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標題: 以蛋白質序列為基礎之結構型B細胞抗原決定位預測
SEP: Sequence-based Strategy for structural B-cell epitope prediction
作者: 梁恆豪
Liang, Heng-Hao
關鍵字: 結構型抗原決定位
structural epitope
support vector machines
association rule
position-specific scoring matrix
出版社: 基因體暨生物資訊學研究所
引用: 1. Davies DR, Cohen GH: Interactions of protein antigens with antibodies. Proceedings of the National Academy of Sciences 1996, 93(1):7-12. 2. Van Regenmortel MH: What is a B-cell epitope? In: Epitope Mapping Protocols. Springer; 2009: 3-20. 3. Barlow D, Edwards M, Thornton J: Continuous and discontinuous protein antigenic determinants. Nature 1986, 322(6081):747-748. 4. Benjamin DC: B-cell epitopes: fact and fiction. In: Inhibitors to Coagulation Factors. Springer; 1996: 95-108. 5. Vinion-Dubiel AD, McClain MS, Cao P, Mernaugh RL, Cover TL: Antigenic diversity among Helicobacter pylori vacuolating toxins. Infection and immunity 2001, 69(7):4329-4336. 6. Kohler G, Milstein C: Continuous cultures of fused cells secreting antibody of predefined specificity. Nature 1975, 256(5517):495-497. 7. Firer MA, Gellerman G: Targeted drug delivery for cancer therapy: the other side of antibodies. Journal of hematology & oncology 2012, 5:70. 8. Hopp TP, Woods KR: Prediction of protein antigenic determinants from amino acid sequences. Proceedings of the National Academy of Sciences 1981, 78(6):3824-3828. 9. Chou P, Fasman G: Prediction of beta-turns. Biophysical journal 1979, 26(3):367-383. 10. Emini EA, Hughes JV, Perlow D, Boger J: Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. Journal of virology 1985, 55(3):836-839. 11. Karplus P, Schulz G: Prediction of chain flexibility in proteins. Naturwissenschaften 1985, 72(4):212-213. 12. Parker J, Guo D, Hodges R: 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. Biochemistry 1986, 25(19):5425-5432. 13. Kolaskar A, Tongaonkar PC: A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS letters 1990, 276(1):172-174. 14. Saha S, Raghava G: Prediction of continuous B‐cell epitopes in an antigen using recurrent neural network. PROTEINS: Structure, Function, and Bioinformatics 2006, 65(1):40-48. 15. Larsen JE, Lund O, Nielsen M: Improved method for predicting linear B-cell epitopes. Immunome research 2006, 2(1):2. 16. EL‐Manzalawy Y, Dobbs D, Honavar V: Predicting linear B‐cell epitopes using string kernels. Journal of Molecular Recognition 2008, 21(4):243-255. 17. Kulkarni-Kale U, Bhosle S, Kolaskar AS: CEP: a conformational epitope prediction server. Nucleic acids research 2005, 33(Web Server issue):W168-171. 18. Haste Andersen P, Nielsen M, Lund O: Prediction of residues in discontinuous B-cell epitopes using protein 3D structures. Protein science : a publication of the Protein Society 2006, 15(11):2558-2567. 19. Sun J, Wu D, Xu T, Wang X, Xu X, Tao L, Li YX, Cao ZW: SEPPA: a computational server for spatial epitope prediction of protein antigens. Nucleic acids research 2009, 37(Web Server issue):W612-616. 20. Sweredoski MJ, Baldi P: COBEpro: a novel system for predicting continuous B-cell epitopes. Protein engineering, design & selection : PEDS 2009, 22(3):113-120. 21. Gao J, Faraggi E, Zhou Y, Ruan J, Kurgan L: BEST: Improved Prediction of B-Cell Epitopes from Antigen Sequences. PloS one 2012, 7(6):e40104. 22. Ansari HR, Raghava GP: Identification of conformational B-cell Epitopes in an antigen from its primary sequence. Immunome research 2010, 6(1):6. 23. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat T, Weissig H, Shindyalov IN, Bourne PE: The protein data bank. Nucleic acids research 2000, 28(1):235-242. 