Recent advances in immunoinformatics: Application of in silico tools to drug development

Current Opinion in Drug Discovery and Development

Volumn 11, Issue 2, 2008, Pages 233-241

  Evans, Mark C ^a  

^a XOMA Corporation   (United States)

Author keywords

B cell; Epitope prediction; Immunogenicity; Immunoinformatics; Major histocompatibility complex; T cell

Indexed keywords

EPITOPE; RECOMBINANT PROTEIN; VACCINE;

ALLERGY; ANTIGEN RECOGNITION; B LYMPHOCYTE; BIOINFORMATICS; DATA BASE; HUMAN; IMMUNITY; IMMUNOGENICITY; IMMUNOINFORMATICS; IMMUNOTHERAPY; INFECTION; INFLAMMATION; NONHUMAN; PHAGE DISPLAY; REVIEW; T LYMPHOCYTE;

ALLERGY AND IMMUNOLOGY; ANIMALS; B-LYMPHOCYTES; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DRUG DESIGN; EPITOPES; HUMANS; HYPERSENSITIVITY; IMMUNOTHERAPY; T-LYMPHOCYTES;

EID: 39549103295     PISSN: 13676733     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Review

References (45)

1. Foundation N (Ed): Immunoinformatics: Bioinformatic Strategies for Better Understanding of Immune Function, No 254. Wiley VCH, Weinheim, Germany (2003).
2. Janeway C, Travers P, Walport M, Shlomchik MJ (Eds): Immunobiology 6th Ed. Garland Science Publishing, New York, NY, USA (2005).
3. Tong JC, Tan TW, Ranganathan S: Methods and protocols for prediction of immunogenic epitopes. Brief Bioinform (2007) 8(2):96-108. •• Examines current computational methods used in the prediction and study of epitope/MHC interactions and provides guidelines for epitope prediction based upon the availability of experimental data.
4. Nielsen M, Lundegaard C, Blicher T, Lamberth K, Harndahl M, Justesen S, Røder G, Peters B, Sette A, Lund O, Buus S: NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoS ONE (2007) 2(8):e796.
5. Trost B, Bickis M, Kusalik A: Strength in numbers: Achieving greater accuracy in MHC-I binding prediction by combining the results from multiple prediction tools. Immunome Res (2007) 3:5. • Describes a benchmark study of 16 T-cell epitope-prediction algorithms illustrating an improved likelihood of success when multiple tools agree on an epitope.
6. Koren E, De Groot AS, Jawa V, Beck KD, Boone T, Rivera D, Li L, Mytych D, Koscec M, Weeraratne D, Swanson S et al: Clinical validation of the 'in silico' prediction of immunogenicity of a human recombinant therapeutic protein. Clin Immunol (2007) 124(1):26-32.
7. Pelat T, Hust M, Laffly E, Condemine F, Bottex C, Vidal D, Lefranc MP, Dübel S, Thullier P: High-affinity, human antibody-like antibody fragment (single-chain variable fragment) neutralizing the lethal factor (LF) of Bacillus anthracis by inhibiting protective antigen-LF complex formation. Antimicrob Agents Chemother (2007) 51(8):2758-2764.
8. Chaitra MG, Shaila MS, Nayak R: Evaluation of T-cell responses to peptides with MHC class I-binding motifs derived from PE_ PGRS 33 protein of Mycobacterium tuberculosis. J Med Microbiol (2007) 56(Pt 4):466-474.
9. McMurry JA, Kimball S, Lee JH, Rivera D, Martin W, Weiner DB, Kutzler M, Sherman DR, Kornfeld H, De Groot AS: Epitope-driven TB vaccine development: A streamlined approach using immunoinformatics, ELISpot assays, and HLA transgenic mice. Curr Mol Med (2007) 7(4):351-368. • Describes a practical path for the development of epitope-based vaccines using immunoinformatics from epitope identification to live challenge.
10. Shaila MS, Nayak R, Prakash SS, Georgousakis M, Brandt E, McMillan DJ, Batzloff MR, Pruksakorn S, Good MF, Sriprakash KS: Comparative in silico analysis of two vaccine candidates for group A streptococcus predicts that they both may have similar safety profiles. Vaccine (2007) 25(18):3567-3573.
