PL EN


Preferences help
enabled [disable] Abstract
Number of results
2018 | 65 | 2 | 209-218
Article title

Prioritizing and modelling of putative drug target proteins of Candida albicans by systems biology approach

Content
Title variants
Languages of publication
EN
Abstracts
EN
Candida albicans (Candida albicans) is one of the major sources of nosocomial infections in humans which may prove fatal in 30% of cases. The hospital acquired infection is very difficult to treat affectively due to the presence of drug resistant pathogenic strains, therefore there is a need to find alternative drug targets to cure this infection. In silico and computational level frame work was used to prioritize and establish antifungal drug targets of Candida albicans. The identification of putative drug targets was based on acquiring 5090 completely annotated genes of Candida albicans from available databases which were categorized into essential and non-essential genes. The result indicated that 9% of proteins were essential and could become potential candidates for intervention which might result in pathogen eradication. We studied cluster of orthologs and the subtractive genomic analysis of these essential proteins against human genome was made as a reference to minimize the side effects. It was seen that 14% of Candida albicans proteins were evolutionary related to the human proteins while 86% are non-human homologs. In the next step of compatible drug target selections, the non-human homologs were sequentially compared to the human microbiome data to minimize the potential effects against gut flora which accumulated to 38% of the essential genome. The sub-cellular localization of these candidate proteins in fungal cellular systems indicated that 80% of them are cytoplasmic, 10% are mitochondrial and the remaining 10% are associated with the cell wall. The role of these non-human and non-gut flora putative target proteins in Candida albicans biological pathways was studied. Due to their integrated and critical role in Candida albicans replication cycle, four proteins were selected for molecular modeling. For drug designing and development, four high quality and reliable protein models with more than 70% sequence identity were constructed. These proteins are used for the docking studies of the known and new ligands (unpublished data). Our study will be an effective framework for drug target identifications of pathogenic microbial strains and development of new therapies against the infections they cause.
Publisher

Year
Volume
65
Issue
2
Pages
209-218
Physical description
Dates
published
2018
received
2017-09-21
revised
2018-01-08
accepted
2018-02-24
(unknown)
2018-06-18
Contributors
author
  • Department of Pharmacy, COMSATS Institute of Information Technology, Abbottabad, 22060, Pakistan
author
  • Department of Pharmacy, COMSATS Institute of Information Technology, Abbottabad, 22060, Pakistan
author
  • Institute of Molecular Biology and BioTechnology, Bahauddin Zakariya University, Multan, 46000, Pakistan
author
  • Department of Pharmaceutics, University of Florida, Gainesville, 36090, USA
author
  • Department of Pharmacy, COMSATS Institute of Information Technology, Abbottabad, 22060, Pakistan
author
  • Department of Pharmacy, COMSATS Institute of Information Technology, Abbottabad, 22060, Pakistan
  • Department of Pharmaceutics Margella Institute of Health Science, Rawalpindi, 44000 Pakistan
  • Department of Pharmacy, COMSATS Institute of Information Technology, Abbottabad, 22060, Pakistan
author
  • Department of Pharmacy, COMSATS Institute of Information Technology, Abbottabad, 22060, Pakistan
References
  • Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Research 25: 3389-3402.doi: 10.1093/nar/25.17.3389.
  • Arias CA, Murray BE (2009) Antibiotic-Resistant Bugs in the 21st Century - A Clinical Super-Challenge. New England Journal of Medicine 360: 439-443.doi: 10.1056/NEJMp0804651.
  • Arnold K, Bordoli L, Kopp J, Schwede T (2006) The SWISS-MODEL workspace: A web-based environment for protein structure homology modelling. Bioinformatics 22: 195-201.doi: 10.1093/bioinformatics/bti770.
  • Barh D, Tiwari S, Jain N, Ali A, Santos AR, Misra AN, Kumar A (2011) In silico subtractive genomics for target identification in human bacterial pathogens. Drug Develop Res 72: 162-177.doi: 10.1002/ddr.20413
  • Bauer-Mehren A (2013) Integration of genomic information with biological networks using Cytoscape. Methods Mol Biol 1021: 37-61.doi: 10.1007/978-1-62703-450-0_3.
  • Beaugerie L, Petit JC (2004) Microbial-gut interactions in health and diseaseAntibiotic-associated diarrhoea. Best Pract Res Clin Gastroenterol 18: 337-52.doi: 10.1016/j.bpg.2003.10.002.
  • Benkert P, Biasini M, Schwede T (2011) Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics 27: 343-350.doi: 10.1093/bioinformatics/btq662.
  • Benkert P, Künzli M, Schwede T (2009) QMEAN server for protein model quality estimation. Nucleic Acids Res 37 (Suppl 2).doi: 10.1093/nar/gkp322.
  • Biasini M, Bienert S, Waterhouse A, Arnold K, Studer G, Schmidt T, Schwede T (2014) SWISS-MODEL: Modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res 42: W252-W258.doi: 10.1093/nar/gku340.
  • Blom N, Gammeltoft S, Brunak S (1999) Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. J Mol Biol 294: 1351-62.doi: 10.1006/jmbi.1999.3310.
