A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms

BMC Bioinformatics

Volumn 14, Issue , 2013, Pages

  Goodswen, Stephen J ^a   Kennedy, Paul J ^b   Ellis, John T ^a  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

AVERAGE SENSITIVITIES; CAENORHABDITIS ELEGANS; COMPUTATIONAL TOOLS; IMMUNE RESPONSE; LABORATORY INVESTIGATIONS; NOVEL STRATEGIES; SUBUNIT VACCINE; TOXOPLASMA GONDII;

LABORATORIES; LEARNING SYSTEMS; PIPELINES; PROTEINS; VACCINES;

LEARNING ALGORITHMS;

CAENORHABDITIS ELEGANS; EUKARYOTA; PLASMODIUM (APICOMPLEXA); PLASMODIUM SP.; TOXOPLASMA GONDII;

ANTIGEN; CAENORHABDITIS ELEGANS PROTEIN; PROTOZOAL PROTEIN; VACCINE;

ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGY; CHEMISTRY; COMPUTER SIMULATION; DRUG DEVELOPMENT; IMMUNOLOGY; METHODOLOGY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ANIMALS; ANTIGENS; ARTIFICIAL INTELLIGENCE; CAENORHABDITIS ELEGANS PROTEINS; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DRUG DISCOVERY; PROTOZOAN PROTEINS; SENSITIVITY AND SPECIFICITY; VACCINES;

EID: 84886737285     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-14-315     Document Type: Article

References (47)

