Mature Epitope Density (MED) 1.0 Server
MED is a server to predict target proteins for reverse vaccinology according to the number of 9-mer epitopes in the mature protein portion. It makes predictions over amino acid sequences from prokaryotic organisms running the predictors programs SurfG+, TMHMM, and NetMHC.

SUBMISSION

Submit a file in FASTA format directly from your local disk:


  Organism group:
   Gram-Positive
   Gram-Negative

  Cell wall measure in amino acids:
  AA

  E-mail for delivery of processing results:
  example: email@todelivery.results
  Please, ensure correct e-mail typing, or you will not receive any results.

   

Restrictions:
The limitation is processing time. For 2,000 orfs the expected execution time is about 60 minutes.

Confidentiality:
The sequences are kept confidential and will be deleted after processing.

CITATION:
Mature Epitope Density – A strategy for target selection based on immunoinformatics and exported prokaryotic proteins
Anderson Santos1,5, Vanessa Pereira1, Eudes Barbosa1,2, Jan Baumbach2,4, Josch Pauling2,4, Richard Rottger2,4, Meritxell Zurita Turk1, Artur Silva3, Anderson Miyoshi1 and Vasco Azevedo1

1 Molecular and Cellular Genetics Laboratory, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brasil.
2 Computational Biology Research Group, Department of Mathematics and Computer Science, University of Southern Denmark, Campusvej, Odense M, Denmark
3 DNA Polymorphism Laboratory, Universidade Federal do Para, Campus do Guama, Belem, Para, Brasil.
4 Computational Systems Biology group, Max Planck Institute for Informatics, Campus E2.1, 66123 Saarbrucken, Germany.
5 Computational Biology Laboratory, Faculty of Computing (FACOM), Universidade Federal de Uberlândia, Uberlândia, Minas Gerais, Brasil.



GETTING HELP
Technical and scientific problems: Anderson Santos


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This software was cited by:

1. Slayden RA, Dawson CC, Cummings JE. Toxin-antitoxin systems and regulatory mechanisms in Mycobacterium tuberculosis [Internet]. Vol. 76, Pathogens and Disease. 2018. Link

2. Silva MT de O, Bezerra FSB, de Pinho RB, Begnini KR, Seixas FK, Collares T, et al. Association of Corynebacterium pseudotuberculosis recombinant proteins rCP09720 or rCP01850 with rPLD as immunogens in caseous lymphadenitis immunoprophylaxis. Vaccine [Internet]. 2018; Link

3. Brum AA, Rezende A de FS, Brilhante FS, Collares T, Begnine K, Seixas FK, et al. Recombinant esterase from Corynebacterium pseudotuberculosis in DNA and subunit recombinant vaccines partially protects mice against challenge. J Med Microbiol [Internet]. 2017 May 1; 66(5):635-42. Link

4. Farrell D, Chubb AJ, Rue-Albrecht K, Malone K, Pirson C, Jones G, et al. Integrated computational prediction and experimental validation identifies promiscuous T cell epitopes in the proteome of Mycobacterium bovis. Microb Genomics [Internet]. 2016; Link

5. Tapia D, Ross BN, Kalita A, Kalita M, Hatcher CL, Muruato LA, et al. From In silico Protein Epitope Density Prediction to Testing Escherichia coli O157:H7 Vaccine Candidates in a Murine Model of Colonization. Front Cell Infect Microbiol [Internet]. 2016 Aug 30; 6:94. Link

6. Angelo HR, Leal KS, Dellagostin O, Santos A, Borsuk S, Portela RW, et al. In silico identification of Corynebacterium pseudotuberculosis antigenic targets and application in immunodiagnosis. J Med Microbiol [Internet]. 2016 Jun 1; 65(6):521-9. Link

7. Guimarães LC, Soares S de C, Trost E, Blom J, Ramos RTJ, Silva A, et al. Genome informatics and vaccine targets in Corynebacterium urealyticum using two whole genomes, comparative genomics, and reverse vaccinology. BMC Genomics [Internet]. 2015; Link

8. Barbosa EG, Aburjaile FF, Ramos RT, Carneiro AR, Le Loir Y, Baumbach J, et al. Value of a newly sequenced bacterial genome. World J Biol Chem [Internet]. 2014; Link



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