Improving in Silico Prediction of Epitope Vaccine Candidates by Union and Intersection of Single Predictors


The in silico prediction of peptide binding affinities to MHC proteins is a very important first step in the process of epi-tope-based vaccine design and development. Five MHC class II binding prediction servers were combined in different ways and the resulting performance of these combinations was evaluated using a test set, which consisted of 4540 known HLA-DRB1 binders. The five servers were: NetMHCIIpan, NetMHCII, ProPred, RANKPEP, and EpiTOP. The top 5% of the ranked predictions from each server were combined using union and intersection operators. The outputs were evaluated in terms of sensitivity and positive predictive value (PPV). The union operator showed high sensitivity (65-79%) and low PPVs (6-8%), while intersection outputs had low sensitivities (4-41%) yet significantly higher PPVs (14-31%). Thus there is a defining trade-off between sensitivity and PPV for each combination. The union of outputs from different servers brings more “noise” than “signal” to the resulting set of predicted binders. Conversely, selecting only commonly predicted binders increases the probability that an identified binder is a true binder.

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I. Dimitrov, D. Flower and I. Doytchinova, "Improving in Silico Prediction of Epitope Vaccine Candidates by Union and Intersection of Single Predictors," World Journal of Vaccines, Vol. 1 No. 2, 2011, pp. 15-22. doi: 10.4236/wjv.2011.12004.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] O. R?tzschke and K. Falk, “Origin, Structure and Motifs of Naturally Processed MHC Class II Ligands,” Current Opinion in Immunology, Vol. 6, No. 1, February 1994, pp. 45-51. doi:10.1016/0952-7915(94)90032-9
[2] J. Robinson, M. J. Waller, P. Parham, N. De Groot, R. Bontrop, L. J. Kennedy, P. Stoehr and S. G. E. Marsh, “IMGT/HLA and IMGT/MHC: Sequence Databases for the Study of the Major Histocompatibility Complex,” Nucleic Acids Research, Vol. 31, No. 1, 2003, pp. 311- 314. doi:10.1093/nar/gkg070
[3] A. Dessen, C. M. Lawrence, S. Cupo, D. M. Zaller and D. C. Wiley, “X-Ray Crystal Structure of HLA-DR4 (DRA* 0101, DRB1*0401) Complexed with a Peptide from Human Collagen II,” Immunity, Vol. 7, No. 4, October 1997, pp. 473-481. doi:10.1016/S1074-7613(00)80369-6
[4] Z. Zavala-Ruiz, E. J. Sundberg, J. D. Stone, D. B. DeOliveira, I. C. Chan, J. Svendsen, R. A. Mariuzza and L. J. Stern, “Exploration of the P6/P7 Region of the Peptide- binding Site of the Human Class II Major Histocompatability Complex Protein HLA-DR1,” Journal of Biological Chemistry, Vol. 278, 2003, pp. 44904-44912. doi:10.1074/jbc.M307652200
[5] Z. Zavala-Ruiz, I. Strug, B. D. Walker, P. J. Norris and L. J. Stern, “A Hairpin Turn in a Class II MHC-Bound Peptide Orients Residues outside the Binding Groove for T Cell Recognition,” Proceeding of National Acadademy of Sciences USA, Vol. 101, No. 36, 2004, pp. 13279-13284. doi:10.1073/pnas.0403371101
[6] M. M. Fernandez, R. Guan, C. P. Swaminathan, E. L. Malchiodi and R. A. Mariuzza, “Crystal Structure of Staphylococcal Enterotoxin I (SEI) in Complex with a Human Major Histocompatibility Complex Class II Molecule,” Journal of Biological Chemistry, Vol. 281, 2006, pp. 25356-25364. doi:10.1074/jbc.M603969200
[7] J. Hennecke and D. C. Wiley, “Structure of a Complex of the Human a/b T Cell Receptor (TCR) HA1.7, Influenza Hemagglutinin Peptide, and Major Histocompatibility Complex Class II Molecule, HLA-DR4 (DRA*0101 and DRB1*0401): Insight into TCR Cross-Restriction and Alloreactivity,” Journal of Experimental Medicine, Vol. 195, 2002, pp. 571-581. doi:10.1084/jem.20011194
[8] L. Deng, R. J. Langley, P. H. Brown, G. Xu, L. Teng, Q. Wang, M. I. Gonzales, G. G. Callender, M. I. Nishimura, S. L. Topalian and R. A. Mariuzza, “Structural Basis for the Recognition of Mutant Self by a Tumor-Specific, MHC Class II-Restricted T Cell Receptor,” Nature Immunology, Vol. 8, 2007, pp. 398-408. doi:10.1038/ni1447
[9] L. Wang, Y. Zhao, Z. Li, Y. Guo, L. L. Jones, D. M. Kranz, W. Mourad and H. Li, “Crystal Structure of a Complete Ternary Complex of TCR, Superantigen and Peptide-MHC,” Nature Structural & Molecular Biology, Vol. 14, 2007, pp. 169-171. doi:10.1038/nsmb1193
[10] C. A. Janeway, P. Travers, M. Walport and J. D. Capra, “Immunobiology: The Immune System in Health and Disease,” Elsevier Science Ltd., 1999.
