Protein structural classification based on pseudo amino acid composition using SVM classifier

• Autorzy: Krajewski Z. , Tkacz E.
• Języki publikacji: EN

Abstrakty

EN

This paper deals with a structural classification by the aid of support vector machine (SVM) classifier. Amino acid composition (AAC) and pseudo amino acid composition (PseAA) features were applied with different variants. Additionally the feature reflecting the length of protein chain was taken into consideration. The SVM classifier was compared to minimallength classifiers with respect to the AAC features. The best model of SVM classifier was chosen using grid method on the basis of cross-validation (CV) as criterion. The best model of SVM classifier is evaluated with respect to proper evaluation rates. The SCOP database and the ASTRAL tool were a source of non-homologous data to avoid the redundancy and to ensure a maximal amount of available data.

Słowa kluczowe

EN

pseudo amino acid composition; support vector machine (SVM); minimal distance methods; protein structural class; SCOP database

PL

skład pseudo aminokwasu; metoda odległości minimalnej; baza danych SCOP; sieć wektorów podtrzymujących

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2013
• Tom: Vol. 33, no. 2
• Strony: 77--87
• Opis fizyczny: Bibliogr. 38 poz., rys., tab., wykr.

Twórcy

autor

Krajewski Z.

• Silesian University of Technology, Gliwice, Poland

autor

Tkacz E.

• Silesian University of Technology, Gliwice, Poland

Bibliografia

• [1] Levitt M, Chothia C. Structural patterns in globular proteins. Nature 1976; 261 (June (5561)): 552–8.
• [2] Hubbard T, Ailey B, Brenner S, Murzin A, Chothia C. SCOP, Structural Classification of Proteins Database: applications to evaluation of the effectiveness of sequence alignment methods and statistics of protein structural data. Acta Cryst 1998; D54: 1147–54.
• [3] Hubbard T, Ailey B, Brenner S, Murzin A, Chothia C. SCOP, Structural Classification of Proteins Database. Nucleic Acids Res 1999; 27(1).
• [4] Chou K. A novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space. Proteins 1995 Apr; 21(4): 319–44.
• [5] Zhou G. An intriguing controversy over protein structural class prediction. J Protein Chem 1998; 17(8).
• [6] Chou K, Zhang C. Prediction of protein structural classes. Crit Rev Biochem Mol Biol 1995; 30(4): 275–349.
• [7] Chou K. Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins Struct Func Bioinform 2001; 43(3): 246–55.
• [8] Chou K, Cai Y. Predicting protein quaternary structure by pseudo amino acid composition. Proteins Struct Func Gen 2003; 53: 282–9.
• [9] Zerrin I. Computational approaches to protein structure prediction. The Graduate School of Engineering and Natural Sciences in partial fulfillment of the requirements for the degree of Master of Science; Thesis (Master), Sabancı University, İstanbul, 2003.
• [10] Lin H, Wang H, Ding H, Chen Y, Li Q. Prediction of subcellular localization of apoptosis protein using Chou's pseudo amino acid composition. Acta Biotheor 2013; 57(3): 321–30.
• [11] Zhang G, Li H, Gao J, Fang B. Predicting lipase types by improved Chou's pseudo-amino acid composition. Protein and Peptide Lett 2008;15(10): 1132–7.
• [12] Eisenhaber F, Persson B, Argos P. Protein structure prediction: recognition of primary, secondary, and tertiary structural features from amino acid sequence. Crit Rev Biochem Mol Biol 1995; 30(1): 1–94.
• [13] Chou K. Pseudo amino acid composition and its applications in bioinformatics. Proteomics and System Biology Current Proteomics 2009; 6: 262–74.
• [14] Chou K, Cai Y. Prediction of protease types in a hybridization space. Biochem Biophys Res Commun 2006; 339: 1015–20.
• [15] Chou K, Cai Y. Predicting subcellular localization of proteins by hybridizing functional domain composition and pseudoamino acid composition. J Cell Biochem 2004; 91(6): 1197–203.
• [16] Chou K. Progress in protein structural class prediction and its impact to bioinformatics and proteomics. Curr Protein Pept Sci 2005; 6 (October (5)): 423–36.
• [17] Cai Y, Zhou G, Chou K. Support vector machines for predicting membrane protein types by using functional domain composition. Biophys J 2003; 84 (May (5)): 3257–63.
• [18] Tanford C. Contribution of hydrophobic interactions to the stability of the globular conformation of proteins. J Am Chem Soc 1962; 84(22): 4240–7.
• [19] Hopp TP, Woods KR. Prediction of protein antigenic determinants from amino acid sequences (hydrophilicity analysis/protein conformation). Proc Natl Acad Sci USA 1981; 78 (June (6)): 3824–8.
• [20] Shen H, Chou K. Ensemble classifier for protein fold pattern recognition. Bioinformatics 2006; 22(14): 1717–22.
• [21] Chou K. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 2005; 21(1): 10–9.
• [22] Esmaeili M, Mohabatkar H, Mohsenzadeh S. Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses. J Theor Biol 2010; 263 (March (2)): 203–9.
• [23] Osuna E, Freund R, Girosi F. Support vector machines training and applications, A.I. Memo No. 1602, C.B.C.L Paper No. 144; 1997.
• [24] Vapnik VN. The nature of statistical learning theory. 2nd ed. Springer; 1995, ISBN: 0-387-98780-0.
• [25] Fradkin D., Muchnik I. Support vector machines for classification. IMACS series in discrete mathematics and theoretical computer science; 2005.
• [26] Shigeo A. Support vector machines for pattern classification. Springer; 2005, ISBN: 1-85233-929-2.
• [27] Burges C. A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 1998; 2: 121–67.
• [28] Hsu C., Chang C., Lin C. A practical guide to support vector classification. Department of Computer Science; 2010. http://www.csie.ntu.edu.tw/_cjlin [accessed 02.08.12].
• [29] Chou K, Maggiora G. Domain structural class prediction. Protein Eng 1998; 11(7): 523–38.
• [30] Gu F, Chen H, Ni J. Protein structural class prediction based on an improved statistical strategy. BMC Bioinformatics 2008;9 (Suppl. 6): S5.
• [31] Murzin A, Brenner S, Hubbard T, Chothia C. SCOP: a Structural Classification of Proteins Database for the investigation of sequences and structures. J Mol Biol 1995; 247: 536–40.
• [32] Chandonia J, Hon G, Walker N, Lo Conte L, Koehl P, Levitt M, et al. The ASTRAL Compendium in 2004. Nucleic Acids Res 2004; 32 (Database issue): D189–92.
• [33] Shen H, Kuo-Chen Chou K. PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. Anal Biochem 2008; 373:386–8.
• [34] Jankowski N. Ontogenic neural networks and their applications to classification of medical data (in Polish – Ontogeniczne sieci neuronowe w zastosowaniu do klasyfikacji danych medycznych). Ph.D. Thesis, Department of Computer Methods, Nicholas Copernicus University, Toruń, Poland; 1999.
• [35] Stateczny A, Praczyk T. Artificial Neural Networks In Maritime Objects Recognition (in Polish – Sztuczne Sieci Neuronowe w Rozpoznawaniu Obiektów Morskich). Gdańsk Scientific Society; 2002.
• [36] Janeczek B. Data credibility analysis based on support vector machines (in Polish). M.A. Dissertation, Warsaw University of Technology, The Faculty of Electronics and Information Technology, The Institute of Computer Science; 2007/2008.
• [37] Aczel A. Business statistics. Warszawa: PWN; 2000 (in Polish).
• [38] Wang L. Support vector machines: theory and applications. Springer; 2005.