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Identification of novel therapeutic candidates in Cryptosporidium parvum: an in silico approach

Published online by Cambridge University Press:  25 April 2018

Chinmaya Panda  and
Rajani Kanta Mahapatra Open the ORCID record for Rajani Kanta Mahapatra [Opens in a new window]
Chinmaya Panda
Affiliation:
Department of Computer Science and Engineering, National Institute of Technology Patna, Patna-800005, India
Rajani Kanta Mahapatra*
Affiliation:
School of Biotechnology, KIIT University, Bhubaneswar-751024, Odisha, India
*
Author for correspondence: Rajani Kanta Mahapatra, E-mail: [email protected]
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Abstract

Unavailability of vaccines and effective drugs are primarily responsible for the growing menace of cryptosporidiosis. This study has incorporated a bioinformatics-based screening approach to explore potential vaccine candidates and novel drug targets in Cryptosporidium parvum proteome. A systematic strategy was defined for comparative genomics, orthology with related Cryptosporidium species, prioritization parameters and MHC class I and II binding promiscuity. The approach reported cytoplasmic protein cgd7_1830, a signal peptide protein, as a novel drug target. SWISS-MODEL online server was used to generate the 3D model of the protein and was validated by PROCHECK. The model has been subjected to in silico docking study with screened potent lead compounds from the ZINC database, PubChem and ChEMBL database using Flare software package of Cresset®. Furthermore, the approach reported protein cgd3_1400, as a vaccine candidate. The predicted B- and T-cell epitopes on the proposed vaccine candidate with highest scores were also subjected to docking study with MHC class I and II alleles using ClusPro web server. Results from this study could facilitate selection of proteins which could serve as drug targets and vaccine candidates to efficiently tackle the growing threat of cryptosporidiosis.

Keywords

Cryptosporidium parvumDrug targetsHomology modelingMolecular DockingVaccine candidate
Type
Research Article
Information
Parasitology , Volume 145 , Issue 14 , December 2018 , pp. 1907 - 1916
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Copyright
Copyright © Cambridge University Press 2018 

