Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/19343
標題: 使用正規化鍵長向量作雙硫鍵鍵結型態之預測
Prediction of Disulfide Connectivity Pattern Using the Normalized Bond Vector
作者: 蔡効昆
Tsai, Shiau-Kuen
關鍵字: disulfide bond
雙硫鍵
protein
connectivity pattern
蛋白質
鍵結型態
出版社: 資訊科學系所
引用: [1] Cynthia Gibas and Per Jambeck, Developing Bioinformatics Computer Skills [2] Stryer's, Biochemistry Fourth Edition Edited by Lubert Stryer [3] Abkevich,V.I. and Shakhnovich,E.I. (2000) What can disulfide bonds tell us about protein energetics, function and folding: Simulations and bioinformatics analysis. J. Mol. Biol., 300, 975-985. [4] Anfinsen C, Scheraga HA. Experimental and theoretical aspects of protein folding. Adv Protein Chem 1975;29:205-300. [5] Bairoch,A. and Apweiler,R. (2000) The Swiss-Prot protein sequence database and Its supplement TrEMBL in 2000. Nucleic Acids Res., 28, 45-48. [6] Baldi,P., Cheng,J. and Vullo,A. (2005) Large-scale prediction of disulphide bond connectivity. In Saul,L.K., Weiss,Y. and Bottou,L. (eds), Advances in Neural Information Processing Systems 17. MIT Press, Cambridge, MA, pp. 97-104. [7] Chen Bo-Juen, Tsai Chi-Hung, Chan Chen-hsiung, Kao Cheng-Yan. (2003) Disulfide connectivity prediction with 70% accuracy using two-level models. Proteins, 55, 1-5. [8] Chen Yu-Ching, Hwang Jenn-Kang (2005). Prediction of Disulfide Connectivity From Protein Sequences. PROTEINS: Structure, Function, and Bioinformatics 61, 507-512 [9] Chen Yu-Ching, Lin Yu-Ching, Lin Chih-Jen and Hwang Jenn-Kang. Prediction of the Bonding States of Cysteines Using the Support Vector Machines Based on Multiple Feature Vectors and Cysteine State Sequences. PROTEINS: Structure, Function, and Bioinformatics 2004;55:1036-1042. [10] Chuang Chao-Chun, Chen Chun-Yin, Yang Jinn-Moon, Lyu Ping-Chiang, Hwang Jenn-Kang (2003) Relationship Between Protein Structures and Disulfide-Bonding Patterns. PROTEINS: Structure, Function, and Bioinformatics 53:1-5 [11] Fariselli,P. and Casadio,R. (2001) Prediction of disulfide connectivity in proteins. Bioinformatics, 17, 957-964. [12] Fariselli,P. et al. (2002) A neural network based method for predicting the disulfide connectivity in proteins. In Damiani,E. (ed.), Knowledge Based Intelligent Information Engineering Systems and Allied Technologies (KES). Vol 1. IOS Press, 464-468. [13] Fariselli,P., Riccobelli,P. and Casadio,R. (1999) Role of evolutionary information in predicting the disulfide-bonding state of cysteine in proteins. Proteins, 36, 340-346. [14] Fiser,A., Cserzo,M., Tudos,E. and Simon,I. (1992) Different sequence environment of cysteines and half cystines in proteins. FEBS Lett., 302, 117-120. [15] Fiser,A. and Simon,I. (2000) Predicting the oxidation state of cysteines by multiple sequence alignment. Bioinformatics, 3, 251-256. [16] F. Ferrè and P. Clote (2005) Disulfide connectivity prediction using secondary structure information and diresidue frequencies. Bioinformatics, 21, 2336-2346 [17] Huang,E.S., Samudrala,R. and Ponder,J.W. (1999) Ab initio fold prediction of small helical proteins using distance geometry and knowledge-based scoring functions. J. Mol. Biol., 290, 267-281. [18] Martelli PL, Fariselli P, Malaguti L, Casadio R. Prediction of the disulfide bonding state of cysteines in proteins with hidden neural networks. Protein Eng 2002;15:951-953. [19] Muccielli-Giorgi MH, Hazout S, Tuffery P. Predicting the disulfide bonding state of cysteines using protein