In silico approach for mining of potential drug targets from hypothetical proteins of bacterial proteome

Omeershffudin, Umairah Natasya Mohd; Kumar, Suresh

doi:10.15406/ijmboa.2019.04.00111

Cited by 17 publications

(16 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a prior study to identify genes associated with macrolide resistance in Streptococcus pneumoniae, we demonstrated a GSEA-approach for gene identification that compared differential gene expression between mRNA expression datasets (41). This study successfully identified known and novel genes though it was limited due to incomplete genome annotation, a common problem for many microbial genomes (42)(43)(44)(45)(46)(47)(48)(49). Applying our GSEA-approach to mRNA expression data derived from a species with a more completely annotated genome, such as human or mouse, our achievable results would improve.…”

Section: Introductionmentioning

confidence: 99%

Gene Expression Meta-Analysis Reveals Interferon-Induced Genes Associated With SARS Infection in Lungs

Park

Harris

2021

Front. Immunol.

View full text Add to dashboard Cite

BackgroundSevere Acute Respiratory Syndrome (SARS) corona virus (CoV) infections are a serious public health threat because of their pandemic-causing potential. This work is the first to analyze mRNA expression data from SARS infections through meta-analysis of gene signatures, possibly identifying therapeutic targets associated with major SARS infections.MethodsThis work defines 37 gene signatures representing SARS-CoV, Middle East Respiratory Syndrome (MERS)-CoV, and SARS-CoV2 infections in human lung cultures and/or mouse lung cultures or samples and compares them through Gene Set Enrichment Analysis (GSEA). To do this, positive and negative infectious clone SARS (icSARS) gene panels are defined from GSEA-identified leading-edge genes between two icSARS-CoV derived signatures, both from human cultures. GSEA then is used to assess enrichment and identify leading-edge icSARS panel genes between icSARS gene panels and 27 other SARS-CoV gene signatures. The meta-analysis is expanded to include five MERS-CoV and three SARS-CoV2 gene signatures. Genes associated with SARS infection are predicted by examining the intersecting membership of GSEA-identified leading-edges across gene signatures.ResultsSignificant enrichment (GSEA p<0.001) is observed between two icSARS-CoV derived signatures, and those leading-edge genes defined the positive (233 genes) and negative (114 genes) icSARS panels. Non-random significant enrichment (null distribution p<0.001) is observed between icSARS panels and all verification icSARSvsmock signatures derived from human cultures, from which 51 over- and 22 under-expressed genes are shared across leading-edges with 10 over-expressed genes already associated with icSARS infection. For the icSARSvsmock mouse signature, significant, non-random significant enrichment held for only the positive icSARS panel, from which nine genes are shared with icSARS infection in human cultures. Considering other SARS strains, significant, non-random enrichment (p<0.05) is observed across signatures derived from other SARS strains for the positive icSARS panel. Five positive icSARS panel genes, CXCL10, OAS3, OASL, IFIT3, and XAF1, are found across mice and human signatures regardless of SARS strains.ConclusionThe GSEA-based meta-analysis approach used here identifies genes with and without reported associations with SARS-CoV infections, highlighting this approach’s predictability and usefulness in identifying genes that have potential as therapeutic targets to preclude or overcome SARS infections.

show abstract

Section: Introductionmentioning

confidence: 99%

Gene Expression Meta-Analysis Reveals Interferon-Induced Genes Associated With SARS Infection in Lungs

Park

Harris

2021

Front. Immunol.

View full text Add to dashboard Cite

show abstract

“…Many studies identified drug and vaccine candidates successfully for various bacteria and virus using reverse vaccinology approach (Kumar 2011(Kumar , 2015Kumar and Ramanujam 2020;Omeershffudin and Kumar 2019). In this study, the development of peptide vaccine design has identified by reverse vaccinology, which aims to classify possible candidates for the vaccine considering all the proteome of the virus with the prediction of MHC-I (47 alleles) and MHC-II (27 set alleles) with high antigenicity.…”

Section: Introductionmentioning

confidence: 99%

<strong>COVID-19 Vaccine Candidates by Identification of B and T Cell Multi-Epitopes Against SARS-COV-2</strong>