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References

Abubakar, I, et al. (2007) Treatment of cryptosporidiosis in immunocompromised individuals: systematic review and meta-analysis. British Journal of Clinical Pharmacology 63(4), 387–393.Google Scholar
Agüero, F, et al. (2008) Genomic-scale prioritization of drug targets: the TDR Targets database. Nature Reviews Drug Discovery 7(11), 900.Google Scholar
Anishetty, S, Pulimi, M and Pennathur, G (2005) Potential drug targets in Mycobacterium tuberculosis through metabolic pathway analysis. Computational Biology and Chemistry 29(5), 368–378.Google Scholar
Benamrouz, S, et al. (2014) Cryptosporidium parvum-induced ileo-caecal adenocarcinoma and Wnt signaling in a mouse model. Disease Models & Mechanisms 7(6), 693–700.Google Scholar
Bernstein, FC, et al. (1977) The protein data bank: a computer-based archival file for macromolecular structures. Journal of Molecular Biology 112(3), 535–542.Google Scholar
Bessoff, K, et al. (2014) Identification of Cryptosporidium parvum active chemical series by Repurposing the open access malaria box. Antimicrobial Agents and Chemotherapy 58(5), 2731–2739.Google Scholar
Bhasin, M and Raghava, GPS (2004) Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine 22(23–24), 3195–3204.Google Scholar
Biasini, M, et al. (2014) SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Research 42(W1), W252–W258.Google Scholar
Butt, AM, et al. (2011) Mycoplasma genitalium: a comparative genomics study of metabolic pathways for the identification of drug and vaccine targets. Infection, Genetics and Evolution 12(1), 53–62.Google Scholar
Carlisle, SM, et al. (1990) Pyrophosphate-dependent phosphofructokinase. Conservation of protein sequence between the alpha-and beta-subunits and with the ATP-dependent phosphofructokinase. Journal of Biological Chemistry 265(30), 18366–18371.Google Scholar
Caspi, R, et al. (2016) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Research 44(D1), D471–D480.Google Scholar
Colovos, C and Yeates, TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Science 2(9), 1511–1519.Google Scholar
Cresset®. (2006) Flare v. 1.0.0. Litlington, Cambridgeshire, UK.Google Scholar
Desai, NT, Sarkar, R and Kang, G (2012) Cryptosporidiosis: an under-recognized public health problem. Tropical Parasitology 2(2), 91–98.Google Scholar
Doytchinova, IA and Flower, DR (2007) Vaxijen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 8(1), 4.Google Scholar
Efstratiou, A, Ongerth, JE and Karanis, P (2017) Waterborne transmission of protozoan parasites: review of worldwide outbreaks-an update 2011–2016. Water Research 114, 14–22.Google Scholar
Eisenberg, D, Lüthy, R and Bowie, JU (1997) [20] VERIFY3D: assessment of protein models with three-dimensional profiles. In Methods in Enzymology. (ed. Carter, CW JR. and Sweet, RM.) vol. 277. Academic Press, Cambridge, MA, USA, 396–404.Google Scholar
Finn, RD, et al. (2016) Interpro in 2017—beyond protein family and domain annotations. Nucleic Acids Research 45(D1), D190–D199.Google Scholar
Garg, A and Raghava, GP (2008) ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins. BMC Bioinformatics 9(1), 503.Google Scholar
Gaulton, A, et al. (2017) The ChEMBL database in 2017. Nucleic Acids Research 45(D1), D945–D954.Google Scholar
Ghosh, S, et al. (2014) Comparative genomics study for the identification of drug and vaccine targets in Staphylococcus aureus: MurA ligase enzyme as a proposed candidate. Journal of Microbiological Methods 101, 1–8.Google Scholar
Gronwald, JW, Miller, SS and Vance, CP (2008) Arabidopsis UDP-sugar pyrophosphorylase: evidence for two isoforms. Plant Physiology and Biochemistry 46(12), 1101–1105.Google Scholar
Gupta, S, et al. (2013) Identification of B-cell epitopes in an antigen for inducing specific class of antibodies. Biology Direct 8(1), 27.Google Scholar
Hajduk, PJ, Huth, JR and Tse, C (2005) Predicting protein druggability. Drug Discovery Today 10(23–24), 1675–1682.Google Scholar
Kai, Y, Matsumura, H and Izui, K (2003) Phosphoenolpyruvate carboxylase: three-dimensional structure and molecular mechanisms. Archives of Biochemistry and Biophysics 414(2), 170–179.Google Scholar
Kanehisa, M, et al. (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Research 45(D1), D353–D361.Google Scholar
Kather, B, et al. (2000) Another unusual type of citric acid cycle enzyme in Helicobacter pylori: the malate: quinone oxidoreductase. Journal of Bacteriology 182(11), 3204–3209.Google Scholar
Kim, S, et al. (2016) Pubchem substance and compound databases. Nucleic Acids Research 44(D1), D1202–D1213.Google Scholar
Kindt, TJ, et al. (2007) B-cell generation, activation, and differentiation. In Immunology. (ed. Goldsby, R). New York: WH Freeman and Company, 271–301.Google Scholar
Knox, C, et al. (2011) Drugbank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Research 39(suppl_1), D1035–D1041.Google Scholar
