zbMATH — the first resource for mathematics

Using the concept of Chou’s pseudo amino acid composition for risk type prediction of human papillomaviruses. (English) Zbl 1406.92455
Summary: High-risk types of human papillomaviruses (HPVs) are the etiological agents in nearly all cases (99.7%) of cervical cancer, and the HPV E6 protein is one of the two viral oncoproteins which is expressed in virtually all HPV-positive cancers. Therefore, classifying the risk type of HPVs is very useful and necessary for diagnosis and remedy of cervical cancer. To predict and to classify the risk types of HPV by bioinformatics analysis, 96 E6 protein sequences from available databases were obtained. To investigate the risk type of these sequences, PseAAC server, ROC curves and statistical analysis were applied. Our classification was based on some characters of HPV E6 proteins, such as hydrophobicity, hydrophilicity, side chain mass, PK of the \(\alpha\)-COOH group, PK of the \(\alpha\)-NH\(3^+\) group and PI at \(25^\circ\)C. Risk type of 4 unknown HPV types and 25 non-reported HPV types were also predicted. These results show that bioinformatics based theoretical approaches can direct and simplify experimental studies.

92D20 Protein sequences, DNA sequences
62P10 Applications of statistics to biology and medical sciences; meta analysis
Full Text: DOI
[1] Beaudenon, S.; Huibregtse, J.M., HPV E6, E6AP and cervical cancer, BMC biochem., 9, 1-7, (2008)
[2] Cai, Y.D.; Zhou, G.P.; Chou, K.C., Support vector machines for predicting membrane protein types by using functional domain composition, Biophys. J., 84, 3257-3263, (2003)
[3] Chen, C.; Chen, L.X.; Zou, X.Y.; Cai, P.X., Predicting protein structural class based on multi-features fusion, J. theor. biol., 253, 388-392, (2008) · Zbl 1398.92196
[4] Chou, K.C., Prediction of protein cellular attributes using pseudo amino acid composition, Proteins struct. funct. genet., 43, 246-255, (2001)
[5] Chou, K.C., Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes, Bioinformatics, 21, 10-15, (2005)
[6] Chou, K.C.; Shen, H.B., Hum-ploc: a novel ensemble classifier for predicting human protein subcellular localization, Biochem. biophys. res. commun., 347, 150-157, (2006)
[7] Chou, K.C.; Shen, H.B., Predicting protein subcellular location by fusing multiple classifiers, J. cell. biochem., 99, 517-527, (2006)
[8] Chou, K.C.; Shen, H.B., Euk-mploc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites, J. proteome res., 6, 1728-1734, (2007)
[9] Chou, K.C.; Shen, H.B., Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides, Biochem. biophys. res. comm., 357, 633-640, (2007)
[10] Chou, K.C.; Shen, H.B., Review: recent progresses in protein subcellular location prediction, Anal. biochem., 370, 1-16, (2007)
[11] Chou, K.C.; Shen, H.B., Memtype-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through pse-PSSM, Biochem. biophys. res. commun., 360, 339-345, (2007)
[12] Chou, K.C.; Shen, H.B., Cell-ploc: a package of web-servers for predicting subcellular localization of proteins in various organisms, Nat. protoc., 3, 153-162, (2008)
[13] Chou, K.C.; Shen, H.B., Protident: a web server for identifying proteases and their types by fusing functional domain and sequential evolution information, Biochem. biophys. res. comm., 376, 321-325, (2008)
[14] Chou, K.C.; Zhang, C.T., Review: prediction of protein structural classes, Crit. rev. biochem. mol. biol., 30, 275-349, (1995)
[15] Cogliano, V.; Baan, R.; Straif, K.; Grosse, Y.; Secretan, B.; El Ghissassi, F., Carcinogenecity of human papillomaviruses, Lancet oncol., 6, 204, (2005)
[16] DeLong; DeLong; Pearson, C., Comparing the areas under two or more correlated receiver operating curves: a nonparametric approach, Biometrics, 44, 837-845, (1988) · Zbl 0715.62207
[17] Ding, Y.S.; Zhang, T.L.; Chou, K.C., Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network, Protein pept. lett., 14, 811-815, (2007)
