Identification of Drug and Vaccine Target in Mycobacterium leprae: A Reverse Vaccinology Approach

Mycobacterium leprae, an infectious agent of chronic infection so-called Leprosy. It is a prime healthconcern in various countries including India. India is currently running one of the extensive leprosy eradication programs in the globe, named as the National Leprosy Eradication Program (NLEP). Still, the situation is getting substandard because of the emergence of resistant strains. In the present study, newer approaches –like computational subtractive proteomics and reverse vaccinology has been applied in order to find out probable drug targets and vaccine candidates. The systematic workflow of the current study consists of a computational approach, where complete proteome of the bacteria is gradually reduced to find out few unique probable drug targets and reverse vaccinology, to find out probable vaccine antigens. Reverse vaccinology approach does not require a pathogen to be grown in the laboratory, encouraging its application to microorganisms that may not be easily cultivated like M. leprae but at least have an accessible genome sequence. This approach facilitates an easier and productive process ofantigen discovery. Results from the present study could facilitate selecting M. leprae proteins for drug design as well as vaccine production pipelines in the future.

[1]  T. Yeates,et al.  Verification of protein structures: Patterns of nonbonded atomic interactions , 1993, Protein science : a publication of the Protein Society.

[2]  D. Eisenberg,et al.  VERIFY3D: assessment of protein models with three-dimensional profiles. , 1997, Methods in enzymology.

[3]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[4]  Yongqun He,et al.  Vaxign: The First Web-Based Vaccine Design Program for Reverse Vaccinology and Applications for Vaccine Development , 2010, Journal of biomedicine & biotechnology.

[5]  O. Nascimento Leprosy neuropathy: clinical presentations. , 2013, Arquivos de neuro-psiquiatria.

[6]  Ajay Kumar,et al.  Computational Identification and Characterization of Potential T-Cell Epitope for the Utility of Vaccine Design Against Enterotoxigenic Escherichia coli , 2018, International Journal of Peptide Research and Therapeutics.

[7]  J M Thornton,et al.  LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. , 1995, Protein engineering.

[8]  K. Khoo,et al.  Mycobacterial lipoarabinomannan: an extraordinary lipoheteroglycan with profound physiological effects. , 1998, Glycobiology.

[9]  R. Rappuoli,et al.  Group B Streptococcus: global incidence and vaccine development , 2006, Nature Reviews Microbiology.

[10]  Michael F Marmor,et al.  The ophthalmic trials of G. H. A. Hansen. , 2002, Survey of ophthalmology.

[11]  Davide Heller,et al.  STRING v10: protein–protein interaction networks, integrated over the tree of life , 2014, Nucleic Acids Res..

[12]  Torsten Schwede,et al.  BIOINFORMATICS Bioinformatics Advance Access published November 12, 2005 The SWISS-MODEL Workspace: A web-based environment for protein structure homology modelling , 2022 .

[13]  S. V. van Beers,et al.  Distribution and persistence of Mycobacterium leprae nasal carriage among a population in which leprosy is endemic in Indonesia. , 1995, Transactions of the Royal Society of Tropical Medicine and Hygiene.

[14]  Chih-Wen Che,et al.  CELLO2GO: A Web Server for Protein subCELlular LOcalization Prediction with Functional Gene Ontology Annotation , 2014 .

[15]  Alessandro Sette,et al.  Reverse vaccinology: developing vaccines in the era of genomics. , 2010, Immunity.

[16]  P. Brennan,et al.  Lipoarabinomannan, a possible virulence factor involved in persistence of Mycobacterium tuberculosis within macrophages , 1991, Infection and immunity.

[17]  M. Suar,et al.  Comparative genomics study for identification of drug and vaccine targets in Vibrio cholerae: MurA ligase as a case study. , 2014, Genomics.

[18]  Jeyakumar Natarajan,et al.  Computational genome analyses of metabolic enzymes in Mycobacterium leprae for drug target identification , 2010, Bioinformation.

