Virus Database Research Articles

Financial Data Anomaly Detection Method Based on Decision Tree and Random Forest Algorithm

The fast‐developing computer network not only brings convenience to people but also brings security problems to people due to the appearance of various abnormal flows. However, various current detection systems for abnormal network flows have more or less flaws, such as the most common intrusion detection system (IDS). Due to the lack of self‐learning capabilities of market‐oriented IDS, developers and maintenance personnel have to update the virus database of the system in real time to make the system work normally. With the emergence of machine learning and data mining in recent years, new ideas and methods have emerged in the detection of abnormal network flows. In this paper, the random forest algorithm is introduced into the detection of abnormal samples, and the concept of abnormal point scale is proposed to measure the abnormal degree of the sample based on the similarity of the samples, and the abnormal samples are screened out according to this scale. Simulation experiments show that compared with the other two distance‐based abnormal sample detection techniques, the random forest‐based abnormal sample detection has greater advantages than the other two methods in terms of improving the accuracy of the model and reducing the computing time.

Investigation of changes in protein stability and substrate affinity of 3CL-protease of SARS-CoV-2 caused by mutations.

3CLpro of SARS-CoV-2 is one of the enzymes required for the replication process of the virus responsible for the COVID-19 pandemic. In this study, changes in protein stability and substrate affinity caused by mutations were investigated to stir the development of potent inhibitors. Sequence data of samples were obtained from the NCBI Virus database. Mutation analyses were performed with RDP4 and MegaX. 3CLpro tertiary models were created using Robetta. Molecular docking for peptidomimetic substrate and inhibitor ligand was done with Autodock v4.2 and Haddock v2.4. Protein stability analysis was performed using mCSM stability and DynaMut2. Twenty-four missense mutations in 3CLpro were identified in this study. Changes in the 3CLpro structure induced by the mutations Met49Thr, Leu167Ser, and Val202Ala resulted in significant levels of instability (-2.029,-2.612,-2.177 kcal.mol-1, respectively). The lowest interaction energy for substrate was -58.7 kcal.mol-1 and -62.6 kcal.mol-1 in wild-type and mutant, respectively. The lowest docking energy for ligand was -6.19 and -9.52 kcal.mol-1 for wild-type and mutant, respectively. This study reports for the first time that mutations cause increased substrate affinity of 3CLpro from SARS-CoV-2. This research provides important data for the development of potent peptidomimetic inhibitors for the treatment of COVID-19.

ICAV: an integrative database of cancer-associated viruses.

To date, various studies have found that the occurrence of cancer may be related to viral infections. Therefore, it is important to explore the relationship between viruses and diseases. The International Agency for Research on Cancer has defined six types of viruses as Class 1 human carcinogens, including Epstein–Barr virus, hepatitis C virus, hepatitis B virus, human T-cell lymphotropic virus, human herpesvirus 8 and human papillomavirus, while Merkel cell polyomavirus is classified as ‘probably carcinogenic to humans’ (Group 2A). Therefore, in-depth research on these viruses will help clarify their relationship with diseases, and substantial efforts have been made to sequence their genomes. However, there is no complete database documenting these cancer-associated viruses, and researchers are not able to easily access and retrieve the published genomes. In this study, we developed iCAV, a database that integrates the genomes of cancer-related viruses and the corresponding phenotypes. We collected a total of 18 649 genome sequences from seven human disease-related viruses, and each virus was further classified by the associated disease, sample and country. iCAV is a comprehensive resource of cancer-associated viruses that provides browse and download functions for viral genomes. Database URL: http://icav.omicsbio.info/

Detection of novel paramyxoviruses in Chaerephon bat species in Nigeria and phylogenetics of paramyxoviruses co-evolution with bats in Africa.