Kotake, T, et al. (2004) UDP-sugar pyrophosphorylase with broad substrate specificity toward various monosaccharide 1-phosphates from pea sprouts. Journal of Biological Chemistry 279(44), 45728–45736.Google Scholar
Kozakov, D, et al. (2017) The ClusPro web server for protein–protein docking. Nature Protocols 12(2), 255.Google Scholar
Krogh, A, et al. (2001) Predicting transmembrane protein topology with a hidden markov model: application to complete genomes. Journal of Molecular Biology 305(3), 567–580.Google Scholar
Laskowski, RA, et al. (1993) PROCHECK: a program to check the stereochemical quality of protein structures. Journal of Applied Crystallography 26(2), 283–291.Google Scholar
Li, L, Stoeckert, CJ and Roos, DS (2003) OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Research 13(9), 2178–2189.Google Scholar
Ludin, P, et al. (2012) In silico prediction of antimalarial drug target candidates. International Journal for Parasitology: Drugs and Drug Resistance 2, 191–199.Google Scholar
O'Boyle, NM, et al. (2011) Open Babel: an open chemical toolbox. Journal of Cheminformatics 3(1), 33.Google Scholar
Pan, YT, Carroll, JD and Elbein, AD (2002) Trehalose-phosphate synthase of Mycobacterium tuberculosis. The FEBS Journal 269(24), 6091–6100.Google Scholar
Petersen, TN, et al. (2011) Signalp 4.0: discriminating signal peptides from transmembrane regions. Nature Methods 8(10), 785.Google Scholar
Pieper, U, et al. (2013) Modbase, a database of annotated comparative protein structure models and associated resources. Nucleic Acids Research 42(D1), D336–D346.Google Scholar
Pontius, J, Richelle, J and Wodak, SJ (1996) Deviations from standard atomic volumes as a quality measure for protein crystal structures. Journal of Molecular Biology 264(1), 121–136.Google Scholar
Robinson, J, et al. (2015) The IPD and IMGT/HLA database: allele variant databases. Nucleic Acids Research 43(D1), D423–D431.Google Scholar
Roy, A and Zhang, Y (2012) Recognizing protein-ligand binding sites by global structural alignment and local geometry refinement. Structure 20(6), 987–997.Google Scholar
Saha, S and Raghava, GPS (2006 a) Algpred: prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Research 34(suppl_2), W202–W209.Google Scholar
Saha, S and Raghava, GPS (2006 b) Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins: Structure, Function, and Bioinformatics 65(1), 40–48.Google Scholar
Samie, A, et al. (2015) Challenges and innovative strategies to interrupt cryptosporidium transmission in resource-limited settings. Current Tropical Medicine Reports 2(3), 161–170.Google Scholar
Scallan, E, et al. (2011) Foodborne illness acquired in the United States-major pathogens. Emerging Infectious Diseases 17(1), 7.Google Scholar
Schrödinger, L (2010) The PyMOL Molecular Graphics System, Version 1.3r1. Portland, Oregon, United States.Google Scholar
Shanmugasundram, A, et al. (2012) Library of apicomplexan metabolic pathways: a manually curated database for metabolic pathways of apicomplexan parasites. Nucleic Acids Research 41(D1), D706–D713.Google Scholar
Shen, Y, et al. (2014) Improved PEP-FOLD approach for peptide and miniprotein structure prediction. Journal of Chemical Theory and Computation 10(10), 4745–4758.Google Scholar
Singh, H and Raghava, GPS (2001) Propred: prediction of HLA-DR binding sites. Bioinformatics (oxford, England) 17(12), 1236–1237.Google Scholar
Singh, H and Raghava, GPS (2003) Propred1: prediction of promiscuous MHC Class-I binding sites. Bioinformatics (oxford, England) 19(8), 1009–1014.Google Scholar
Snelling, WJ, et al. (2007) Cryptosporidiosis in developing countries. Journal of Infection in Developing Countries 1(03), 242–256.Google Scholar
Sterling, T and Irwin, JJ (2015) ZINC 15–ligand discovery for everyone. Journal of Chemical Information and Modeling 55(11), 2324–2337.Google Scholar
Stroganov, OV, et al. (2008) Lead finder: an approach to improve accuracy of protein−ligand docking, binding energy estimation, and virtual screening. Journal of Chemical Information and Modeling 48(12), 2371–2385.Google Scholar
Tan, BK, et al. (2012) Discovery of a cardiolipin synthase utilizing phosphatidylethanolamine and phosphatidylglycerol as substrates. Proceedings of the National Academy of Sciences of the United States opf America 109(41), 16504–16509.Google Scholar
The UniProt Consortium (2016) Uniprot: the universal protein knowledgebase. Nucleic Acids Research 45(D1), D158–D169.Google Scholar
Ward, JJ, et al. (2004) Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. Journal of Molecular Biology 337(3), 635–645.Google Scholar
Wass, MN, Kelley, LA and Sternberg, MJ (2010) 3DLigandSite: predicting ligand-binding sites using similar structures. Nucleic Acids Research 38(suppl_2), W469–W473.Google Scholar
Yu, CS, et al. (2006) Prediction of protein subcellular localization. Proteins: Structure, Function, and Bioinformatics 64(3), 643–651.Google Scholar
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