[19]  David S. Goodsell,et al.  AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility , 2009, J. Comput. Chem..

[20]  U. Farooq,et al.  Computational identification of potential drug targets against Mycobacterium leprae , 2016, Medicinal Chemistry Research.

[21]  M. Sternberg,et al.  Protein structure prediction on the Web: a case study using the Phyre server , 2009, Nature Protocols.

[22]  A. Vargas-González,et al.  Possible mode of emergence for drug-resistant leprosy is revealed by an analysis of samples from Mexico. , 2010, Japanese journal of infectious diseases.

[23]  D. Eisenberg,et al.  Assessment of protein models with three-dimensional profiles , 1992, Nature.

[24]  Martin Ester,et al.  PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes , 2010, Bioinform..

[25]  X. Chen,et al.  SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence , 2003, Nucleic Acids Res..

[26]  Archana P Kumar,et al.  Molecular detection of multidrug-resistant Mycobacterium leprae from Indian leprosy patients. , 2017, Journal of global antimicrobial resistance.

[27]  I. Cooper,et al.  Predicting conserved essential genes in bacteria: in silico identification of putative drug targets. , 2010, Molecular bioSystems.

[28]  Narmada Thanki,et al.  CDD: NCBI's conserved domain database , 2014, Nucleic Acids Res..

[29]  Alberto Parra,et al.  Identification of Novel Vaccine Candidates against Campylobacter through Reverse Vaccinology , 2016, Journal of immunology research.

[30]  Ajay Kumar,et al.  Exploring Highly Antigenic Protein of Campylobacter jejuni for Designing Epitope Based Vaccine: Immunoinformatics Approach , 2018, International Journal of Peptide Research and Therapeutics.

[31]  Damian Szklarczyk,et al.  STITCH 3: zooming in on protein–chemical interactions , 2011, Nucleic Acids Res..

[32]  E. Birney,et al.  Pfam: the protein families database , 2013, Nucleic Acids Res..

[33]  Srinivasan Ramachandran,et al.  SPAAN: a software program for prediction of adhesins and adhesin-like proteins using neural networks , 2004, Bioinform..

[34]  J. Suh,et al.  Putative drug and vaccine target protein identification using comparative genomic analysis of KEGG annotated metabolic pathways of Mycoplasma hyopneumoniae. , 2013, Genomics.

[35]  U. Amineni,et al.  In silico identification of common putative drug targets in Leptospira interrogans , 2010, Journal of chemical biology.

[36]  J. Thornton,et al.  PROCHECK: a program to check the stereochemical quality of protein structures , 1993 .

[37]  Rolf Apweiler,et al.  InterProScan - an integration platform for the signature-recognition methods in InterPro , 2001, Bioinform..

[38]  Jenn-Kang Hwang,et al.  CELLO2GO: A Web Server for Protein subCELlular LOcalization Prediction with Functional Gene Ontology Annotation , 2014, PloS one.

[39]  Erik L. L. Sonnhammer,et al.  Advantages of combined transmembrane topology and signal peptide prediction—the Phobius web server , 2007, Nucleic Acids Res..

[40]  L. Holm,et al.  The Pfam protein families database , 2005, Nucleic Acids Res..

[41]  Rahmah Mohamed,et al.  In Silico Analysis of Burkholderia pseudomallei Genome Sequence for Potential Drug Targets , 2006, Silico Biol..

[42]  Jenn-Kang Hwang,et al.  Predicting subcellular localization of proteins for Gram‐negative bacteria by support vector machines based on n‐peptide compositions , 2004, Protein science : a publication of the Protein Society.

[43]  Jie Liang,et al.  CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues , 2006, Nucleic Acids Res..

[44]  James R. Brown,et al.  Bioinformatics and the discovery of novel anti-microbial targets. , 2002, Current drug targets. Infectious disorders.

[45]  Irini A. Doytchinova,et al.  BMC Bioinformatics BioMed Central Methodology article VaxiJen: a server for prediction of protective antigens, tumour , 2007 .