Bat paramyxoviruses (PmV) are a diverse group of viruses and include zoonotic viruses such as henipaviruses. Members of this group in other continents have been associated with severe respiratory and neurological infections in animals and humans. Furthermore, despite the richness of diverse bat species that can transmit this virus in African countries like Nigeria, there is very scanty information as to the presence and co-evolution of paramyxoviruses in bats. There is a need for continuous surveillance of zoonotic viruses and their biological reservoirs as this will help in the prevention and management of pathogens' spillovers. This study detected novel paramyxoviruses in Chaerephon nigeriae bat species found in Badagry, Lagos. Phylogenetic analyses of paramyxovirus sequences' co-evolution with frugivorous and insectivorous bats circulating in African countries were also performed using sequences of African origin available in the Database of Bat-Associated Viruses (DBatVir: http://www.mgc.ac.cn/DBatVir/). Oral swabs (n=18) and blood samples (n=32) were collected from C.nigeriae bats in Badagry, Lagos. The L gene of bat paramyxovirus was detected in all oral swabs using PCR techniques. Six of the amplicons were successfully sequenced. Estimated phylogenies placed the sequences in close relationship with those isolated from insectivorous bats. Phylogenetic analyses of previously sequenced isolates in the African region showed the likelihood of different co-evolution mechanisms of paramyxoviruses with frugivorous bats compared with insectivorous bats. This may be due to codon usage bias of the L gene. Spatial distribution of paramyxoviruses in African countries showed limited ongoing surveillance of this virus in the continent, especially in southern and northern countries. Extensive surveillance of paramyxoviruses with possible zoonotic potentials among bat species in the continent is recommended. This will provide further insights into co-evolution as well as prevent possible spillover into the human population.

Mutations in the SARS CoV2 Spike Gene and Their Reflections on the Spike Protein

Objective: In this study, it was aimed to determine the mutation frequency in spike (S) genes of SARS CoV2 from six different regions of the world, their distribution on the gene and reflections of these mutations to the S protein. Materials and methods: SARS CoV2 S gene sequences originating from Asia, Africa, Europe, South America, Oceania and North America were obtained from NCBI virus database. The sequences were analyzed with Geneious and BioEdit multiple sequence alignment programs. Results and Conclusion: 865 distinct mutations on the S genes were detected in the virus samples. Among these, 59 variants with numbers of 10 and above in the virus population were detected. The D614G(A1841G) substitution was found to be the most common with an average of 88.6%. Furthermore, it was determined that S477N(G1430A) substitution in theviruses of Oceania differed from other regions with a rate of 86.7%. The average mutation frequency of the S genes from different regions was calculated as 4x10-5. The amino acid substitutions particularly in the RBD (receptor binding domain) have great importance for the virus adsorption to the cells via ACE2 (angiotensin converting enzyme 2) receptor and transmission. However, the importance of these mutations needs to be demonstrated both in silico and experimental studies.

ZOVER: the database of zoonotic and vector-borne viruses.

Emerging infectious diseases significantly threaten global public health and socioeconomic security. The majority of emerging infectious disease outbreaks are caused by zoonotic/vector-borne viruses. Bats and rodents are the two most important reservoir hosts of many zoonotic viruses that can cross species barriers to infect humans, whereas mosquitos and ticks are well-established major vectors of many arboviral diseases. Moreover, some emerging zoonotic diseases require a vector to spread or are intrinsically vector-borne and zoonotically transmitted. In this study, we present a newly upgraded database of zoonotic and vector-borne viruses designated ZOVER (http://www.mgc.ac.cn/ZOVER). It incorporates two previously released databases, DBatVir and DRodVir, for bat- and rodent-associated viruses, respectively, and further collects up-to-date knowledge on mosquito- and tick-associated viruses to establish a comprehensive online resource for zoonotic and vector-borne viruses. Additionally, it integrates a set of online visualization tools for convenient comparative analyses to facilitate the discovery of potential patterns of virome diversity and ecological characteristics between/within different viral hosts/vectors. The ZOVER database will be a valuable resource for virologists, zoologists and epidemiologists to better understand the diversity and dynamics of zoonotic and vector-borne viruses and conduct effective surveillance to monitor potential interspecies spillover for efficient prevention and control of future emerging zoonotic diseases.