1. Goodswen SJ, Kennedy PJ, Ellis JT. A guide to in silico vaccine discovery for eukaryotic pathogens. Brief Bioinform 2012, [Epub ahead of print].
2. Mora M, Donati C, Medini D, Covacci A, Rappuoli R. Microbial genomes and vaccine design: refinements to the classical reverse vaccinology approach. Current Opinion in Microbiology 2006, 9(5):532-536. 10.1016/j.mib.2006.07.003, 16890009.
3. Rappuoli R. Bridging the knowledge gaps in vaccine design. Nat Biotech 2007, 25(12):1361-1366.
4. He Y, Xiang Z, Mobley HLT. Vaxign: The First Web-Based Vaccine Design Program for Reverse Vaccinology and Applications for Vaccine Development. Journal of Biomedicine and Biotechnology 2010, 2010:297505. 2910479, 20671958.
5. Vivona S, Bernante F, Filippini F. NERVE: new enhanced reverse vaccinology environment. BMC Biotechnol 2006, 6(1):35. 10.1186/1472-6750-6-35, 1570458, 16848907.
6. Leuzzi R, Savino S, Pizza M, Rappuoli R. Genome Mining and Reverse Vaccinology 2006, 391-402. Wiley-VCH Verlag GmbH & Co. KGaA, Handbook of Meningococcal Disease.
7. Serino L, Pizza M, Rappuoli R. Reverse Vaccinology: Revolutionizing the Approach to Vaccine Design 2006, 533-554. Wiley-VCH Verlag GmbH & Co. KGaA, Pathogenomics.
8. Vivona S, Gardy JL, Ramachandran S, Brinkman FSL, Raghava GPS, Flower DR, Filippini F. Computer-aided biotechnology: from immuno-informatics to reverse vaccinology. Trends in biotechnology 2008, 26(4):190-200. 10.1016/j.tibtech.2007.12.006, 18291542.
9. Dyrløv Bendtsen J, Nielsen H, von Heijne G, Brunak S. Improved prediction of signal peptides: signalP 3.0. J Mol Biol 2004, 340(4):783-795. 10.1016/j.jmb.2004.05.028, 15223320.
10. Emanuelsson O, Brunak S, von Heijne G, Nielsen H. Locating proteins in the cell using targetP, signalP and related tools. Nat Protocols 2007, 2(4):953-971.
11. Horton P, Park K-J, Obayashi T, Fujita N, Harada H, Adams-Collier CJ, Nakai K. WoLF PSORT: protein localization predictor. Nucleic Acids Res 2007, 35(suppl 2):W585-W587. 1933216, 17517783.
12. Kall L, Krogh A, Sonnhammer ELL. A combined transmembrane topology and signal peptide prediction method. J Mol Biol 2004, 338(5):1027-1036. 10.1016/j.jmb.2004.03.016, 15111065.
13. Krogh A, Larsson B, von Heijne G, Sonnhammer ELL. Predicting transmembrane protein topology with a hidden markov model: application to complete genomes. J Mol Biol 2001, 305(3):567-580. 10.1006/jmbi.2000.4315, 11152613.
14. Peters B, Bui H-H, Frankild S, Nielsen M, Lundegaard C, Kostem E, Basch D, Lamberth K, Harndahl M, Fleri W, et al. A community resource benchmarking predictions of peptide binding to MHC-I molecules. PLoS Comput Biol 2006, 2(6):574-584.
15. Wang P, Sidney J, Dow C, Mothe B, Sette A, Peters B. A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. PLoS Comput Biol 2008, 4(4):e1000048. 10.1371/journal.pcbi.1000048, 2267221, 18389056.
16. Bhasin M, Raghava GPS. Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine 2004, 22(23-24):3195-3204.
17. Bowman BN, McAdam PR, Vivona S, Zhang J, Luong T, Belew RK, Sahota H, Guiney D, Valafar F, Fierer J, et al. Improving reverse vaccinology with a machine learning approach. Vaccine 2011, In Press, Uncorrected Proof.
18. Sollner J, Mayer B. Machine learning approaches for prediction of linear B-cell epitopes on proteins. J Mol Recognit 2006, 19(3):200-208. 10.1002/jmr.771, 16598694.
19. Rocchi MS, Bartley PM, Inglis NF, Collantes-Fernandez E, Entrican G, Katzer F, Innes EA. Selection of Neospora caninum antigens stimulating bovine CD4(+ve) T cell responses through immuno-potency screening and proteomic approaches. Veterinary Research 2011, 42:1-91. 3031223, 21314969.
20. Montoya JG, Liesenfeld O. Toxoplasmosis. Lancet 2004, 363(9425):1965-1976. 10.1016/S0140-6736(04)16412-X, 15194258.
21. Che F-Y, Madrid-Aliste C, Burd B, Zhang H, Nieves E, Kim K, Fiser A, Angeletti RH, Weiss LM. Comprehensive proteomic analysis of membrane proteins in toxoplasma gondii. Mol Cell Proteomics 2010, 10(1):M110 000745. 3013445, 20935347.
22. Kim K, Weiss LM. Toxoplasma gondii: the model apicomplexan. Int J Parasitol 2004, 34(3):423-432. 10.1016/j.ijpara.2003.12.009, 3086386, 15003501.
23. Roos DS. Themes and variations in apicomplexan parasite biology. Science 2005, 309(5731):72-73. 10.1126/science.1115252, 15994520.
