Predicting MHC-II binding affinity using multiple instance regression

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 8, Issue 4, 2011, Pages 1067-1079

  El Manzalawy, Yasser ^a   Dobbs, Drena ^b   Honavar, Vasant ^c  

^a AL AZHAR UNIVERSITY   (Egypt)

^b IOWA STATE UNIVERSITY   (United States)

^c Iowa State University   (United States)

Author keywords

MHC II peptide prediction; multiple instance learning; multiple instance regression.

Indexed keywords

AMINO ACID SEQUENCE; ANTIGEN PEPTIDE; BENCHMARK DATA; BINDING AFFINITIES; BINDING PEPTIDE; CORE REGION; IMMUNE RESPONSE; MAJOR HISTOCOMPATIBILITY COMPLEX CLASS; MHC-II BINDING AFFINITY; MULTIPLE INSTANCE LEARNING; MULTIPLE INSTANCE REGRESSION.; MULTIPLE INSTANCES; NOVEL METHODS; PEPTIDE PREDICTION; REGRESSION PROBLEM; STATE-OF-THE-ART METHODS; WEB SERVERS;

AMINO ACIDS; BINDING ENERGY; PEPTIDES; REGRESSION ANALYSIS; USER INTERFACES;

FORECASTING;

DNA BINDING PROTEIN; PEPTIDE; TRANSCRIPTION FACTOR;

AMINO ACID SEQUENCE; ANIMAL; AREA UNDER THE CURVE; ARTICLE; BIOLOGY; CHEMISTRY; GENE; HUMAN; METABOLISM; METHODOLOGY; MOLECULAR GENETICS; MOUSE; PROTEIN BINDING; REGRESSION ANALYSIS; REPRODUCIBILITY; STATISTICAL MODEL;

AMINO ACID SEQUENCE; ANIMALS; AREA UNDER CURVE; COMPUTATIONAL BIOLOGY; DNA-BINDING PROTEINS; GENES, MHC CLASS II; HUMANS; MICE; MODELS, STATISTICAL; MOLECULAR SEQUENCE DATA; PEPTIDES; PROTEIN BINDING; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; TRANSCRIPTION FACTORS;

EID: 79957588907     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2010.94     Document Type: Article

References (64)