Cohort study: Apparent redundancy of fibrosis assessment in young persons with HCV; development of realistic approaches to break the paradigm

Introduction and ObjectivesHepatitis C Virus (HCV) is a blood-borne, hepatotropic RNA virus causing both acute and chronic infection. Chronic HCV infection predisposes individuals to liver fibrosis, cirrhosis and hepatocellular carcinoma. Staging of fibrosis prior to treatment to determine either treatment choice or required follow up, is standard practice. However, this often acts as a barrier to treatment initiation. We sought to validate the hypothesis that those individuals; mono-infected with HCV, ≤35 years of age; with no additional hepatic insult were unlikely to have significant fibrosis. MethodsWe performed a retrospective analysis of a Hepatitis C Virus database; with collation of relevant basic demographics including age, sex and baseline Transient Elastography measurements pre-treatment. Additionally, we compared the reliability of biochemical fibrosis scores with corresponding transient elastography scores. ResultsOur results support the hypothesis that those individuals with chronic HCV ≤35 years old, with no additional risk for fibrogenesis did not have significant liver fibrosis within our cohort. ConclusionPatients ≤35 years old likely do not necessitate fibrosis assessment prior to Direct Acting Antiviral (DAA) treatment in the absence of other significant risk factors for fibrosis. Given the emerging evidence that DAA treatment results in a significant decrease in all-cause mortality and hepatocellular carcinoma development, treatment of those with chronic HCV represents a global priority.

Phylogenicity of B.1.1.7 surface glycoprotein, novel distance function and first report of V90T missense mutation in SARS-CoV-2 surface glycoprotein

Phylogenicity of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B.1.1.7 surface glycoproteins reported from Europe to the National Center for Biotechnology Information (NCBI) virus database by the mid of April 2021 is analyzed. Novel function for computing phylogenetic distance is proposed for that purpose. Proposed distance function resulted in better-fitted clusters than Jaccard and Sorensen-Dice and accurate evolutionary links were predicted for B.1.1.7 spike variants. Most B.1.1.7 spike variants were linked to their likely direct predecessors at single amino acid change, that in many cases resulted in loss of the key mutations that are associated to the higher B.1.1.7 SARS-CoV-2 infectivity. There were also cases where second mutation was introduced to compensate for the missing mutation. Unreported V90T SARS-CoV-2 surface glycoprotein mutation was also identified, that contributes towards escaping 2–51 neutralizing antibody.

Geographic Classification and Identification of SARS-CoV2 From Related Viral Sequences

The COVID-19 pandemic has introduced to mild the risks of deadly epidemic-prone illnesses sweeping our globalized planet. The pandemic is still going strong, with additional viral variations popping up all the time. For the close to future, the international response will have to continue. The molecular tests for SARS-CoV-2 detection may lead to False-negative results due to their genetic similarity with other coronaviruses, as well as their ability to mutate and evolve. Furthermore, the clinical features caused by SARS-CoV-2 seem to be like the symptoms of other viral infections, making identification even harder. We constructed seven hidden Markov models for each coronavirus family (SARS-CoV2, HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-HKU1, MERS-CoV, and SARS-CoV), using their complete genome to accurate diagnose human infections. Besides, this study characterized and classified the SARS-CoV2 strains according to their different geographical regions. We built six SARS-CoV2 classifiers for each world's continent (Africa, Asia, Europe, North America, South America, and Australia). The dataset used was retrieved from the NCBI virus database. The classification accuracy of these models achieves 100% in differentiating any virus model among others in the Coronavirus family. However, the accuracy of the continent models showed a variable range of accuracies, sensitivity, and specificity due to heterogeneous evolutional paths among strains from 27 countries. South America model was the highest accurate model compared to the other geographical models. This finding has vital implications for the management of COVID-19 and the improvement of vaccines.