  • Bordoli L, Kiefer F, Arnold K, Benkert P, Battey J, Schwede T (2009) Protein structure homology modeling using SWISS-MODEL workspace. Nat Protocols 4: 1-13.doi: 10.1038/nprot.2008.197.
  • Boyanova L, Kolarov R, Mitov I (2007) Antimicrobial resistance and the management of anaerobic infections. Expert Rev Anti Infect Ther 5: 685-701.doi: 10.1586/14787210.5.4.685.
  • Braun BR, van het Hoog M, d'Enfert C, Martchenko M, Dungan J, Kuo A, Nantel A (2005) A human-curated annotation of the Candida albicans genome. PLoS Genetics 1: 36-57.doi: 10.1371/journal.pgen.0010001.
  • Carman RJ, Simon MA, Fernández H, Miller MA, Bartholomew MJ (2004) Ciprofloxacin at low levels disrupts colonization resistance of human fecal microflora growing in chemostats. Regul Toxicol Pharmacol 40: 319-326.doi: 10.1016/j.yrtph.2004.08.005.
  • Chan JN, Nislow C, Emili A (2010) Recent advances and method development for drug target identification. Trends Pharmacol Sci 31: 82-88.doi: 10.1016/j.tips.2009.11.002.
  • Fischbach MA, Walsh CT (2010) NIH Public Access 325 (5944) 1089-1093.doi: 10.1126/science.1176667
  • Garcı S (2004) Candida albicans. Society 3: 536-545. doi: 10.1128/EC.3.2.536
  • Guarner F, Malagelada JR (2003) Gut flora in health and disease. Lancet 361: 512-519.doi: 10.1016/S0140-6736(03)12489-0.
  • Guex N, Peitsch MC (1997) SWISS-MODEL and the Swiss-PdbViewer: An environment for comparative protein modeling. Electrophoresis 18: 2714-2723.doi: 10.1002/elps.1150181505.
  • Gupta R, Jung E, Brunak S (2002) Prediction of N-glycosylation sites in human proteins. Pacific Symposium on Biocomputing 7: 310-322
  • http://fungidb.org/common/downloads/release-2.0/Calbicans_SC5314/fasta/data/(n.d.) No Title.
  • http://www.expasy.org/(n.d.) No Title. Retrieved from https://www.expasy.org/
  • Hugot JP (2004) Inflammatory bowel disease: a complex group of genetic disorders. Best Pract Res Clin Gastroenterol 18: 451-462.doi: 10.1016/j.bpg.2004.01.001.
  • Jensen LJ, Gupta R, Blom N, Devos D, Tamames J, Kesmir C, Brunak S (2002) Prediction of human protein function from post-translational modifications and localization features. J Mol Biol 319: 1257-1265.doi: 10.1016/S0022-2836 (02)00379-0.
  • Jernberg C, Löfmark S, Edlund C, Jansson JK (2010) Long-term impacts of antibiotic exposure on the human intestinal microbiota. Microbiology 156: 3216-3223.doi: 10.1099/mic.0.040618-0.
  • Ji YD (2002) The role of genomics in the discovery of novel targets for antibiotic therapy. Pharmacogenomics 3: 315-323.doi: doi 10.1517/14622416.3.3.315.
  • Kabir MA, Hussain MA, Ahmad Z (2012) Candida albicans: a model organism for studying fungal pathogens. ISRN Microbiol 2012: 538694.doi: 10.5402/2012/538694.
  • Kiemer L, Bendtsen JD, Blom N (2005) NetAcet: Prediction of N-terminal acetylation sites. Bioinformatics 21: 1269-1270.doi: 10.1093/bioinformatics/bti130.
  • Lewis GD, Wand JD (2013) Analysis of the human gut microbiome and association with diseaseblic access. Clin Gastroenterol Hepatol 11: 774-777.doi: 10.1016/j.cgh.2013.03.038.
  • Lin J, Qian J (2007) Systems biology approach to integrative comparative genomics. Expert Rev Proteomics 4: 107-19.doi: 10.1586/14789450.4.1.107.
  • Lu Z, Szafron D, Greiner R, Lu P, Wishart DS, Poulin B, Eisner R (2004) Predicting subcellular localization of proteins using machine-learned classifiers. Bioinformatics 20: 547-556.doi: 10.1093/bioinformatics/btg447.
  • Luo H, Lin Y, Gao F, Zhang CT, Zhang R (2014) DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements. Nucleic Acids Res 42: D574-D580.doi: 10.1093/nar/gkt1131.
  • Marton MJ, DeRisi JL, Bennett H, Iyer VR, Meyer MR, Roberts CJ, Friend SH (1998) Drug target validation and identification of secondary drug target effects using DNA microarrays. Nat Med 4: 1293-1301.doi: 10.1038/3282.
  • Materi W, Wishart DS (2007) Computational systems biology in drug discovery and development: methods and applications. Drug Discovery Today 12: 295-303.doi: 10.1016/j.drudis.2007.02.013.
  • Materi W, Wishart DS (2007) Computational systems biology in drug discovery and development: Methods and applications. Drug Discov Today 12: 295-303.doi: 10.1016/j.drudis.2007.02.013.
