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Prediction of Peptide Reactivity with Human IVIg through a Knowledge-Based Approach

By Nicola Barbarini, Alessandra Tiengo and Riccardo Bellazzi


The prediction of antibody-protein (antigen) interactions is very difficult due to the huge variability that characterizes the structure of the antibodies. The region of the antigen bound to the antibodies is called epitope. Experimental data indicate that many antibodies react with a panel of distinct epitopes (positive reaction). The Challenge 1 of DREAM5 aims at understanding whether there exists rules for predicting the reactivity of a peptide/epitope, i.e., its capability to bind to human antibodies. DREAM 5 provided a training set of peptides with experimentally identified high and low reactivities to human antibodies. On the basis of this training set, the participants to the challenge were asked to develop a predictive model of reactivity. A test set was then provided to evaluate the performance of the model implemented so far

Topics: Research Article
Publisher: Public Library of Science
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Provided by: PubMed Central

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  1. (2004). A dominant linear B-cell epitope of ricin A-chain is the target of a neutralizing antibody response in Hodgkin’s lymphoma patients treated with an anti-CD25 immunotoxin.
  2. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins.
  3. (1974). A new look at the statistical model identification.
  4. (2008). A new structure-activity relationship of linear cationic a-helical antimicrobial peptides.
  5. (2009). A Perl procedure for protein identification by Peptide Mass Fingerprinting.
  6. (1990). A semi-empirical method for prediction of antigenic determinants on protein antigens.
  7. (1978). Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins.
  8. (2007). Antibody-protein interactions: benchmark datasets and prediction tools evaluation.
  9. (2005). Benchmarking B cell epitope prediction: underperformance of existing methods.
  10. (1993). C4.5: programs for machine learning.
  11. (2005). CEP: a conformational epitope prediction server.
  12. (2010). Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. PLoS One 5(4): e9862. Prediction of Peptide Reactivity
  13. (1974). Conformational parameters for amino acids in helical, beta-sheet, and random coil regions calculated from proteins.
  14. (1978). Conformational preferences of amino acids in globular proteins.
  15. (2005). ConSurf 2005: the projection of evolutionary conservation scores of residues on protein structures.
  16. (1997). Correlation between side chain mobility and conformation in protein structures.
  17. (1993). Correlation between the location of antigenic sites and the prediction of turns in proteins.
  18. (2010). EpiTOP–a proteochemometric tool for MHC class II binding prediction.
  19. (1995). Estimating continuous distributions in bayesian classifiers.
  20. (1998). Generating accurate rule sets without global optimization.
  21. (1993). HLA-A1 and HLA-A3 T cell epitopes derived from influenza virus proteins predicted from peptide binding motifs.
  22. (1981). Identification of common molecular subsequences.
  23. (2004). Identification of two antigenic epitopes on SARS-CoV spike protein.
  24. (2006). Immunoinformatics comes of age.
  25. (2006). Improved method for predicting linear Bcell epitopes.
  26. (1985). Induction of hepatitis A virusneutralizing antibody by a virus-specific synthetic peptide.
  27. (1992). Learning with continuous classes.
  28. (2006). Machine learning approaches for prediction of linear B-cell epitopes on proteins.
  29. (2007). Modeling the adaptive immune system: predictions and simulations.
  30. (1986). New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites.
  31. (2007). PIER: protein interface recognition for structural proteomics.
  32. (1988). Positional flexibilities of amino. acid residues in globular proteins.
  33. (2006). Prediction of continuous B-cell epitopes in an antigen using recurrent neural network.
  34. (1981). Prediction of protein antigenic determinants from amino acid sequences.
  35. (2006). Prediction of residues in discontinuous B-cell epitopes using protein 3D structures.
  36. (2008). Predictive data mining in clinical medicine: current issues and guidelines.
  37. (2009). Probing the epitope signatures of IgG antibodies in human serum from patients with autoimmune disease.
  38. (2008). Progress and challenges in protein structure prediction.
  39. (2004). ProMate: a structure based prediction program to identify the location of protein-protein binding sites.
  40. (1996). Regression shrinkage and selection via the lasso.
  41. (2006). Selection and combination of machine learning classifiers for prediction of linear B-cell epitopes on proteins.
  42. (1990). Sequence Logos: A new way to display consensus sequences.
  43. (1986). Static accessibility model of protein antigenicity: the case of scorpion neurotoxin.
  44. (2010). Structural properties of MHC class II ligands, implications for the prediction of MHC class II epitopes.
  45. (1979). Surface and inside volumes in globular proteins.
  46. (2003). SWISS-MODEL: an automated protein homology-modeling server.
  47. (2010). The immune epitope database 2.0.
  48. (2007). Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools.
  49. (2010). Why similar protein sequences encode similar three-dimensional structures?.
  50. (2003). ZDOCK: an initial-stage protein-docking algorithm.