First report of Potato spindle tuber viroid infecting tree tomato in Kenya in mixed infection with Potato virus Y

First report of <i>Potato spindle tuber viroid</i> infecting tree tomato in Kenya in mixed infection with <i>Potato virus Y</i>

Whole genome comparison of Pakistani Corona virus with Chinese and US Strains along with its predictive severity of COVID-19

Initially submitted 784 SARS-nCoV2 whole genome sequences on NCBI Virus database were selected for phylogenetic analysis to look into their similarities with two of Pakistani sequenced coronavirus strains having accessions of MT240479 and MT262993. The MT240479 named (Gilgit1-Pak) was found in close proximity to MT184913 named (CruiseA-USA), while MT262993 named (Manga-Pak) was in neighboring to MT039887 named (WI-USA) strain, which were further chosen for variant calling analysis along with reference genome NC_045512 as out-group to construct concluding cladogram and looked for evolutionary distance with PAUP software in this article. Aforementioned Pakistani strains each of having 29,836 bases were compared with MT263429 (WI-USA) of 29,889 bases and MT259229 (Wuhan-P.R. China) of 29,864 bases. Whole genome variant calling pipeline revealed 31 variants in both Pakistani strains collectively (Manga-Pak vs USA having 2del & 7SNPs, while different from Chinese strain with 2del & 2SNPs, similarly Gilgit1-Pak vs USA having 10SNPs, while different from Chinese strains having 8SNPs). These variants harbour ORF1ab, ORF1a and N genes having their role is viral replication/translation, host innate immunity and viral capsid formation respectively. These novel variants may be one of the reasons for low mortality rate in Pakistan with 385 deaths as compared to USA with 63,871 and P.R. China with 4633 by May 01, 2020. However functional characterization of these variants and their integrations with other viral proteins including variability of human receptors (ACE2 & NRP1) may be the other reasons for unlikely COVID-19 statistics in Pakistan which need further confirmatory studies. Moreover, mutated N and ORF1a proteins in Pakistani strains were also analyzed by 3D structure modeling, which give another dimension of comparing these alterations at amino acid level. In a nutshell, these novel variants are correlated with reduced mortality of COVID-19 severity in Pakistan while more robust results can be obtained by wet lab experimentation. This also gives insight of genomic landscape of these indigenous strains to develop diagnostics kits, vaccines and therapeutic interventions.

Mutational analysis of structural proteins of SARS-CoV-2

SARS-CoV-2 transmissibility is higher than that of other human coronaviruses; therefore, it poses a threat to the populated communities. We investigated mutations among envelope (E), membrane (M), and spike (S) proteins from different isolates of SARS-CoV-2 and plausible signaling influenced by mutated virus in a host. We procured updated protein sequences from the NCBI virus database. Mutations were analyzed in the retrieved sequences of the viral proteins through multiple sequence alignment. Additionally, the data was subjected to ScanPROSITE to analyse if the mutations generated a relevant sequence for host signaling. Unique mutations in E, M, and S proteins resulted in modification sites like PKC phosphorylation and N-myristoylation sites. Based on structural analysis, our study revealed that the D614G mutation in the S protein diminished the interaction with T859 and K854 of adjacent chains. Moreover, the S protein of SARS-CoV-2 consists of an Arg-Gly-Asp (RGD) tripeptide sequence, which could potentially interact with various members of integrin family receptors. RGD sequence in S protein might aid in the initial virus attachment. We speculated crucial host pathways which the mutated isolates of SARS-CoV-2 may alter like PKC, Src, and integrin mediated signaling pathways. PKC signaling is known to influence the caveosome/raft pathway which is critical for virus entry. Additionally, the myristoylated proteins might activate NF-κB, a master molecule of inflammation. Thus the mutations may contribute to the disease pathogenesis and distinct lung pathophysiological changes. Further the frequently occurring mutations in the protein can be studied for possible therapeutic interventions.

