Prediction of β-lactamase and its class by Chou’s pseudo-amino acid composition and support vector machine. β-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. β-Lactamases are endogenously produced enzyme that makes bacteria resistant against β-lactam antibiotics by cleaving the β-lactam ring. On the basis of primary structures, β-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 β-lactamases protein prediction method, which differentiate β-lactamases from non-β-lactamases at first level and then classify predicted β-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 β-lactamases from non-β-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

References in zbMATH (referenced in 12 articles )

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  1. Hussain, Waqar; Khan, Yaser Daanial; Rasool, Nouman; Khan, Sher Afzal; Chou, Kuo-Chen: SPrenylC-PseAAC: a sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins (2019)
  2. Jia, Jianhua; Li, Xiaoyan; Qiu, Wangren; Xiao, Xuan; Chou, Kuo-Chen: iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC (2019)
  3. Cheng, Xiang; Xiao, Xuan; Chou, Kuo-Chen: pLoc_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC (2018)
  4. Srivastava, Abhishikha; Kumar, Ravindra; Kumar, Manish: BlaPred: predicting and classifying (\beta)-lactamase using a 3-tier prediction system via Chou’s general PseAAC (2018)
  5. Jia, Jianhua; Liu, Zi; Xiao, Xuan; Liu, Bingxiang; Chou, Kuo-Chen: pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach (2016)
  6. Jiao, Ya-Sen; Du, Pu-Feng: Prediction of Golgi-resident protein types using general form of Chou’s pseudo-amino acid compositions: approaches with minimal redundancy maximal relevance feature selection (2016)
  7. Jiao, Ya-Sen; Du, Pu-Feng: Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (2016)
  8. Mishra, Avdesh; Iqbal, Sumaiya; Hoque, Md Tamjidul: Discriminate protein decoys from native by using a scoring function based on ubiquitous phi and psi angles computed for all atom (2016)
  9. Muthu Krishnan, S.: Classify vertebrate hemoglobin proteins by incorporating the evolutionary information into the general PseAAC with the hybrid approach (2016)
  10. Yang, Lianping; Zhang, Xiangde; Fu, Haoyue; Yang, Chenhui: An estimator for local analysis of genome based on the minimal absent word (2016)
  11. Ju, Zhe; Cao, Jun-Zhe; Gu, Hong: iLM-2L: a two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou’s general PseAAC (2015)
  12. Kumar, Ravindra; Srivastava, Abhishikha; Kumari, Bandana; Kumar, Manish: Prediction of (\beta)-lactamase and its class by Chou’s pseudo-amino acid composition and support vector machine (2015)