1. C. Janeway et al., Immunobiology: The Immune System in Health and Disease, sixth ed. Garland Pub, 2004.
2. B. Korber, M. LaBute, and K. Yusim, "Immunoinformatics Comes of Age," PLoS Computational Biology, vol. 2, no. 6, pp. 484-492, 2006.
3. U. Gowthaman and J. Agrewala, "In Silico Tools for Predicting Peptides Binding to HLA-Class II Molecules: More Confusion than Conclusion," J. Proteome Research, vol. 7, no. 1, pp. 154-63, 2008. (Pubitemid 351171127)
4. H. Rammensee, T. Friede, and S. Stevanović, "MHC Ligands and Peptide Motifs: First Listing," Immunogenetics, vol. 41, no. 4, pp. 178-228, 1995.
5. D. Madden, "The Three-Dimensional Structure of Peptide-MHC Complexes," Ann. Rev. Immunology, vol. 13, pp. 587-622, 1995.
6. I. Doytchinova and D. 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, pp. 2263-2270, 2003. (Pubitemid 37492833)
7. C. Hattotuwagama, P. Guan, I. Doytchinova, C. Zygouri, and D. Flower, "Quantitative Online Prediction of Peptide Binding to the Major Histocompatibility Complex," J. Molecular Graphics and Modelling, vol. 22, no. 3, pp. 195-207, 2004. (Pubitemid 37415184)
8. W. Liu, X. Meng, Q. Xu, D. Flower, and T. Li, "Quantitative Prediction of Mouse Class I MHC Peptide Binding Affinity Using Support Vector Machine Regression (SVR) Models," BMC Bioinformatics, vol. 7, no. 1, article no. 182, 2006.
9. H. Bui, J. Sidney, B. Peters, M. Sathiamurthy, A. Sinichi, K. Purton, B. Mothé, F. Chisari, D. Watkins, and A. Sette, "Automated Generation and Evaluation of Specific MHC Binding Predictive Tools: ARB Matrix Applications," Immunogenetics, vol. 57, no. 5, pp. 304-314, 2005. (Pubitemid 40905049)
10. M. Nielsen, C. Lundegaard, and O. Lund, "Prediction of MHC Class II Binding Affinity Using SMM-Align, a Novel Stabilization Matrix Alignment Method," BMC Bioinformatics, vol. 8, article no. 238, 2007.
11. P. Reche, J. Glutting, H. Zhang, and E. Reinherz, "Enhancement to the RANKPEP Resource for the Prediction of Peptide Binding to MHC Molecules Using Profiles," Immunogenetics, vol. 56, no. 6, pp. 405-419, 2004. (Pubitemid 39359226)
12. H. Singh and G. Raghava, "ProPred: Prediction of HLA-DR Binding Sites," Bioinformatics, vol. 17, no. 12, pp. 1236-1237, 2001. (Pubitemid 33735355)
13. M. Nielsen, C. Lundegaard, P. Worning, C. Sylvester-Hvid, K. Lamberth, S. Buus, S. Brunak, and O. Lund, "Improved Prediction of MHC Class I and II Epitopes Using a Novel Gibbs Sampling Approach," Bioinformatics, vol. 20, pp. 1388-1397, 2004. (Pubitemid 38937025)
14. M. Rajapakse, B. Schmidt, L. Feng, and V. Brusic, "Predicting Peptides Binding to MHC Class II Molecules Using Multi- Objective Evolutionary Algorithms," BMC Bioinformatics, vol. 8, no. 1, article no. 459, 2007.
15. H. Mamitsuka, "Predicting Peptides that Bind to MHC Molecules Using Supervised Learning of Hidden Markov Models," PROTEINS: Structure, Function, and Genetics, vol. 33, pp. 460-474, 1998. (Pubitemid 28565732)
16. H. Noguchi, R. Kato, T. Hanai, Y. Matsubara, H. Honda, V. Brusic, and T. Kobayashi, "Hidden Markov Model-Based Prediction of Antigenic Peptides that Interact with MHC Class II Molecules," J. Bioscience and Bioeng., vol. 94, no. 3, pp. 264-270, 2002. (Pubitemid 35192075)
17. M. Nielsen, C. Lundegaard, P. Worning, S. Lauemøller, K. Lamberth, S. Buus, S. Brunak, and O. Lund, "Reliable Prediction of T-Cell Epitopes Using Neural Networks with Novel Sequence Representations," Protein Science, vol. 12, pp. 1007-1017, 2003. (Pubitemid 36505435)
18. S. Buus, S. Lauemoller, P. Worning, C. Kesmir, T. Frimurer, S. Corbet, A. Fomsgaard, J. Hilden, A. Holm, and S. Brunak, "Sensitive Quantitative Predictions of Peptide-MHC Binding by a'Query by Committee' Artificial Neural Network Approach," Tissue Antigens, vol. 62, no. 5, pp. 378-384, 2003. (Pubitemid 37345160)