11. McMurry JA, Gregory SH, Moise L, Rivera D, Buus S, De Groot AS: Diversity of Francisella tularensis Schu4 antigens recognized by T lymphocytes after natural infections in humans: Identification of candidate epitopes for inclusion in a rationally designed tularemia vaccine. Vaccine (2007) 25(16):3179-3191.
12. Doytchinova IA, Flower DR: Identifying candidate subunit vaccines using an alignment-independent method based on principal amino acid properties. Vaccine (2007) 25(5):856-866. • Describes an alignment-independent method of antigen recognition based upon amino acid chemical properties with a prediction accuracy of 83%.
13. Doytchinova IA, Flower DR: VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics (2007) 8:4.
14. Bui HH, Sidney J, Li W, Fusseder N, Sette A: Development of an epitope conservancy analysis tool to facilitate the design of epitope-based diagnostics and vaccines. BMC Bioinformatics (2007) 8(1):361.
15. Allergen Nomenclature: International Union of Immunological Societies, Allergen Nomenclature Sub-Committee (2007). http://www.allergen. org
16. Codex Alimentarius Commission, Rome, Italy (2007). http://www. codexalimentarius.net
17. European Food Safety Authority: Guidance document of the Scientific Panel on Genetically Modified Organisms for the risk assessment of genetically modified plants and derived food and feed. EFSA J (2006) 99:1-100.
18. Martinez Barrio A, Soeria-Atmadja D, Nistér A, Gustafsson MG, Hammerling U, Bongcam-Rudloff E: EVALLER: A web server for in silico assessment of potential protein allergenicity. Nucleic Acids Res (2007) 35(Web Server issue):W694-W700.
19. Soeria-Atmadja D, Lundell T, Gustafsson MG, Hammerling U: Computational detection of allergenic proteins attains a new level of accuracy with in silico variable-length peptide extraction and machine learning. Nucleic Acids Res (2006) 34(13):3779-3793.
20. Ivanciuc O, Schein CH, Braun W: SDAP: Database and computational tools for allergenic proteins. Nucleic Acids Res (2003) 31(1):359-362.
21. Ivanciuc O, Schein CH, Braun W: Data mining of sequences and 3D structures of allergenic proteins. Bioinformatics (2002) 18(10):1358-1364.
22. Schein CH, Ivanciuc O, Braun W: Bioinformatics approaches to classifying allergens and predicting cross-reactivity. Immunol Allergy Clin North Am (2007) 27(1):1-27. •• Reviews existing immunoinformatic tools that are applicable to allergy with detailed explanations of the methods involved together with examples of their uses and applications.
23. Cui J, Han LY, Li H, Ung CY, Tang ZQ, Zheng CJ, Cao ZW, Chen YZ: Computer prediction of allergen proteins from sequence-derived protein structural and physicochemical properties. Mol Immunol (2007) 44(4):514-520.
24. Tanabe S: Epitope peptides and immunotherapy. Curr Protein Pept Sci (2007) 8(1):109-118. •• Excellent review of the use of T-cell epitope peptides in current immunotherapy for allergy.
25. Müller U, Akdis CA, Fricker M, Akdis M, Blesken T, Bettens F, Blaser K: Successful immunotherapy with T-cell epitope peptides of bee venom phospholipase A2 induces specific T-cell anergy in patients allergic to bee venom. J Allergy Clin Immunol (1998) 101(6 Pt 1):747-754.
26. Fellrath JM, Kettner A, Dufour N, Frigerio C, Schneeberger D, Leimgruber A, Corradin G, Spertini F: Allergen-specific T-cell tolerance induction with allergen-derived long synthetic peptides: Results of a phase I trial. J Allergy Clin Immunol (2003) 111(4):854-861.
27. Oldfield WL, Larché M, Kay AB: Effect of T-cell peptides derived from Fel d 1 on allergic reactions and cytokine production in patients sensitive to cats: A randomised controlled trial. Lancet (2002) 360(9326):47-53.