24. Vita R, Zarebski L, Greenbaum JA, Emami H, Hoof I, Salimi N, Damle R, Sette A, Peters B: The immune epitope database 2.0. Nucleic acids research 2010, 38(suppl 1):D854-D862. 25. Premendu PM: Structural Epitope Database (SEDB): A Web-based Database for the Epitope, and its Intermolecular Interaction Along with the Tertiary Structure Information. Journal of Proteomics & Bioinformatics 2012, 5:85-89. 26. Petersen B, Petersen T, Andersen P, Nielsen M, Lundegaard C: A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC Structural Biology 2009, 9(1):51. 27. Henikoff S, Henikoff JG: Amino acid substitution matrices from protein blocks. Proceedings of the National Academy of Sciences 1992, 89(22):10915-10919. 28. Pietrokovski S, Henikoff JG, Henikoff S: The Blocks database—a system for protein classification. Nucleic acids research 1996, 24(1):197-200. 29. Frank E, Hall M, Trigg L, Holmes G, Witten IH: Data mining in bioinformatics using Weka. Bioinformatics 2004, 20(15):2479-2481. 30. Wang H-W, Lin Y-C, Pai T-W, Chang H-T: Prediction of B-cell linear epitopes with a combination of support vector machine classification and amino acid propensity identification. BioMed Research International 2011, 2011. 31. Chang C-C, Lin C-J: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2011, 2(3):27. 32. Nelson DL, Cox MM: Lehninger principles of biochemistry: Wh Freeman; 2010. 33. Koolman J, Rohm K-H: Color atlas of biochemistry: George Thieme Verlag; 2005. 34. Sayle RA, Milner-White EJ: RASMOL: biomolecular graphics for all. Trends in biochemical sciences 1995, 20(9):374-376. 35. Ostermeier C, Harrenga A, Ermler U, Michel H: Structure at 2.7 A resolution of the Paracoccus denitrificans two-subunit cytochrome c oxidase complexed with an antibody FV fragment. Proceedings of the National Academy of Sciences 1997, 94(20):10547-10553.
摘要: 免疫反應為人體消滅侵入體內之病原體之重要的防禦機制,而抗原決定位又為抗原抗體結合時,抗原蛋白質上與抗體結合最重要的位置。病原體之抗原決定位多為結構型抗原決定位,但現有之以序列做結構型抗原決定位預測網站,其效能還有很大的進步空間。因此,本研究利用SVM作為學習的工具,從蛋白質的序列進行預測。利用5種特徵編碼: 二進位、蛋白質組成、序列保留性、二級結構與可接觸表面面積及關聯性規則作為建構第一層之模型依據,選出適合個編碼之片段長度。接著將第一層模型輸出之信心分數做為第二層模型之輸入值,利用第二層模型將第一層模型做整合,來提高預測之準確率。最後完成之預測模型對於結構型抗原決定位之預測準確度可達到63%,效能比現有之預測網站較佳,最後再以Paracoccus denitrificans之two-subunit cytochrome c oxidase做測試,準確可達66%。
Immune reaction is the most important defense mechanism for destroying invading-pathogens in our body, and the epitope is the position of antigen-antibody interaction on pathogen proteins. Most of epitopes belong to structural epitope, but the existing sequence-based predicting website still has a lot of ways to improve the predicting performance. Therefore, in this study used SVM as machine learning tool to predict epitope by protein sequences. First, we built five SVM models in the first layer according to five features include binary, composition, position-specific scoring matrix, secondary structure, accessible surface area and association rule, then choosing the patterns which have the best performance in each model. Second, using the confidence score of first layer models as the input value for the SVM model in the second layer that SVM model integrated first layer SVM models for improving the predicting accuracy. The final prediction model can achieve up to 63% of accuracy in epitope predicting result, the predicting performance is batter then existing predicting website. Finally, a case study used two-subunit cytochrome c oxidase of Paracoccus denitrificans as testing, the accuracy can achieve up to 66%.
其他識別: U0005-2808201312574800
Appears in Collections:基因體暨生物資訊學研究所



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