24. Snow RW, Guerra CA, Noor AM, Myint HY, Hay SI. The global distribution of clinical episodes of plasmodium falciparum malaria. Nature 2005, 434(7030):214-217. 10.1038/nature03342, 3128492, 15759000.
25. Kurz CL, Ewbank JJ. Caenorhabditis elegans: an emerging genetic model for the study of innate immunity. Nat Rev Genet 2003, 4(5):380-390. 10.1038/nrg1067, 12728280.
26. Flower DR, Macdonald IK, Ramakrishnan K, Davies MN, Doytchinova IA. Computer aided selection of candidate vaccine antigens. Immunome Research 2010, 6(Suppl 2):S1.
27. Kaech SM, Wherry EJ, Ahmed R. Effector and memory T-cell differentiation: implications for vaccine development. Nat Rev Immunol 2002, 2(4):251-262. 10.1038/nri778, 12001996.
28. Sette A, Fikes J. Epitope-based vaccines: an update on epitope identification, vaccine design and delivery. Curr Opin Immunol 2003, 15(4):461-470. 10.1016/S0952-7915(03)00083-9, 12900280.
29. Petersen TN, Brunak S, von Heijne G, Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods 2011, 8(10):785-786. 10.1038/nmeth.1701, 21959131.
30. Kim Y, Ponomarenko J, Zhu Z, Tamang D, Wang P, Greenbaum J, Lundegaard C, Sette A, Lund O, Bourne PE, et al. Immune epitope database analysis resource. Nucleic Acids Res 2012, 40(W1):W525-W530. 10.1093/nar/gks438, 3394288, 22610854.
31. Kim Y, Sette A, Peters B. Applications for T-cell epitope queries and tools in the immune epitope database and analysis resource. J Immunol Methods 2011, 374(1-2):62-69. 3041860, 21047510.
32. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees 1984, Wadsworth International Group.
33. Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 1997, 55(1):119-139.
34. Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting. Ann Stat 2000, 28(2):337-374.
35. Breiman L. Random forests. Mach Learn 2001, 45(1):5-32.
36. Ripley BD. Pattern Recognition and Neural Networks 1996, Cambridge University Press, 1.
37. Venables WN, Ripley BD. Modern Applied Statistics with S 2002, Springer, 4.
38. Platt J. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers 1999, 1999:61-74.
39. Chambers JM, Hastie TJ. Statistical Models in S 1992, Wadsworth and Books/Cole Computer Science Series, Chapman and Hall.
40. Dubey JP, Carpenter JL, Speer CA, Topper MJ, Uggla A. Newly recognized fatal protozoan disease of dogs. J Am Vet Med Assoc 1988, 192(9):1269-1285.
41. Andrianarivo AG, Anderson ML, Rowe JD, Gardner IA, Reynolds JP, Choromanski L, Conrad PA. Immune responses during pregnancy in heifers naturally infected with neospora caninum with and without immunization. Parasitol Res 2005, 96(1):24-31. 10.1007/s00436-005-1313-y, 15841416.
42. Reichel MP, Ellis JT. Neospora caninum - how close are we to development of an efficacious vaccine that prevents abortion in cattle?. Int J Parasitol 2009, 39(11):1173-1187. 10.1016/j.ijpara.2009.05.007, 19497326.
43. Tuo WB, Fetterer R, Jenkins M, Dubey JP. Identification and characterization of neospora caninum cyclophilin that elicits gamma interferon production. Infect Immun 2005, 73(8):5093-5100. 10.1128/IAI.73.8.5093-5100.2005, 1201279, 16041025.
44. Cong H, Gu QM, Yin HE, Wang JW, Zhao QL, Zhou HY, Li Y, Zhang JQ. Multi-epitope DNA vaccine linked to the A(2)/B subunit of cholera toxin protect mice against toxoplasma gondii. Vaccine 2008, 26(31):3913-3921. 10.1016/j.vaccine.2008.04.046, 18555564.
45. Maksimov P, Zerweck J, Maksimov A, Hotop A, Gross U, Pleyer U, Spekker K, Daeubener W, Werdermann S, Niederstrasser O, et al. Peptide microarray analysis of in silico-predicted epitopes for serological diagnosis of toxoplasma gondii infection in humans. Clin Vaccine Immunol 2012, 19(6):865-874. 10.1128/CVI.00119-12, 3370440, 22496494.
46. Nielsen HV, Lauemoller SL, Christiansen L, Buus S, Fomsgaard A, Petersen E. Complete protection against lethal toxoplasma gondii infection in mice immunized with a plasmid encoding the SAG1 gene. Infect Immun 1999, 67(12):6358-6363. 97042, 10569750.
47. Wang Y, Wang M, Wang G, Pang A, Fu B, Yin H, Zhang D. Increased survival time in mice vaccinated with a branched lysine multiple antigenic peptide containing B- and T-cell epitopes from T. gondii antigens. Vaccine 2011, 29(47):8619-8623. 10.1016/j.vaccine.2011.09.016, 21939715.