SecretP: identifying bacterial secreted proteins by fusing new features into Chou’s pseudo-amino acid composition. Protein secretion plays an important role in bacterial lifestyles. Secreted proteins are crucial for bacterial pathogenesis by making bacteria interact with their environments, particularly delivering pathogenic and symbiotic bacteria into their eukaryotic hosts. Therefore, identification of bacterial secreted proteins becomes an important process for the study of various diseases and the corresponding drugs. In this paper, fusing several new features into Chou’s pseudo-amino acid composition (PseAAC), two support vector machine (SVM)-based ternary classifiers are developed to predict secreted proteins of Gram-negative and Gram-positive bacteria. For the two types of bacteria, the high accuracy of 94.03% and 94.36% are obtained in distinguishing classically secreted, non-classically secreted and non-secreted proteins by our method. In order to compare the practical ability of our method in identifying bacterial secreted proteins with those of six published methods, proteins in Escherichia coli and Bacillus subtilis are collected to construct the test sets of Gram-negative and Gram-positive bacteria, and the prediction results of our method are comparable to those of existing methods. When performed on two public independent data sets for predicting NCSPs, it also yields satisfactory results for Gram-negative bacterial proteins. The prediction server SecretP can be accessed at

References in zbMATH (referenced in 14 articles )

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  1. Huang, Chao; Yuan, Jing-Qi: Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou’s pseudo amino acid compositions (2013)
  2. Li, Yao-Wang; Li, Bo: Characterization of structure-antioxidant activity relationship of peptides in free radical systems using QSAR models: key sequence positions and their amino acid properties (2013)
  3. Yu, Chenglong; Deng, Mo; Cheng, Shiu-Yuen; Yau, Shek-Chung; He, Rong L.; Yau, Stephen S.-T.: Protein space: a natural method for realizing the nature of protein universe (2013)
  4. Fan, Guo-Liang; Li, Qian-Zhong: Predicting mycobacterial proteins subcellular locations by incorporating pseudo-average chemical shift into the general form of Chou’s pseudo amino acid composition (2012)
  5. Hayat, Maqsood; Khan, Asifullah: MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM (2012)
  6. Hemmateenejad, Bahram; Miri, Ramin; Elyasi, Maryam: A segmented principal component analysis -- regression approach to QSAR study of peptides (2012)
  7. Jahandideh, Samad; Srinivasasainagendra, Vinodh; Zhi, Degui: Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection (2012)
  8. Lu, Jin-Long; Hu, Xue-Hai; Hu, Dong-Gang: A new hybrid fractal algorithm for predicting thermophilic nucleotide sequences (2012)
  9. Mei, Suyu: Multi-kernel transfer learning based on Chou’s PseAAC formulation for protein submitochondria localization (2012)
  10. Qiu, Zhijun; Wang, Xicheng: Prediction of protein-protein interaction sites using patch-based residue characterization (2012)
  11. Chou, Kuo-Chen: Some remarks on protein attribute prediction and pseudo amino acid composition (2011)
  12. Mohabatkar, Hassan; Mohammad Beigi, Majid; Esmaeili, Abolghasem: Prediction of GABA(_\mathrmA) receptor proteins using the concept of Chou’s pseudo-amino acid composition and support vector machine (2011)
  13. Xiao, Xuan; Wu, Zhi-Cheng; Chou, Kuo-Chen: \textbfiLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites (2011)
  14. Yu, Lezheng; Guo, Yanzhi; Li, Yizhou; Li, Gongbing; Li, Menglong; Luo, Jiesi; Xiong, Wenjia; Qin, Wenli: SecretP: identifying bacterial secreted proteins by fusing new features into Chou’s pseudo-amino acid composition (2010)