28. Günther S, Hempel D, Dunkel M, Rother K, Preissner R: SuperHapten: A comprehensive database for small immunogenic compounds. Nucleic Acids Res (2007) 35(Database issue):D906-D910.
29. Nicola G, Vakser IA: A simple shape characteristic of protein-protein recognition. Bioinformatics (2007) 23(7):789-792.
30. Shi Y, Zhang X, Wan J, Wang Y, Yin W, Cao Z, Guo Y: Predicting the distance between antibody's interface residue and antigen to recognize antigen types by support vector machine. Neural Comput Applic (2007) 16(4-5):481-490.
31. Saha S, Raghava GP: Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins (2006) 65(1):40-48.
32. Saha S, Bhasin M, Raghava GP: Bcipep: A database of B-cell epitopes. BMC Genomics (2005) 6(1):79.
33. Bairoch A, Apweiler R: The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res (2000) 28(1):45-48.
34. Nekrasov AN: Analysis of information structure of protein sequences: A new method for analyzing the domain organization of proteins. J Biomol Struct Dyn (2004) 21(5):615-623.
35. Nekrasov AN: Entropy of protein sequences: An integral approach. J Biomol Struct Dyn (2002) 20(1):87-92.
36. Alekseeva L, Nekrasov A, Marchenko A, Shevchenko M, Benevolenskii S, Sapozhnikov A, Kurup VP, Svirshchevskaya E: Cryptic B-cell epitope identification through informational analysis of protein sequences. Vaccine (2007) 25(14):2688-2697.
37. Blythe MJ, Flower DR: Benchmarking B-cell epitope prediction: Underperformance of existing methods. Protein Sci (2005) 14(1):246-248.
38. Ghate AD, Bhagwat BU, Bhosle SG, Gadepalli SM, Kulkarni-Kale UD: Characterization of antibody-binding sites on proteins: Development of a knowledgebase and its applications in improving epitope prediction. Protein Pept Lett (2007) 14(6):531-535.
39. Moreau V, Granier C, Villard S, Laune D, Molina F: Discontinuous epitope prediction based on mimotope analysis. Bioinformatics (2006) 22(9):1088-1095.
40. Huang J, Gutteridge A, Honda W, Kanehisa M: MIMOX: A web tool for phage display based epitope mapping. BMC Bioinformatics (2006) 7:451.
41. Mayrose I, Shlomi T, Rubinstein ND, Gershoni JM, Ruppin E, Sharan R, Pupko T: Epitope mapping using combinatorial phage-display libraries: A graph-based algorithm. Nucleic Acids Res (2007) 35(1):69-78. •• Pepsurf predicts conformational B-cell epitopes by aligning affinity-isolated peptide sequences to computationally derived antigen surface sequences. The method successfully predicted epitopes of four antibody/antigen co-crystal structures in a statistically significant manner.
42. Bublil EM, Freund NT, Mayrose I, Penn O, Roitburd-Berman A, Rubinstein ND, Pupko T, Gershoni JM: Stepwise prediction of conformational discontinuous B-cell epitopes using the Mapitope algorithm. Proteins (2007) 68(1):294-304. •• Mapitope predicts conformational B-cell epitopes using amino acid pairs derived from sequences of affinity-isolated peptides. The method was successfully validated with four antibody/antigen co-crystal structures.
43. Castrignanò T, De Meo PD, Carrabino D, Orsini M, Floris M, Tramontano A: The MEPS server for identifying protein conformational epitopes. BMC Bioinformatics (2007) 8(Suppl 1):S6.
44. Bratland E, Wolff AS, Haavik J, Kämpe O, Sköldberg F, Perheentupa J, Bredholt G, Knappskog PM, Husebye ES: Epitope mapping of human aromatic l-amino acid decarboxylase. Biochem Biophys Res Commun (2007) 353(3):692-698. • Provides an example of applied immunoinformatics using open-access tools to predict a conformational B-cell epitope.
45. DeepView - Swiss-PdbViewer: GlaxoSmithKline R&D & the Swiss Institute of Bioinformatics, Geneva, Switzerland (2006). http://www.expasy.org/spdbv