Drug repositioning based on similarity constrained probabilistic matrix factorization: COVID-19 as a case study

The novel coronavirus disease 2019 (COVID-19) pandemic has caused a massive health crisis worldwide and upended the global economy. However, vaccines and traditional drug discovery for COVID-19 cost too much in terms of time, manpower, and money. Drug repurposing becomes one of the promising treatment strategies amid the COVID-19 crisis. At present, there are no publicly existing databases for experimentally supported human drug–virus interactions, and most existing drug repurposing methods require the rich information, which is not always available, especially for a new virus. In this study, on the one hand, we put size-able efforts to collect drug–virus interaction entries from literature and build the Human Drug Virus Database (HDVD). On the other hand, we propose a new approach, called SCPMF (similarity constrained probabilistic matrix factorization), to identify new drug–virus interactions for drug repurposing. SCPMF is implemented on an adjacency matrix of a heterogeneous drug–virus network, which integrates the known drug–virus interactions, drug chemical structures, and virus genomic sequences. SCPMF projects the drug–virus interactions matrix into two latent feature matrices for the drugs and viruses, which reconstruct the drug–virus interactions matrix when multiplied together, and then introduces the weighted similarity interaction matrix as constraints for drugs and viruses. Benchmarking comparisons on two different datasets demonstrate that SCPMF has reliable prediction performance and outperforms several recent approaches. Moreover, SCPMF-predicted drug candidates of COVID-19 also confirm the accuracy and reliability of SCPMF.

A k-mer based approach for classifying viruses without taxonomy identifies viral associations in human autism and plant microbiomes

Viruses are an underrepresented taxa in the study and identification of microbiome constituents; however, they play an essential role in health, microbiome regulation, and transfer of genetic material. Only a few thousand viruses have been isolated, sequenced, and assigned a taxonomy, which limits the ability to identify and quantify viruses in the microbiome. Additionally, the vast diversity of viruses represents a challenge for classification, not only in constructing a viral taxonomy, but also in identifying similarities between a virus’ genotype and its phenotype. However, the diversity of viral sequences can be leveraged to classify their sequences in metagenomic and metatranscriptomic samples, even if they do not have a taxonomy. To identify and quantify viruses in transcriptomic and genomic samples, we developed a dynamic programming algorithm for creating a classification tree out of 715,672 metagenome viruses. To create the classification tree, we clustered proportional similarity scores generated from the k-mer profiles of each of the metagenome viruses to create a database of metagenomic viruses. The resulting Kraken2 database of the metagenomic viruses can be found here: https://www.osti.gov/biblio/1615774 and is compatible with Kraken2. We then integrated the viral classification database with databases created with genomes from NCBI for use with ParaKraken (a parallelized version of Kraken provided in Supplemental Zip 1), a metagenomic/transcriptomic classifier. To illustrate the breadth of our utility for classifying metagenome viruses, we analyzed data from a plant metagenome study identifying genotypic and compartment specific differences between two Populus genotypes in three different compartments. We also identified a significant increase in abundance of eight viral sequences in post mortem brains in a human metatranscriptome study comparing Autism Spectrum Disorder patients and controls. We also show the potential accuracy for classifying viruses by utilizing both the JGI and NCBI viral databases to identify the uniqueness of viral sequences. Finally, we validate the accuracy of viral classification with NCBI databases containing viruses with taxonomy to identify pathogenic viruses in known COVID-19 and cassava brown streak virus infection samples. Our method represents the compulsory first step in better understanding the role of viruses in the microbiome by allowing for a more complete identification of sequences without taxonomy. Better classification of viruses will improve identifying associations between viruses and their hosts as well as viruses and other microbiome members. Despite the lack of taxonomy, this database of metagenomic viruses can be used with any tool that utilizes a taxonomy, such as Kraken, for accurate classification of viruses.

Research on spam filtering algorithm based on mutual information and weighted naive Bayesian classification

In this paper, N-gram algorithm is used to construct the characteristic database of e-mail viruses. Based on the characteristic database, the transmission and immune automatic behaviour of e-mail viruses are constructed based on the disease transmission model. Based on a large number of samples of five different virus types, a mail virus feature library is generated, and the automatic transmission and immune process of mail virus are analysed according to the mail virus feature library. Then, aiming at the low precision and recall rate of the traditional spam filtering algorithm, an improved mutual information feature and weighted naive Bayesian classification algorithm is proposed to complete the spam filtering. Experiments on trec06c open source data set show that the feature library generated by this method has a good performance of e-mail virus detection, and the analysis of e-mail virus behaviour can better meet the actual work of e-mail virus prevention.