19. P. Donnes and O. Kohlbacher, "SVMHC: A Server for Prediction of MHC-Binding Peptides," Nucleic Acids Research, vol. 34, no. Web Server issue, pp. W194-W197, 2006. (Pubitemid 44529763)
20. M. Bhasin and G. Raghava, "SVM Based Method for Predicting HLA-DRB1 0401 Binding Peptides in an Antigen Sequence," Bioinformatics, vol. 20, pp. 421-423, 2004. (Pubitemid 38262776)
21. J. Cui, L. Han, H. Lin, H. Zhang, Z. Tang, C. Zheng, Z. Cao, and Y. Chen, "Prediction of MHC-Binding Peptides of Flexible Lengths from Sequence-Derived Structural and Physicochemical Properties," Molecular Immunology, vol. 44, pp. 866-877, 2006.
22. J. Salomon and D. Flower, "Predicting Class II MHC-Peptide Binding: A Kernel Based Approach Using Similarity Scores," BMC Bioinformatics, vol. 7, no. 1, article no. 501, 2006.
23. N. Murugan and Y. Dai, "Prediction of MHC Class II Binding Peptides Based on an Iterative Learning Model," Immunome Research, vol. 1, no. 1, p. 6, 2005.
24. T. Hertz and C. Yanover, "PepDist: A New Framework for Protein-Peptide Binding Prediction Based on Learning Peptide Distance Functions," BMC Bioinformatics, vol. 7, pp. S1-S3, 2006.
25. T. Bailey and C. Elkan, "Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization," Machine Learning, vol. 21, no. 1, pp. 51-80, 1995.
26. C. Lawrence, S. Altschul, M. Boguski, J. Liu, A. Neuwald, and J. Wootton, "Detecting Subtle Sequence Signals: A Gibbs Sampling Strategy for Multiple Alignment," Science, vol. 262, no. 5131, pp. 208-214, 1993.
27. C. Fonseca and P. Fleming, "Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization," Proc. Fifth Int'l Conf. Genetic Algorithms, vol. 423, pp. 416- 423, 1993.
28. N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller, "Equation of State Calculations by Fast Computing Machines," J. Chemical Physics, vol. 21, p. 1087, 2004.
29. K. Bennett and O. Mangasarian, "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets," Optimization Methods and Software, vol. 1, no. 1, pp. 23-34, 1992. (Pubitemid 23615796)
30. S. Chang, D. Ghosh, D. Kirschner, and J. Linderman, "Peptide Length-Based Prediction of Peptide-MHC Class II Binding," Bioinformatics, vol. 22, no. 22, pp. 2761-2767, 2006. (Pubitemid 44742396)
31. 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, no. 1, article no. 296, 2009.
32. Y. Chen, J. Bi, and J. Wang, "MILES: Multiple-Instance Learning via Embedded Instance Selection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 1931-1947, Dec. 2006. (Pubitemid 46405705)
33. R.H. Dietterich, T.G. Lathrop, and T. Lozano-Perez, "Solving the Multiple-Instance Problem with Axis Parallel Rectangles," Artificial Intelligence, vol. 89, nos. 1/2, pp. 31-71, 1997. (Pubitemid 127412230)
34. J. Ramon and L. De Raedt, "Multi Instance Neural Networks," Proc. Int'l Conf. Machine Learning (ICML '00) Workshop Attribute- Value and Relational Learning, 2000.
35. J. Wang and J.D. Zucker, "Solving the Multiple-Instance Problem: A Lazy Learning Approach," Proc. 17th Int'l Conf. Machine Learning, pp. 1119-1125, 2000.
36. O. Maron and T. Lozano-Perez, "A Framework for Multiple- Instance Learning," Advances in Neural Information Processing Systems, vol. 10, pp. 570-576, 1998.
37. Q. Zhang and S.A. Goldman, "EM-DD: An Improved Multiple- Instance Learning Technique," Advances in Neural Information Processing Systems, vol. 14, pp. 1073-1080, 2001.
38. S. Andrews, I. Tsochantaridis, and T. Hofmann, "Support Vector Machines for Multiple-Instance Learning," Advances in Neural Information Processing Systems, vol. 15, pp. 561-568, 2002.
39. T. Gartner, P. Flach, A. Kowalczyk, and A. Smola, "Multi-Instance Kernels," Proc. 19th Int'l Conf. Machine Learning, pp. 179-186, 2002.
40. S. Ray and M. Craven, "Supervised versus Multiple Instance Learning: An Empirical Comparison," Proc. 22nd Int'l Conf. Machine Learning, pp. 697-704, 2005. (Pubitemid 43183395)