  • Mdluli K, Spigelman M (2006) Novel targets for tuberculosis drug discovery. Curr Opin Pharmacol 6: 459-467.doi: 10.1016/j.coph.2006.06.004.
  • Meiller TF, Hube B, Schild L, Shirtliff ME, Scheper MA, Winkler R, Jabra-Rizk MA (2009) A novel immune evasion strategy of Candida albicans: Proteolytic cleavage of a salivary antimicrobial peptide. PLoS One 4: e5039.doi: 10.1371/journal.pone.0005039.
  • Muhammad SA, Ahmed S, Ali A, Huang H, Wu X, Yang XF, Chen J (2014) Prioritizing drug targets in Clostridium botulinum with a computational systems biology approach. Genomics. doi: 10.1016/j.ygeno.2014.05.002.
  • O'Hara AM, Shanahan F (2006) The gut flora as a forgotten organ. EMBO Rep 7: 688-693.doi: 10.1038/sj.embor.7400731.
  • Payne S, Gibson G, Wynne A, Hudspith B, Brostoff J, Tuohy K (2003) In vitro studies on colonization resistance of the human gut microbiota to Candida albicans and the effects of tetracycline and Lactobacillus plantarum LPK. Curr Issues Intest Microbiol 4: 1-8.
  • Perumal D, Lim CS, Sakharkar KR, Sakharkar MK (2007) Differential genome analyses of metabolic enzymes in Pseudomonas aeruginosa for drug target identification. In Silico Biol 7: 453-465.
  • Pucci MJ (2006) Use of genomics to select antibacterial targets. Biochem Pharmacol.doi: 10.1016/j.bcp.2005.12.004.
  • Raman K, Chandra N (2008) Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance. BMC Microbiol 8.doi: 10.1186/1471-2180-8-234.
  • Rausch F, Schicht M, Brauer L, Paulsen F, Brandt W (2014) Protein modeling and molecular dynamics simulation of the two novel surfactant proteins SP-G and SP-H. J Mol Model 20: 2513.doi: 10.1007/s00894-014-2513-0.
  • Remmert M, Biegert A, Hauser A, Söding J (2011) HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat Methods 9: 173-175.doi: 10.1038/nmeth.1818.
  • Ren J, Wen L, Gao X, Jin C, Xue Y, Yao X (2008) CSS-Palm 2.0: An updated software for palmitoylation sites prediction. Protein Eng Des Sel 21: 639-644.doi: 10.1093/protein/gzn039.
  • Riesbeck K (2013) Candida albicans is a crafty microbe that deceives its host by using complement regulators and proteases. J Infect Dis 207: 550-552.doi: 10.1093/infdis/jis722.
  • Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234: 779-815.doi: 10.1006/jmbi.1993.1626.
  • Schulze J, Sonnenborn U (2009) Pilze im Darm - von kommensalen Untermietern zu Infektionserregern. Dtsch Arztebl Int 106: 837–842.doi: 10.3238/arztebl.2009.0837 (in German).
  • Scully C, El-Kabir M, Samaranayake LP (1994) Candida and Oral Candidosis: A Review. Crit Rev Oral Biol Med 5: 125-157.doi: 10.1177/10454411940050020101.
  • Singh NK, Selvam SM, Chakravarthy P (2006) T-iDT : tool for identification of drug target in bacteria and validation by Mycobacterium tuberculosis. In Silico Biol 6: 485-493. doi: 2006060045.
  • Soll DR, Staebell M, Langtimm C, Pfaller M, Hicks J, Rao TV (1988) Multiple Candida strains in the course of a single systemic infection. J Clin Microbiol 26: 1448-1459.
  • Steentoft C, Vakhrushev SY, Joshi HJ, Kong Y, Vester-Christensen MB, Schjoldager KT, Clausen H (2013) Precision mapping of the human O-GalNAc glycoproteome through SimpleCell technology. EMBO J 32: 1478-88.doi: 10.1038/emboj.2013.79.
  • Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Von Mering C (2011) The STRING database in 2011: Functional interaction networks of proteins globally integrated and scored. Nucleic Acids Res 39 (Suppl 1). doi: 10.1093/nar/gkq973.
  • Volkswirtschaftliche Z (2005) Innovation in the Pharmaceutical Industry - Future Prospects. Roche: Prevention 1-8.
  • von Mering C, Jensen LJ, Kuhn M, Chaffron S, Doerks T, Krüger B, Bork P (2007) STRING 7 - recent developments in the integration and prediction of protein interactions. Nucleic Acids Res 35 (Database issue) D358-D362.doi: 10.1093/nar/gkl825.
  • Zhang R, Lin Y (2009) DEG 5.0, a database of essential genes in both prokaryotes and eukaryotes. Nucleic Acids Res 37 (Suppl 1) D455-D458.doi: 10.1093/nar/gkn858.
  • Zhang R, Ou HY, Zhang CT (2004) DEG: a database of essential genes. Nucleic Acids Res 32 (Database issue) D271-D272.doi: 10.1093/nar/gkh024.
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.bwnjournal-article-abpv65p209kz
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.