A Vicenary Analysis of SARS-CoV-2 Genomes

Coronaviruses are responsible for various diseases ranging from the common cold to severe infections like the Middle East syndromes and the severe acute respiratory syndrome. However, a new coronavirus strain known as COVID-19 developed into a pandemic resulting in an ongoing global public health crisis. Therefore, there is a need to understand the genomic transformations that occur within this family of viruses in order to limit disease spread and develop new therapeutic targets. The nucleotide sequences of SARS-CoV-2 are consist of several bases. These bases can be classified into purines and pyrimidines according to their chemical composition. Purines include adenine (A) and guanine (G), while pyrimidines include cytosine (C) and tyrosine (T). There is a need to understand the spatial distribution of these bases on the nucleotide sequence to facilitate the development of antivirals (including neutralizing antibodies) and epitomes necessary for vaccine development. This study aimed to evaluate all the purine and pyrimidine associations within the SARS-CoV-2 genome sequence by measuring mathematical parameters including; Shannon entropy, Hurst exponent, and the nucleotide guanine-cytosine content. The Shannon entropy is used to identify closely associated sequences. Whereas Hurst exponent is used to identifying the auto-correlation of purine-pyrimidine bases even if their organization differs. Different frequency patterns can be used to determine the distribution of all four proteins and the density of each base. The GC-content is used to understand the stability of the DNA. The relevant genome sequences were extracted from the National Center for Biotechnology Information (NCBI) virus database. Furthermore, the phylogenetic properties of the COVID-19 virus were characterized to compare the closeness of the COVID-19 virus with other coronaviruses by evaluating the purine and pyrimidine distribution.

PVDP: A portable open source pipeline for detection of plant viruses in RNAseq data. A case study on potato viruses in Antioquia (Colombia)

High-throughput sequencing methods are becoming an essential tool in disease management programs. Unfortunately, the implementation of these techniques in developing countries can be hindered by a limited availability of well-trained bioinformaticians and/or high-performance computing centers that can process and analyze the data with reliability and speed. In this work we present a stand-alone plant virus detection pipeline (PVDP) optimized for the surveillance of potato viruses but also useful in other plant hosts. This open source pipeline runs under Python and includes a curated database of plant viruses in addition to an R script for presenting results as a user-friendly html report. The pipeline scripts are freely-available in GitHub, can be executed locally under LINUX, macOS or Windows, and does not require data submission to third-parties or access to high performance computing centers. The pipeline was calibrated using twelve previously obtained potato datasets and validated with new samples collected in Solanum tuberosum and S. phureja fields from eastern Antioquia (Colombia). The plant virus detection pipeline revealed high infection levels of PVY (1678.4 RPM) and lower levels of PMTV (1.2–2 RPM) infecting S. tuberosum. In S. phureja, the pipeline detected high infection levels of PVX, PVS and PVV (RPM > 2044) and lower levels of PMTV, PVY, and PYVV (RPM <82.1). Results also suggest the probable infection of S. phureja by pepino mosaic virus (PepMV) and by a putative unidentified potyvirus. Hopefully, this plant virus detection pipeline will facilitate the implementation of HTS methods in developing countries, such as Colombia, where there is an urgent need to improve the current plant and seed health standards.

Machine-Learning Driven Drug Repurposing for COVID-19

The integration of machine learning methods into bioinformatics provides particular benefits in identifying how therapeutics effective in one context might have utility in an unknown clinical context or against a novel pathology. We aim to discover the underlying associations between viral proteins and antiviral therapeutics that are effective against them by employing neural network models. Using the National Center for Biotechnology Information virus protein database and the Drug Virus database, which provides a comprehensive report of Broad-Spectrum Antiviral Agents (BSAAs) and viruses they inhibit, we trained ANN models with virus protein sequences as inputs and antiviral agents deemed safe-in-humans as outputs. Model training excluded SARS-CoV-2 proteins and included only Phases II, III, IV and approved level drugs. Using sequences for SARS-CoV-2 (the coronavirus that causes COVID-19) as inputs to the trained models produces outputs of tentative safe-in-human antiviral candidates for treating COVID-19. Our results suggest multiple drug candidates, some of which complement recent findings from noteworthy clinical studies. Our in-silico approach to drug repurposing has promise in identifying new drug candidates and treatments for other viral, bacterial and parasitic infections.