41. S. Scott, J. Zhang, and J. Brown, "On Generalized Multiple- Instance Learning," Int'l J. Computational Intelligence and Applications, vol. 5, no. 1, pp. 21-35, 2005.
42. Q. Tao, S. Scott, N. Vinodchandran, T. Osugi, and B. Mueller, "Kernels for Generalized Multiple-Instance Learning," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 12, pp. 2084-2098, Dec. 2008.
43. O. Maron and A. Ratan, "Multiple-Instance Learning for Natural Scene Classification," Proc. 15th Int'l Conf. Machine Learning, pp. 341-349, 1998.
44. Q. Zhang, S. Goldman, W. Yu, and J. Fritts, "Content-Based Image Retrieval Using Multiple-Instance Learning," Proc. 19th Int'l Conf. Machine Learning, pp. 682-689, 2002.
45. Z. Zhou, K. Jiang, and M. Li, "Multi-Instance Learning Based Web Mining," Applied Intelligence, vol. 22, no. 2, pp. 135-147, 2005. (Pubitemid 40400730)
46. G. Fung, M. Dundar, B. Krishnapuram, and R. Rao, "Multiple Instance Learning for Computer Aided Diagnosis," Proc. Conf. Advances in Neural Information Processing Systems, pp. 425-432, 2007.
47. S. Ray and D. Page, "Multiple Instance Regression," Proc. 18th Int'l Conf. Machine Learning, pp. 425-432, 2001.
48. S. Goldman and S. Scott, "Multiple-Instance Learning of Real- Valued Geometric Patterns," Annals of Math. and Artificial Intelligence, vol. 39, no. 3, pp. 259-290, 2003.
49. P. Wang, J. Sidney, C. Dow, B. Mothé, 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, no. 4, 2008.
50. H. Lin, G. Zhang, S. Tongchusak, E. Reinherz, and V. Brusic, "Evaluation of MHC-II Peptide Binding Prediction Servers: Applications for Vaccine Research," BMC Bioinformatics, vol. 9, article no. S22, 2008.
51. 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, no. 7, 2008.
52. H. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. Bhat, H. Weissig, I. Shindyalov, and P. Bourne, "The Protein Data Bank," Nucleic Acids Research, vol. 28, no. 1, pp. 235-242, 2000. (Pubitemid 30047768)
53. J. Zhu, S. Kosset, T. Hastie, and R. Tibshirani, "1-Norm Support Vector Machines," Advances in Neural Information Processing Systems, vol. 16, pp. 49-56, 2004.
54. S. Shevade, S. Keerthi, C. Bhattacharyya, and K. Murthy, "Improvements to the SMO Algorithm for SVM Regression," IEEE Trans. Neural Networks, vol. 11, no. 5, pp. 1188-1193, Sept. 2000.
55. S. Henikoff and J. Henikoff, "Amino Acid Substitution Matrices from Protein Blocks," Proc. Nat'l Academy of Sciences USA, vol. 89, no. 22, pp. 10915-10919, 1992.
56. J. Swets, "Measuring the Accuracy of Diagnostic Systems," Science, vol. 240, no. 4857, pp. 1285-1293, 1988.
57. T. Sturniolo et al., "Generation of Tissue-Specific and Promiscuous HLA Ligand Databases Using DNA Microarrays and Virtual HLA Class II matrices," Nature Biotechnology, vol. 17, pp. 555-561, 1999. (Pubitemid 29262918)
58. B. Peters et al., "The Immune Epitope Database and Analysis Resource: From Vision to Blueprint," PLoS Biology, vol. 3, no. 3, pp. 379-381, 2005.
59. T. Urdan, Statistics In Plain English. Lawrence Erlbaum Assoc., 2005.
60. J. Demšar, "Statistical Comparisons of Classifiers over Multiple Data Sets," J. Machine Learning Research, vol. 7, pp. 1-30, 2006. (Pubitemid 43022939)
61. V. Brusic, G. Rudy, G. Honeyman, J. Hammer, and L. Harrison, "Prediction of MHC Class II-Binding Peptides Using an Evolutionary Algorithm and Artificial Neural Network," Bioinformatics, vol. 14, no. 2, pp. 121-130, 1998. (Pubitemid 28395870)
62. O. Mangasarian and E. Wild, "Multiple Instance Classification via Successive Linear Programming," Technical Report 05-02, Data Mining Inst., 2005.
63. Z. Zhou and M. Zhang, "Solving Multi-Instance Problems with Classifier Ensemble Based on Constructive Clustering," Knowledge and Information Systems, vol. 11, no. 2, pp. 155-170, 2007.
64. N. Weidmann, E. Frank, and B. Pfahringer, "A Two-Level Learning Method for Generalized Multi-Instance Problems," Proc. European Conf. Machine Learning, pp. 468-479, 2003. (Pubitemid 37231000)