×

zbMATH — the first resource for mathematics

Prediction of \(\beta\)-lactamase and its class by Chou’s pseudo-amino acid composition and support vector machine. (English) Zbl 1314.92055
Summary: \(\beta\)-Lactam class of antibiotics is used as major therapeutic agent against a number of pathogenic microbes. The widespread and indiscriminate use of antibiotics to treat bacterial infection has prompted evolution of several evading mechanisms from the lethal effect of antibiotics. \(\beta\)-Lactamases are endogenously produced enzyme that makes bacteria resistant against \(\beta\)-lactam antibiotics by cleaving the \(\beta\)-lactam ring. On the basis of primary structures, \(\beta\)-lactamase family of enzymes is divided into four classes namely A, B, C and D. Class B are metallo-enzymes while A, C and D does not need any metal in the enzyme catalysis. In the present study we developed a SVM based two level \(\beta\)-lactamases protein prediction method, which differentiate \(\beta\)-lactamases from non-\(\beta\)-lactamases at first level and then classify predicted \(\beta\)-lactamases into different classes at second level. We evaluated performance of different input vectors namely simple amino acid composition, Type-1 and Type-2 Chou’s pseudo amino acid compositions. Comparative performances indicated that SVM model trained on Type-1 pseudo amino acid composition has the best performance. At first level we were able to classify \(\beta\)-lactamases from non-\(\beta\)-lactamases with 90.63% accuracy. At second level we found maximum accuracy of 61.82%, 89.09%, 70.91% and 70.91% of class A, class B, class C and class D, respectively. A web-server as well as standalone, PredLactamase, is also developed to make the method available to the scientific community, which can be accessed at http://14.139.227.92/mkumar/predlactamase.

MSC:
92C40 Biochemistry, molecular biology
92D20 Protein sequences, DNA sequences
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] Ambler, R. P., The structure of beta-lactamases, Philos. Trans. R. Soc. London, Ser. B, 289, 321-331, (1980)
[2] Bailey, T. L., MEME SUITE: tools for motif discovery and searching, Nucleic Acids Res., 37, W202-W208, (2009)
[3] Bock, J. R.; Gough, D. A., Predicting protein-protein interactions from primary structure, Bioinformatics, 17, 455-460, (2001)
[4] Bonomo, J.; Gill, R. T., Antibiotic resistance as a model for strain engineering, Comput. Chem. Eng., 29, 509-517, (2005)
[5] Bradley, A. E., The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognit., 30, 1145-1159, (1997)
[6] Brown, M. P., Knowledge-based analysis of microarray gene expression data by using support vector machines, Proc. Natl. Acad. Sci. U.S.A., 97, 262-267, (2000)
[7] Cao, D. S., Propy: a tool to generate various modes of chou’s pseaac, Bioinformatics, 29, 960-962, (2013)
[8] Chen, L., Predicting anatomical therapeutic chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similarities, PLoS One, 7, e35254, (2012)
[9] Chen, W., Itis-psetnc: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition, Anal. Biochem., 462, 76-83, (2014)
[10] Chen, Y. K.; Li, K. B., Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of chou’s pseudo amino acid composition, J. Theor. Biol., 318, 1-12, (2013)
[11] Chou, K. C., Psedo amino acid composition and its application in bioinformatics, proteomics and system biology, Curr. Proteomics, 6, 262-274, (2009)
[12] Chou, K. C., Prediction of protein subcellular locations by incorporating quasi-sequence-order effect, Biochem. Biophys. Res. Commun., 278, 477-483, (2000)
[13] Chou, K. C., Prediction of protein cellular attributes using pseudo-amino acid composition, Proteins, 43, 246-255, (2001)
[14] Chou, K. C., Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes, Bioinformatics, 21, 10-19, (2005)
[15] Chou, K. C., Some remarks on protein attribute prediction and pseudo amino acid composition, J. Theor. Biol., 273, 236-247, (2011) · Zbl 1405.92212
[16] Chou, K. C., Some remarks on predicting multi-label attributes in molecular biosystems, Mol. Biosyst., 9, 1092-1100, (2013)
[17] Chou, K. C.; Zhang, C. T., Prediction of protein structural classes, Crit. Rev. Biochem. Mol. Biol., 30, 275-349, (1995)
[18] Chou, K. C.; Cai, Y. D., Using functional domain composition and support vector machines for prediction of protein subcellular location, J. Biol. Chem., 277, 45765-45769, (2002)
[19] Chou, K. C., Iloc-euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins, PLoS One, 6, e18258, (2011)
[20] Chou, K. C., Iloc-hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites, Mol. Biosyst., 8, 629-641, (2012)