[18] Ding, Y.S.; Zhang, T.L.; Gu, Q.; Zhao, P.Y.; Chou, K.C., Using maximum entropy model to predict protein secondary structure with single sequence, Protein pept. lett., 16, 552-560, (2009)
[19] Du, P.; Li, Y., Prediction of C-to-U RNA editing sites in plant mitochondria using both biochemical and evolutionary information, J. theor. biol., 253, 579-589, (2008)
[20] Esmaeili, M.; Mohabatkar, H., Computational prediction of nuclear localization signals and structural characteristics of 91 types of HPV E6 proteins, Asian pac. J. cancer prev., 9, 631-636, (2008)
[21] Gillison, M.L.; Koch, W.M.; Capone, R.B.; Spafford, M.; Westra, W.H.; Wu, L.; Zahurak, M.L.; Daniel, R.W.; Viglione, M.; Symer, D.E.; Shah, K.V.; Sidransky, D., Evidence for a causal association between human papillomavirus and a subset of head and neck cancers, J. natl. cancer inst., 92, 709-720, (2000)
[22] Gussione, E.; Massimi, P.; Bernat, A.; Bank, L., Comparative analysis of the intracellular location of the high and lowof risk human papillomavirus oncoproteins, Virology, 293, 20-25, (2002)
[23] Hanley, J.A.; McNeil, B.J., The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology, 143, 29-36, (1982)
[24] Hiller, T.; Poppelreuther, S.; Stubenrauch, F.; Iftner, T., Comparative analysis of 19 genital human papillomavirus types with regard to P53 degradation, immortalization, phylogeny and epidemiologic risk classification, Cancer epidemiol. biomarkers prev., 15, 1262-1267, (2006)
[25] Hopp, T.P.; Woods, K.R., Prediction of protein antigenic determinants from amino acid sequences, Proc. natl. acad. sci. USA, 78, 3824-3828, (1981)
[26] Incassati, A.; Patel, D.; McCance, D.J., Induction of tetraploidy through loss of P53 and upregulation of plk1 by human papillomavirus type-16 E6, Oncogene, 25, 2444-2451, (2006)
[27] Iovino, F.; Lentini, L.; Amato, A.; Di Leonardo, A., RB acute loss induces centrosome amplification and aneuploidy in murine primary fibroblasts, Mol. cancer, 5, 38-49, (2006)
[28] Janiceck, M.F.; Arerette, H.E., Cervical cancer: prevention, diagnosis, and therapeutics, CA cancer J. clin., 51, 92-114, (2001)
[29] Joung, J.G.; O, S.G.; Zheng, B.T., Protein sequence-based rike classification for human papillomaviruses, Comput. biol. med., 36, 656-667, (2006)
[30] Kim, S.; Kim, J.; Zhang, B.T., Ensembled support vector machines for human papillomavirus risk type prediction from protein secondary structures, Comput. biol. med., 39, 187-193, (2009)
[31] Lin, H.; Ding, H.; Feng-Biao Guo, F.B.; Zhang, A.Y.; Huang, J., Predicting subcellular localization of mycobacterial proteins by using Chou’s pseudo amino acid composition, Protein pept. lett., 15, 739-744, (2008)
[32] Metz, C.E., Basic principles of ROC analysis, Semin. nucl. med., 8, 283-298, (1978)
[33] Motoyama, S.; Ladines-Llave, C.A.; Villanueva, S.L.; Maruo, T., The role of human papillomavirus in the molecular biology of cervical carcinogenesis, Kobe J. med. sci., 50, 9-19, (2004)
[34] Munger, K.; Baldwin, A.; Edwards, K.M.; Hayakawa, H.; Nguyen, C.L.; Owens, M.; Grace, M.; Huh, K., Mechanisms of human papillomavirus-induced oncogenesis, J. virol., 78, 11451-11460, (2004)
[35] Munger, K.; Howley, P.M., Human papillomavirus immortalization and transformation functions, Virus res., 89, 213-228, (2002)
[36] Muñoz, N.; Bosch, F.X.; Sanjosé, S.; Herrero, D.; Castellsagué, X.; . Shah, K.V.; Snijders, P.J.F.; Meijer, C.J.L.M., Epidemiologic classification of human papillomavirus type associated with cervical cancer, N. engl. J. med., 348, 518-527, (2003)
[37] Narisawa-Saito, M.; Kiyono, T., Basic mechanisms of high-risk human papillomavirus-induced carcinogenesis: role of E6 and E7 proteins, Cancer sci., 98, 1505-1511, (2007)
[38] Park, S.B., Hwang, S., Zhang, B.T., 2003. Classification of the risk type of human papillomavirus by decision trees. In: Lecture Notes in Computer Sciences, vol. 2690, pp. 540-544.
