the regulators and a biosynthetic gene, cluster 36 was identified as the BGC responsible for the biosynthesis of the antiviral compounds. Bioactivity-based molecular networking analysis of mass spectrometric data from different recombinant strains illustrated that the antiviral compounds were a class of structural analogues. Finally, 18 pseudo-tetrapeptides with an internal ureido linkage, omicsynins A1-A6, B1-B6, and C1-C6, were identified and/or isolated from fermentation broth. Among them, 11 compounds (omicsynins A1, A2, A6, B1-B3, B5, B6, C1, C2, and C6) are new compounds. Omicsynins B1-B4 exhibited potent antiviral activity against IAV with the 50% inhibitory concentration (IC50) of approximately 1 µmol∙L-1 and a selectivity index (SI) ranging from 100 to 300. Omicsynins B1-B4 also showed significant antiviral activity against human coronavirus HCoV-229E. By integrating multi-omics data, we discovered a number of novel antiviral pseudo-tetrapeptides produced by Streptomyces sp. 1647, indicating that the secondary metabolites of microorganisms are a valuable source of novel antivirals.