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View all- Patt MSolanki JGeorrge J(undefined)In Silico Analysis of Hypothetical Proteins of Haemophilus Influenzae PittGG StrainSSRN Electronic Journal10.2139/ssrn.3574587
Functional Annotation of Hypothetical proteins of Lactobacillus rhamnosus: An In Silico Approach
Pages 65 - 69
Abstract
Probiotic bacterium, Lactobacillus rhamnosus (LR24) is widely used in the food and nutraceutical industries owing to its numerous health benefits and industrial applications. To efficiently use the bacterium, various genetic engineering techniques and strategies are in use like Genome shuffling, Anti-sense RNA technology and so forth. It is really important to understand the system biology and metabolism of this bacterium and hence the proteome analysis of the bacterium becomes an essential research prospect. However, in NCBI-Genome out of 2640 proteins, 420 are termed hypothetical. Adding functional information to these hypothetical proteins has become strategically important to study systems level and metabolic pathway analysis of the bacterium. The present study focuses on using the in silico strategies like homology analysis based on sequence similarity, sub-cellular localization prediction, domain extraction, searching for essential proteins, mapping with gene ontology terms and functional annotation. Out of 420 proteins, total 46 proteins were annotated and among them 18 proteins were known to have homologs in the Database of essential genes (DEG). Out of 18 proteins, One protein KFK47528.1 had the maximum no.(55) of homologs with DEG so it was modelled with good confidence and query coverage. Overall approach was to assign a putative function to the hypothetical proteins by integrating the information obtained from the various resources. This study also reports a need to develop a standardized pipeline based on intelligent learning with fast and exhaustive approach to solve the biological problem of accumulating hypothetical proteins.
References
[1]
Indu, P., K., Kanwaljit, C., and Amarpreet, S. 2002. Probiotics: potential pharmaceutical applications. European Journal of Pharmaceutical Sciences. 15(1), 1--9.
[2]
Pérez, C. 2015. Probiotics for the treating acute diarrhea and preventing antibiotic-associated diarrhea in children. Nutr Hosp. 31(Suppl 1):64--7.
[3]
Segers, M. E. and Lebeer, S. 2014. Towards a better understanding of lactobacillus rhamnosus GG - host interactions. Microbial Cell Factories. 13(Suppl 1):S7.
[4]
Conway, P. L., Gorbach, S. L., and Goldin, B. R. 1987. Survival of lactic acid bacteria in the human stomach and adhesion to intestinal cells. Journal of. Dairy Science. 70 (1): 1--12.
[5]
Guandalini, S., Pensabene, L., Abu, Z. M. et al. 2000. Lactobacillus GG administered in oral rehydration solution to children with acute diarrhea: A multi-center European trial. Journal of Pediatric Gastroenterology & Nutrition. 30: 54--60.
[6]
Anukam, K. C., Osazuwa, E., Osadolor, H. B., Bruce, A. W., and Reid, G. 2008. Yogurt containing probiotic lactobacillus rhamnosus GR-1 and L. reuteri RC-14 Helps Resolve Moderate Diarrhea and Increases CD4 Count in HIV/AIDS Patients. Journal of Clinical Gastroenterology. 42(3): 239--243.
[7]
Vanderhoof, J. A. et al. 1999. Lactobacillus GG in the prevention of antibiotic-associated diarrhea in children. The Journal of Pediatrics 135 (5): 564--568.
[8]
Näse, L., Hatakka, K., Savilahti, E., Saxelin, M., Pönkä, A., Poussa, T., Korpela, R., and Meurman, J. H. 2001. Effect of long-term consumption of a probiotic bacterium, Lactobacillus rhamnosus GG, in milk on dental caries and caries risk in children. Caries Res. 35(6):412--20.
[9]
Monika, J., Khushboo, G., and Payal, J. 2014. Significance of probiotics and prebiotics in health and nutrition. Malaya Journal of Biosciences. 1(3):181--195.
[10]
Yuhua, W., Yan, L., Xiaolin, P., Lei, Y., and Yan, F. 2007, Genome-shuffling improved acid tolerance and l-lactic acid volumetric productivity in Lactobacillus rhamnosus. Journal of Biotechnology. 129: 510--515.
[11]
María, J. Y., Vicente, M., Gaspar, P. M., and Jesús, R. D.2012. Genetically engineered lactobacilli for technological and functional food applications. Food Industrial Processes - Methods and Equipment. ISBN 978-953-307-905-9.
[12]
Fang, Y. and Brent, P. D. 2012. Lactobacillus rhamnosus GG: an updated strategy to use microbial products to promote health. Functional Food Reviews. 4(2): 77--84.