SARS CoV-2 nsp1 Mutasyonlarının Protein Yapıda Ortaya Çıkardığı Değişimler

Şiddetli akut solunum yolu sendromu koronavirüsü 2 (SARS CoV-2) pozitif polariteli ve tek iplikli bir RNA virüsüdür. Virüsün sebep olduğu COVID19 hastalığı on ay gibi kısa bir sürede 900 binden fazla insanın ölümüne neden oldu. Virüs ile mücadelede etkin ve spesifik bir ilaç ve aşı henüz bulunmamaktadır. İlaç ve aşı geliştirme çalışmaları virüsün yapısal ve fonksiyonel özelliklerinin kapsamlı bir şekilde anlaşılmasını gerekli kılmaktadır. Hızlı yayılım gösteren virüsün yüksek mutasyon hızı geliştirilecek aşı ve ilaçların etkinliklerini sürdürebilmelerinin önündeki en büyük engellerden biridir. Hücresel boyutta viral enfeksiyonun başlangıcında yer alan SARS CoV-2 yapısal olmayan protein 1 (nsp1) önleyici tedavi için potansiyel hedef proteindir. Konak hücre translasyonunu engelleyen nsp1’in yapısının bilinmesi önemlidir. Bu çalışmada 222 Avrupa izolatında görülen nsp1 mutasyonlarının protein yapıda ortaya çıkarabileceği değişimler yapay zekâ tabanlı bir modelleme yazılımı olan trRosetta kullanılarak modellenmiştir. NCBI Virüs veritabanından elde edilen dizi bilgileri MAFFT çoklu dizi hizalama programı ile hizalanmıştır. Mutasyon analizleri RDP4 yazılımı ile yapılmıştır. Mutant protein primer yapı MegaX yazılımı ile oluşturulmuştur. Protein kalite skorları QMEAN algoritması kullanılarak analiz edilmiştir. Proteinleri fizikokimyasla özellikleri ProtParam ExPAsy programı ile yapılmıştır. Elde edilen protein yapıların konformasyonel analizleri PyMOL ile yapılmıştır. SARS CoV-2 Avrupa izolatlarında görülen nsp1 mutasyonlarının protein sekonder ve tersiyer yapısında konformasyonel ve topolojik değişimlere neden olabileceği tespit edilmiştir. SARS CoV-2 katalitik bölgeyi içine alan P153 ve N178 rezidüleri arasında kalan bölgede görülen değişimin proteinin fonksiyonel özelliklerini etkileyebileceği düşünülmektedir. Elde edilen verilerin önleyici ve tedavi edici yaklaşımlara önemli veriler sunabileceği düşünülmektedir.

NCBI's Virus Discovery Codeathon: Building "FIVE" -The Federated Index of Viral Experiments API Index.

Viruses represent important test cases for data federation due to their genome size and the rapid increase in sequence data in publicly available databases. However, some consequences of previously decentralized (unfederated) data are lack of consensus or comparisons between feature annotations. Unifying or displaying alternative annotations should be a priority both for communities with robust entry representation and for nascent communities with burgeoning data sources. To this end, during this three-day continuation of the Virus Hunting Toolkit codeathon series (VHT-2), a new integrated and federated viral index was elaborated. This Federated Index of Viral Experiments (FIVE) integrates pre-existing and novel functional and taxonomy annotations and virus–host pairings. Variability in the context of viral genomic diversity is often overlooked in virus databases. As a proof-of-concept, FIVE was the first attempt to include viral genome variation for HIV, the most well-studied human pathogen, through viral genome diversity graphs. As per the publication of this manuscript, FIVE is the first implementation of a virus-specific federated index of such scope. FIVE is coded in BigQuery for optimal access of large quantities of data and is publicly accessible. Many projects of database or index federation fail to provide easier alternatives to access or query information. To this end, a Python API query system was developed to enhance the accessibility of FIVE.