[21] Cortes, C.; Vapnik, V., Support-vector networks, Mach. Learn., 20, 273-297, (1995) · Zbl 0831.68098
[22] Davies, J.; Davies, D., Origins and evolution of antibiotic resistance, Microbiol. Mol. Biol. Rev., 74, 417-433, (2010)
[23] Demain, A. L.; Sanchez, S., Microbial drug discovery: 80 years of progress, J. Antibiot. (Tokyo), 62, 5-16, (2009)
[24] Ding, H., Ictx-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels, BioMed Res. Int., 2014, 286419, (2014)
[25] Du, P., Pseaac-general: fast building various modes of general form of chou’s pseudo-amino acid composition for large-scale protein datasets, Int. J. Mol. Sci., 15, 3495-3506, (2014)
[26] Du, P., Pseaac-builder: a cross-platform stand-alone program for generating various special chou’s pseudo-amino acid compositions, Anal. Biochem., 425, 117-119, (2012)
[27] Fawcett, T., An introduction to ROC analysis, Pattern Recognit. Lett., 27, 861-874, (2006)
[28] Feng, P. M., Naive Bayes classifier with feature selection to identify phage virion proteins, Comput. Math. Methods Med., 2013, 530696, (2013) · Zbl 1275.92017
[29] Fisher, J. F., Bacterial resistance to beta-lactam antibiotics: compelling opportunism, compelling opportunity, Chem. Rev., 105, 395-424, (2005)
[30] Franco, B. E., The determinants of the antibiotic resistance process, Infect. Drug Resist., 2, 1-11, (2009)
[31] Furey, T. S., Support vector machine classification and validation of cancer tissue samples using microarray expression data, Bioinformatics, 16, 906-914, (2000)
[32] Guo, S. H., Inuc-pseknc: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition, Bioinformatics, 30, 1522-1529, (2014)
[33] Hall, B. G.; Barlow, M., Revised ambler classification of β-lactamases, J. Antimicrob. Chemother., 55, 1050-1051, (2005)
[34] Han, G. S., A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of chou’s pseaac, J. Theor. Biol., 344, 31-39, (2014)
[35] Ho, I. I.Y., Aminoglycoside resistance in mycobacterium kansasii, mycobacterium avium-M. intracellulare, and mycobacterium fortuitum: are aminoglycoside-modifying enzymes responsible?, Antimicrob. Agents Chemother., 44, 39-42, (2000)
[36] Hua, S.; Sun, Z., A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach, J. Mol. Biol., 308, 397-407, (2001)
[37] Jacoby, G. A.; Archer, G. L., New mechanisms of bacterial resistance to antimicrobial agents, N. Engl. J. Med., 324, 601-612, (1991)
[38] Jia, C., Prediction of protein S-nitrosylation sites based on adapted normal distribution bi-profile Bayes and chou’s pseudo amino acid composition, Int. J. Mol. Sci., 15, 10410-10423, (2014)
[39] Katrijn, B.; Arthur, V. A., Antimicrobial resistance in bacteria, Cent. Eur. J. Med., 141-155, (2009)
[40] Kong, K. F., Beta-lactam antibiotics: from antibiosis to resistance and bacteriology, APMIS, 118, 1-36, (2010)
[41] Kumar, M.; Raghava, G. P., Prediction of nuclear proteins using SVM and HMM models, BMC Bioinf., 10, 22, (2009)
[42] Kumar, M., Prediction of mitochondrial proteins using support vector machine and hidden Markov model, J. Biol. Chem., 281, 5357-5363, (2006)
[43] Kumar, M., Identification of DNA-binding proteins using support vector machines and evolutionary profiles, BMC Bioinf., 8, 463, (2007)
[44] Kumar, M., Prediction of RNA binding sites in a protein using SVM and PSSM profile, Proteins, 71, 189-194, (2008)
[45] Kumar, M., SVM based prediction of RNA-binding proteins using binding residues and evolutionary information, J. Mol. Recognit., 24, 303-313, (2010)
[46] Kumar, R., Protein sub-nuclear localization prediction using SVM and pfam domain information, PLoS One, 9, e98345, (2014)
[47] Kumari, B., Palmpred: an SVM based palmitoylation prediction method using sequence profile information, PLoS One, 9, e89246, (2014)
[48] Li, B. Q., Predict and analyze S-nitrosylation modification sites with the mrmr and IFS approaches, J. Proteomics, 75, 1654-1665, (2012)
[49] Li, L., Prediction of bacterial protein subcellular localization by incorporating various features into chou’s pseaac and a backward feature selection approach, Biochimie, 104, 100-107, (2014)
[50] Li, W.; Godzik, A., Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences, Bioinformatics, 22, 1658-1659, (2006)
[51] Liu, B., Idna-prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition, PLoS One, 9, e106691, (2014)
[52] Liu, B., Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection, Bioinformatics, 30, 472-479, (2014)
[53] Livermore, D. M., Are all beta-lactams created equal?, Scand. J. Infect. Dis. Suppl., 101, 33-43, (1996)
[54] Magrane, M. and UniProt Consortium, 2011. UniProt Knowledgebase: A Hub of Integrated Protein Data. Database (Oxford) 2011. bar009.