descriptors. Proteins 2002;46:243-249. [20] Muskal,S.M., Holbrook,R.S. and Kim,S.H. (1990) Prediction of the disulfide-bonding state of cysteine in proteins. Protein Eng., 3, 667-672. [21] Skolnick,J., Kolinski,A. and Ortiz,A.R. (1997) MONSSTER: A method for folding globular proteins with a small number of distance restraints. J. Mol. Biol., 265, 217-241. [22] Tsai Chi-Hung, Chen Bo-Juen, Chan Chen-hsiung, Liu Hsuan-Liang and Kao Cheng-Yan (2005) Improving disulfide connectivity prediction with sequential distance between oxidized cysteines. Bioinformatics, 21, 4416-4419 [23] Vullo,A. and Frasconi,P. (2004) Disulfide connectivity prediction using recursive neural networks and evolutionary information. Bioinformatics, 20, 653-659. [24] Wedemeyer,W.J.,Welker,E., Narayan,M. and Scheraga,H.A. (2000) Disulfide bonds and protein folding. Biochemistry, 39, 4207-4216. [25] Welker,E., Wedemeyer,W.J., Narayan,M. and Scheraga,H.A. (2001) Coupling of conformational folding and disulfide-bond reactions in oxidative folding of proteins. Biochemistry, 40, 9059-9064. [26] Zhao East, Liu Hsuan-Liang, Tsai Chi-Hung, Tsai Huai-Kuang, Chan Chen-hsiung and Kao Cheng-Yan. (2005) Cysteine separations profiles on protein sequences infer disulfide connectivity. Bioinformatics, 21, 1415-1420.
摘要: 雙硫鍵(disulfide bond)在蛋白質折疊上扮演一個重要的角色,所以能夠精準的預測雙硫鍵的鍵結也能夠增加預測蛋白質結構的精確度。目前大多數的文獻是基於已知雙硫鍵鍵結狀態(bonding state)下來預測雙硫鍵的鍵結型態(bonding pattern),在這篇論文中,我們提出一個有效的方法能在未知鍵結狀態但已知有二到五個雙硫鍵之情況下以蛋白質的胺基酸序列來預測雙硫鍵的鍵結型態,我們的方法使用模版比對的方式並且使用一個全域的特徵來進行預測,我們稱之為正規化鍵長向量(Normalized bond vector),而這個方法在已知雙硫鍵鍵結狀態下也夠提供一個更好的預測率。 在以往的文獻中,在未知鍵結狀態使用SwissProt39(SP39)資料集下之Qp(整個鍵結型態之正確率)以及Qc(單個鍵結之正確率)最好的預測率分別為28%以及41%,而在已知鍵結狀態下正確率則為70%。為了測試我們的方法,我們使用與其他文獻相同的資料集(SP39),於未知鍵結狀態下執行四輪交叉測試(4-fold cross-validation),我們的準確率可達52%以及57%,而於已知鍵結狀態下則可以達到66%以及68%。此外我們也建構出一個預測的模板,提供往後新進資料的預測。
The prediction of the location of disulfide bridges helps towards the solution of protein folding problem. Most of previous works on disulfide connectivity pattern prediction use the prior knowledge of the bonding state of cysteines. In this study an effective method is proposed to predict disulfide connectivity pattern without the prior knowledge of cysteins' bonding state. The method is based on template matching and only a global feature called the normalized bond vector is used. This method can also be applied and achieves a better accuracy rate if the bonding state of cysteines is known. In previous research works reported in the literature, to the best of our knowledge, without the prior knowledge of the bonding state of cysteines, the best accuracy rate of disulfide connectivity pattern prediction and that of disulfide bridge prediction are 28% and 41% respectively. With the prior knowledge of the bonding state of cysteines, the best accuracy rates increase to 70%. Testing our method on the same test dataset SwissProt39 (sp39), the accuracy rates of a fourfold cross-validation are 52% and 57% for disulfide connectivity pattern prediction and disulfide bridge prediction when the bonding state of cysteines is not known in advance. With the prior knowledge of bonding state, the accuracy rates increase to 66% and 68%. Furthermore we construct a template prediction model for predicting new arrival data.
URI: http://hdl.handle.net/11455/19343
其他識別: U0005-1707200616373800
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1707200616373800
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