[11] I. A. Doytchinova and D. R. Flower, “Towards the in Silico Identification of Class II Restricted T-Cell Epitopes: a Partial Least Squares Iterative Self-Consistent Algorithm for Affinity Prediction,” Bioinformatics, Vol. 19, No. 17, 2003, pp. 2263-2270. doi:10.1093/bioinformatics/btg312
[12] J. C. Tong, G. L. Zhang, T. W. Tan, J. T. August, V. Brusic and S. Ranganathan, “Prediction of HLA-DQ3.2β Ligands: Evidence of Multiple Registers in Class II Binding Peptides,” Bioinformatics, Vol. 22, No. 10, 2006, pp. 1232-1238. doi:10.1093/bioinformatics/btl071
[13] H. H. Lin, G. L. Zhang, S. Tongchusak, E. L. Reinherz and V. Brusic, “Evaluation of MHC-II Peptide Binding Prediction Servers: Applications for Vaccine Research,” BMC Bioinformatics, Vol. 9, 2008, Suppl. 12, S22.
[14] R. R. Mallios, “A Consensus Strategy for Combining HLA-DR Binding Algorithms,” Human Immunology, Vol. 64, No. 9, September 2003, pp. 852-856. doi:10.1016/S0198-8859(03)00142-3
[15] H. Rammensee, J. Bachmann, N. P. Emmerich, O. A. Bachor and S. Stevanovic, “SYFPEITHI: Database for MHC Ligands and Peptide Motifs,” Immunogenetics, Vol. 50, No. 3-4, 1999, pp. 213-219. doi:10.1007/s002510050595
[16] H. Singh and G. P. Raghava, “ProPred: Prediction of HLA-DR Binding Sites,” Bioinformatics, Vol. 17, No. 12, 2001, pp. 1236-1237. doi:10.1093/bioinformatics/17.12.1236
[17] R. R. Mallios, “Predicting Class II MHC/Peptide Multi- Level Binding with an Iterative Stepwise Discriminant Analysis Meta-Algorithm,” Bioinformatics, Vol. 17, No. 10, 2001, pp. 942-948. doi:10.1093/bioinformatics/17.10.942
[18] G. L. Zhang, A. M. Khan, K. N. Srinivasan, J. T. August and V. Brusic, “Neural Models for Predicting Viral Vaccine Targets,” Journal of Bioiformatics and Computational Biology, Vol. 3, No. 5, 2005, pp. 1207-1225. doi:10.1142/S0219720005001466
[19] O. Karpenko, L. Huang and Y. Dai, “A Probabilistic Meta-Predictor for the MHC Class II Binding Peptides,” Immunogenetics, Vol. 60, No. 1, 2008, pp. 25-36. doi:10.1007/s00251-007-0266-y
[20] P. Wang, J. Sidney, C. Dow, B. Mothe, A. Sette and B. Peters, “A Systematic Assessment of MHC Class II Peptide Binding Predictions and Evaluation of a Consensus Approach,” PLoS Computational Biology, Vol. 4, 2008, p. e1000048. doi:10.1371/journal.pcbi.1000048
[21] P. A. Reche, J. P. Glutting and E. L. Reinherz, “Enhancement to the RANKPEP Resource for the Prediction of Peptide Binding to MHC Molecules Using Profiles,” Immunogenetics, Vol. 56, No. 6, 2004, pp. 405-419. doi:10.1007/s00251-004-0709-7
[22] M. Nielsen, C. Lundegaard, T. Blicher, B. Peters, A. Sette, S. Justesen, S. Buus and O. Lund, “Quantitative Predictions of Peptide Binding to Any HLA-DR Molecule of Known Sequence: NetMHCIIpan,” PLoS Computational Biology, Vol. 4, 2008, p. e1000107.