[55] McKeegan, K. S., Microbial and viral drug resistance mechanisms, Trends Microbiol., 10, S8-14, (2002)
[56] McManus, M. C., Mechanisms of bacterial resistance to antimicrobial agents, Am. J. Health Syst. Pharm., 54, 1420-1433, (1997), (quiz 1444-6)
[57] Mei, S., Multi-kernel transfer learning based on chou’s pseaac formulation for protein submitochondria localization, J. Theor. Biol., 293, 121-130, (2012) · Zbl 1307.92085
[58] Mishra, N. K., Support vector machine based prediction of glutathione S-transferase proteins, Protein Pept. Lett., 14, 575-580, (2007)
[59] Nanninga, N., Morphogenesis of Escherichia coli, Microbiol. Mol. Biol. Rev., 62, 110-129, (1998)
[60] Nikaido, H., Multidrug resistance in bacteria, Annu. Rev. Biochem., 78, 119-146, (2009)
[61] Petrosino, J., Beta-lactamases: protein evolution in real time, Trends Microbiol., 6, 323-327, (1998)
[62] Poirel, L., Characterization of class 1 integrons from pseudomonas aeruginosa that contain the bla(VIM-2) carbapenem-hydrolyzing beta-lactamase gene and of two novel aminoglycoside resistance gene cassettes, Antimicrob. Agents Chemother., 45, 546-552, (2001)
[63] Qiu, W. R., Irspot-tncpseaac: identify recombination spots with trinucleotide composition and pseudo amino acid components, Int. J. Mol. Sci., 15, 1746-1766, (2014)
[64] Robert, H.; Andreas, C., On qualitative robustness of support vector machines, J. Multivariate Anal., 102, 993-1007, (2011) · Zbl 1274.62285
[65] 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)
[66] 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, (2008)
[67] Shi, S. P., PLMLA: prediction of lysine methylation and lysine acetylation by combining multiple features, Mol. Biosyst., 8, 1520-1527, (2012)
[68] Shien, D. M., Incorporating structural characteristics for identification of protein methylation sites, J. Comput. Chem., 30, 1532-1543, (2009)
[69] Sing, T., ROCR: visualizing classifier performance in R, Bioinformatics, 21, 3940-3941, (2005)
[70] Srivastava, A., Identification of family specific fingerprints in β-lactamase families, Sci. World J., 2014, 2014, 7, (2014)
[71] Vacic, V., Composition profiler: a tool for discovery and visualization of amino acid composition differences, BMC Bioinf., 8, 211, (2007)
[72] Vapnik, V., The nature of statistical learning theory, (1995), Springer New York, NY · Zbl 0833.62008
[73] Wang, L., Linear and nonlinear support vector machine for the classification of human 5-HT1A ligand functionality, Mol. Inf., 31, 85-95, (2012)
[74] Wang, M., Weighted-support vector machines for predicting membrane protein types based on pseudo-amino acid composition, Protein Eng. Des. Sel., 17, 509-516, (2004)
[75] Wang, P., NR-2L: a two-level predictor for identifying nuclear receptor subfamilies based on sequence-derived features, PLoS One, 6, e23505, (2011)
[76] Xiao, X., Iamp-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types, Anal. Biochem., 436, 168-177, (2013)
[77] Xie, H. L., Using ensemble SVM to identify human GPCRs N-linked glycosylation sites based on the general form of chou’s pseaac, Protein Eng. Des. Sel., 26, 735-742, (2013)
[78] Xu, Y., Isno-pseaac: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition, PLoS One, 8, e55844, (2013)
[79] Xu, Y., Ihyd-pseaac: predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition, Int. J. Mol. Sci., 15, 7594-7610, (2014)
[80] Xu, Y., Initro-tyr: prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition, PLoS One, 9, e105018, (2014)
[81] Zervosen, A., Development of new drugs for an old target: the penicillin binding proteins, Molecules, 17, 12478-12505, (2012)
[82] Zhang, J., PSNO: predicting cysteine S-nitrosylation sites by incorporating various sequence-derived features into the general form of chou’s pseaac, Int. J. Mol. Sci., 15, 11204-11219, (2014)
[83] Zhang, L., Predict protein structural class for low-similarity sequences by evolutionary difference information into the general form of chou’s pseudo amino acid composition, J. Theor. Biol., 355, 105-110, (2014)
[84] Zhou, G. P.; Assa-Munt, N., Some insights into protein structural class prediction, Proteins, 44, 57-59, (2001)
[85] Zien, A., Engineering support vector machine kernels that recognize translation initiation sites, Bioinformatics, 16, 799-807, (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.