Kumar¹,

Mathavan²,

W³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Coronavirus disease (COVID-19) is a new discovered strain where WHO officially declares the disease as COVID-19 while the virus responsible for it called Severe Acute Respiratory Syndrome Coronavirus 2 or SARS-CoV-2. The incubation period of this disease is between 14 days. Ordinary clinical symptoms that reported around the world include fever, cough, fatigue, diarrhoea and vomiting as well as asymptomatic for certain people. Infection is spread mainly through broad droplets. In early March 2020, WHO again has announced that COVID-19 is a pandemic with currently no specific treatment. The potential use of SARS-COV-2 proteome as a vaccine candidate by analysing through B-cell and T-cell antigenicity by using a immunoinformatics approach as a vaccine development early stage. In this study, we used consensus sequence for SARS-COV-2 proteome that was retrieved from NCBI database. VaxiJen 2.0 was mainly used to identify the antigenic property of SARS-COV-2 proteins. IEDB then used to analyse the B-cell epitope, the presence of T cell immunogenic epitope in SARS-COV-2 proteins was obtained by using compromise method of MHC class I and II tools that accessible respectively using ProPred-1 server and MHC II Binding Prediction in IEDB database. The best epitopes of B and T-cell epitopes were predicted with high antigencity and the information is disseminated through web-based database resource (https://covid-19.omicstutorials.com/epitopes/). This study will be useful to find a new epitope-based candidate for SARS-COV-2. However, further study needs to be done for the next stages of vaccine development.

show abstract

“…Unfortunately, many genomes have a substantial number (up to 70%) of hypothetical proteins (HPs), which are ORFs with unknown functions ( Sivashankari and Shanmughavel, 2006 ; Mohan and Venugopal, 2012 ; Bharat Siva Varma et al, 2015 ; Ijaq et al, 2015 ; Islam et al, 2015 ; School et al, 2016 ). Reports estimated that around 33% of National Center for Biotechnology Information (NCBI) knowledgebase sequences in 2006 were HPs ( Kolker et al, 2004 ; Sivashankari and Shanmughavel, 2006 ; Omeershffudin and Kumar, 2019 ). While the exact number of HPs in today’s NCBI is unknown, recent papers on Mycobacterium tuberculosis and Exiguobacterium antarcticum strain B7 genomes report around 27% HPs ( da Costa et al, 2018 ; Yang et al, 2019 ) with 16% HPs in Shigella flexneri ( Gazi et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

An Educational Bioinformatics Project to Improve Genome Annotation

2020

View full text Add to dashboard Cite

Scientific advancement is hindered without proper genome annotation because biologists lack a complete understanding of cellular protein functions. In bacterial cells, hypothetical proteins (HPs) are open reading frames with unknown functions. HPs result from either an outdated database or insufficient experimental evidence (i.e., indeterminate annotation). While automated annotation reviews help keep genome annotation up to date, often manual reviews are needed to verify proper annotation. Students can provide the manual review necessary to improve genome annotation. This paper outlines an innovative classroom project that determines if HPs have outdated or indeterminate annotation. The Hypothetical Protein Characterization Project uses multiple well-documented, freely available, web-based, bioinformatics resources that analyze an amino acid sequence to (1) detect sequence similarities to other proteins, (2) identify domains, (3) predict tertiary structure including active site characterization and potential binding ligands, and (4) determine cellular location. Enough evidence can be generated from these analyses to support re-annotation of HPs or prioritize HPs for experimental examinations such as structural determination via X-ray crystallography. Additionally, this paper details several approaches for selecting HPs to characterize using the Hypothetical Protein Characterization Project. These approaches include student- and instructor-directed random selection, selection using differential gene expression from mRNA expression data, and selection based on phylogenetic relations. This paper also provides additional resources to support instructional use of the Hypothetical Protein Characterization Project, such as example assignment instructions with grading rubrics, links to training videos in YouTube, and several step-by-step example projects to demonstrate and interpret the range of achievable results that students might encounter. Educational use of the Hypothetical Protein Characterization Project provides students with an opportunity to learn and apply knowledge of bioinformatic programs to address scientific questions. The project is highly customizable in that HP selection and analysis can be specifically formulated based on the scope and purpose of each student’s investigations. Programs used for HP analysis can be easily adapted to course learning objectives. The project can be used in both online and in-seat instruction for a wide variety of undergraduate and graduate classes as well as undergraduate capstone, honor’s, and experiential learning projects.

show abstract

In silico approach for mining of potential drug targets from hypothetical proteins of bacterial proteome

Cited by 17 publications

References 37 publications

Gene Expression Meta-Analysis Reveals Interferon-Induced Genes Associated With SARS Infection in Lungs

Gene Expression Meta-Analysis Reveals Interferon-Induced Genes Associated With SARS Infection in Lungs

<strong>COVID-19 Vaccine Candidates by Identification of B and T Cell Multi-Epitopes Against SARS-COV-2</strong>

An Educational Bioinformatics Project to Improve Genome Annotation

Contact Info

Product

Resources

About