[23] M. Nielsen and O. Lund, “NN-Align: An Artificial Neural Network-Based Alignment Algorithm for MHC Class II Peptide Binding Prediction,” BMC Bioinformatics, Vol. 10, 2009, p. 296. doi:10.1186/1471-2105-10-296
[24] I. Dimitrov, P. Garnev, D. R. Flower and I. Doytchinova, “Peptide Binding to the HLA-DRB1 Supertype: A Proteochemometrics Analysis,” European Journal of Medicinal Chemistry, Vol. 45, No. 1, January 2010, pp. 236- 243. doi:10.1016/j.ejmech.2009.09.049
[25] I. Dimitrov, P. Garnev, D. R. Flower and I. Doytchinova, “EpiTOP—A Proteochemometric Tool for MHC Class II Binding Prediction,” Bioinformatics, Vol. 26, No. 16, 2010, pp. 2066-2068. doi:10.1093/bioinformatics/btq324
[26] I. Dimitrov, P. Garnev, D. R. Flower and I. Doytchinova, “MHC Class II Binding Prediction: A Little Help from a Friend,” Journal of Biomedicine and Biotechnology, Vol. 2010, Special Issue Vaccine Informatics, 2010, Article ID705821.
[27] B. Peters and A. Sette, “Integrating Epitope Data into the Emerging Web of Biomedical Knowledge Resources,” Nature Reviews Immunology, Vol. 7, June 2007, pp. 485- 490. doi:10.1038/nri2092
[28] T. Sturniolo, E. Bono, J. Ding, L. Raddrizzani, O. Tuereci, U. Sahin, M. Braxenthaler, F. Gallazzi, M. P. Protti, F. Sinigaglia and J. Hammer, “Generation of Tissue-Specific and Promiscuous HLA Ligand Databases Using DNA Microarrays and Virtual HLA Class II Matrices,” Nature Biotechnology, Vol. 17, June 1999, pp. 555-561. doi:10.1038/9858
[29] D. R. Flower, “Vaccines: Data Driven Prediction of Binders, Epitopes and Immunogenicity. Bioinformatics for Vaccinology,” Wiley-Blackwell, 2008.
[30] K. C. Parker, M. A. Bednarek and J. E. Coligan, “Scheme for Ranking Potential HLA-A2 Binding Peptides Based on Independent Binding of Individual Peptide Side- Chains,” Journal of Immunology, Vol. 152, No. 1, 1994, pp. 163-175.
[31] M. Pawlowski, M. J. Gajda, R. Matlak and J. M. Bujnicki, “MetaMQAP: A Meta-Server for the Quality Assessment of Protein Models,” BMC Bioinformatics, Vol. 9, 2008, p. 403. doi:10.1186/1471-2105-9-403
[32] B. Wallner, P. Larsson and A. Elofsson, “Pcons.Net: Protein Structure Prediction Meta Server,” Nucleic Acids Research, Vol. 35 (Web Server Issue), 2007, pp. W369-374.
[33] I. Friedberg, T. Harder and A. Godzik, “JAFA: A Protein Function Annotation Meta-Server,” Nucleic Acids Research, Vol. 34 (Web Server Issue), 2006, pp. W379-381.
[34] O. Karpenko, L. Huang and Y. Dai, “A Probabilistic Meta-Predictor for the MHC Class II Binding Peptides,” Immunogenetics, Vol. 60, No. 1, 2008, pp. 25-36. doi:10.1007/s00251-007-0266-y
[35] B. Trost, M. Bickis and A. Kusalik, “Strength in Numbers: Achieving Greater Accuracy in MHC-I Binding Prediction by Combining the Results from Multiple Prediction Tools,” Immunome Research, Vol. 3, 2007, p. 5. doi:10.1186/1745-7580-3-5

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