[39] Rezaei, M.A.; Abdolmaleki, P.; Karami, Z.; Asadabadi, E.B.; Sherafat, M.A.; Abrishami-Moghaddam, H.; Fadaie, M.; Forouzanfar, M., Prediction of membrane protein types by means of wavelet analysis and cascaded neural networks, J. theor. biol., 254, 817-820, (2008)
[40] Roberts, E.; Eargle, J.; Wright, D.; Luthey-Schulten, Z., Multiseq: unifying sequence and structure data for evolutionary analysis, BMC bioinformatics, 7, 382-393, (2006)
[41] Roux, G.L.; Moroianu, J., Nuclear entry of high-risk human papillomavirus type 16 E6 oncoprotein occurs via several pathways, J. virol., 77, 2330-2337, (2002)
[42] Shen, H.B.; Chou, K.C., Ensemble classifier for protein fold pattern recognition, Bioinformatics, 22, 1717-1722, (2006)
[43] Shen, H.B.; Chou, K.C., Signal-3L: a 3-layer approach for predicting signal peptides, Biochem. biophys. res. commun., 363, 297-303, (2007)
[44] Shen, H.B.; Chou, K.C., Nuc-ploc: a new web-server for predicting protein subnuclear localization by fusing pseaa composition and psepssm, Protein eng. des. sel., 20, 561-567, (2007)
[45] Shen, H.B.; Chou, K.C., Pseaac: a flexible web server for generating various kinds of protein pseudo amino acid composition, Anal. biochem., 373, 386-388, (2007)
[46] Shen, H.B.; Chou, K.C., Identification of proteases and their types, Anal. biochem., 385, 153-160, (2009)
[47] Shen, H.B.; Chou, K.C., Quatident: a web server for identifying protein quaternary structural attribute by fusing functional domain and sequential evolution information, J. proteome res., 8, 1577-1584, (2009)
[48] Shen, H.B.; Yang, J.; Chou, K.C., Euk-ploc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction, Amino acids, 33, 57-67, (2007)
[49] Shen, H.B., Song, J.N., Chou, K.C., 2009. Prediction of protein folding rates from primary sequence by fusing multiple sequential features. Journal of Biomedical Science and Engineering (JBiSE) 2, 136-143 (open accessible at 〈http://www.srpublishing.org/journal/jbise/〉).
[50] Tanford, C., Contribution of hydrophobic interactions to the stability of the globular conformation of proteins, J. am. chem. soc., 84, 4240-4274, (1962)
[51] Tian, F.; Lv, F.; Zhou, P.; Yang, Q.; Jalbout, A.F., Toward prediction of binding affinities between the MHC protein and its peptide ligands using quantitative structure-activity relationship approach, Protein pept. lett., 15, 1033-1043, (2008)
[52] Villiers, E.M.; Faquet, C.; Broker, T.R.; Bernard, H.V.; Zur Hausen, H., Classification of papillomaviruses, Virology, 324, 17-27, (2004)
[53] Walboomers, J.M.; Jacobs, M.V.; Manos, M.M.; Bosch, F.X.; Kummer, J.A.; Shah, K.V.; Snijders, P.J.; Peto, J.; Meijer, C.J.; Muñoz, N., Human papillomavirus is a necessary cause of invasive cervical cancer worldwide, J. pathol., 189, 12-19, (1999)
[54] Wang, S.Q.; Yang, J.; Chou, K.C., Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition, J. theor. biol., 242, 941-946, (2006)
[55] Xiao, X.; Shao, S.H.; Ding, Y.S.; Huang, Z.D.; Huang, Y.; Chou, K.C., Using complexity measure factor to predict protein subcellular location, Amino acids, 28, 29-35, (2005)
[56] Xiao, X.; Shao, S.H.; Huang, Z.D.; Chou, K.C., Using pseudo amino acid composition to predict protein structural classes: approached with complexity measure factor, J. comput. chem., 27, 478-482, (2006)
[57] Xiao, X.; Shao, S.H.; Huang, Z.D., Using cellular automata images and pseudo amino acid composition to predict protein sub-cellular location, Amino acids, 30, 49-54, (2006)
[58] Xiao, X.; Lin, W.Z.; Chou, K.C., Using grey dynamic modeling and pseudo amino acid composition to predict protein structural classes, J. comput. chem., 29, 2018-2024, (2008)
[59] Xiao, X.; Wang, P.; Chou, K.C., Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image, J. theor. biol., 254, 691-696, (2008) · Zbl 1400.92416
[60] Xiao, X.; Wang, P.; Chou, K.C., GPCR-CA: a cellular automaton image approach for predicting G-protein-coupled receptor functional classes, J. comput. chem., 30, 1414-1423, (2009)
[61] Xiao, X.; Wang, P.; Chou, K.C., Prediction of quaternary structural type of proteins with functional domain composition, J. appl. crystallogr., 42, 169-173, (2009)
[62] Yang, J.Y.; Peng, Z.L.; Yu, Z.G.; Zhang, R.J.; Anh, V.; Wang, D., Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation, J. theor. biol., 257, 618-626, (2009) · Zbl 1400.92417
[63] Yugawa, T.; Kiyono, T., Molecular mechanisms of cervical carcinogenesis by high-risk human papillomaviruses: novel function of E6 and E7 oncoproteins, Rev. med. virol., 19, 97-113, (2009)
[64] Zhang, T.L.; Ding, Y.S.; Chou, K.C., Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern, J. theor. biol., 250, 186-193, (2008) · Zbl 1397.92551
[65] Zheng, Z.M.; Baker, C.C., Papillomavirus genome structure, expression, and post transcriptional regulation, Front. biosci., 11, 2286-2302, (2006)
[66] Zhou, G.P., An intriguing controversy over protein structural class prediction, J. protein chem., 17, 729-738, (1998)
[67] Zur Hausen, H., Human papillomaviruses in the pathogenesis of anogenital cancer, Virology, 184, 9-13, (1991)
[68] Zur Hausen, H., Papillomaviruses causing cancer: evasion from host-cell control inearly events carcinogenesis, J. natl. cancer inst., 92, 690-698, (2000)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.