[13]
Minna, K. S., Soile, T., Hilpi, R. et al. 2002. Lactobacillus Bacteremia during a Rapid Increase in Probiotic Use of Lactobacillus rhamnosus GG in Finland. Clinical Infectious Diseases. 35(10):1155--60.
[14]
Bouazzaoui, K. and LaPointe G. 2006. Use of antisense RNA to modulate glycosyltransferase gene expression and exopolysaccharide molecular mass in Lactobacillus rhamnosus. Journal of Microbiological Methods. 65(2): 216--225.
[15]
URL: http://www.ncbi.nlm.nih.gov/genome/913?genome_assembly_id=205296
[16]
Desler, C., Suravajhala, P., Sanderhoff, M., Rasmussen, M., and Rasmussen, L. J. 2009. In silico screening for functional candidates amongst hypothetical proteins; BMC Bioinformatics. 10:289.
[17]
Doerks, T., Noort, V., Minguez, P., and Bork, P. 2012. Annotation of the M. tuberculosis Hypothetical Orfeome: Adding Functional Information to More than Half of the Uncharacterized Proteins. PLoS ONE. 7(4): e34302.
[18]
Lubec, G., Afjehi-Sadat, L., Yang, J., John, J. P. P. 2005. Searching for hypothetical proteins: Theory and practice based upon original data and literature. Progress in Neurobiology. 77 (1-2): 90--127.
[19]
Butt, A. M., Batool, M., and Tong, Y. 2011. Homology modeling, comparative genomics and functional annotation of Mycoplasma genitalium hypothetical protein MG_237. Bioinformation. 7(6): 299--303.
[20]
Elias, D. A., Mukhopadhyay, A., Joachimiak, M. P. et al. 2009. Expression profiling of hypothetical genes in Desulfovibrio vulgaris leads to improved functional annotation. Nucleic Acids Research. 37(9): 2926--2939.
[21]
Jitendra, S., Narula, R., Agnihotri, S., and Singh, M, 2011. Annotation of hypothetical proteins orthologous in Pongo abelii and Sus scrofa. Bioinformation. 6(8): 297--299.
[22]
Yu, C. S., Chen, Y. C., Lu, C. H., and Hwang, J. K. 2006. Prediction of protein subcellular localization. Proteins Structure Function and Bioinformatics. 64(3):643--651.
[23]
Hao, L., Yan, L., Feng, G., Chun, T. Z., and Ren, Z, 2014. DEG 10, an update of the Database of Essential Genes that includes both protein-coding genes and non-coding genomic elements. Nucleic Acids Research. 42(D1): D574--D580.
[24]
Finn, R. D., Clements, J., and Eddy S. R. 2011. HMMER web server: interactive sequence similarity searching. Nucleic Acids Research. 39(Web Server issue): W29--W37.
[25]
Marchler, B. A. et al. 2011. CDD: a Conserved Domain Database for the functional annotation of proteins. Nucleic Acids Res. 39 (suppl_1): D225-D229.
[26]
Götz, S. et al. 2011. B2G-FAR, a species centered GO annotation repository. Bioinformatics. 27 (7):919--924.
[27]
Götz, S. et al. 2014. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Research. 36:3420--3435.
[28]
Kelley, L. A. and Sternberg, M. J. E. 2009. Protein structure prediction on the web: a case study using the Phyre server, Nature Protocols 4, 363--371.
[29]
Oliveros, J. C. 2007 VENNY. An interactive tool for comparing lists with Venn diagrams, URL:http://bioinfogp.cnb.csic.es/tools/venny/index.html.
[30]
Oliveros, J. C. (2007-2015) Venny. An interactive tool for comparing lists with Venn's diagrams. http://bioinfogp.cnb.csic.es/tools/venny/index.html
Index Terms
- Functional Annotation of Hypothetical proteins of Lactobacillus rhamnosus: An In Silico Approach
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June 2017
184 pages
ISBN:9781450352222
DOI:10.1145/3121138
Copyright © 2017 ACM.
© 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
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- Natl University of Singapore: National University of Singapore
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Association for Computing Machinery
New York, NY, United States
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Published: 22 June 2017
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ICBBS '17
ICBBS '17: 6th International Conference on Bioinformatics and Biomedical Science
June 22 - 24, 2017
Singapore, Singapore
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View all- Patt MSolanki JGeorrge J(undefined)In Silico Analysis of Hypothetical Proteins of Haemophilus Influenzae PittGG StrainSSRN Electronic Journal10.2139/ssrn.3574587
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