iAMP-2L

iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Antimicrobial peptides (AMPs), also called host defense peptides, are an evolutionarily conserved component of the innate immune response and are found among all classes of life. According to their special functions, AMPs are generally classified into ten categories: Antibacterial Peptides, Anticancer/tumor Peptides, Antifungal Peptides, Anti-HIV Peptides, Antiviral Peptides, Antiparasital Peptides, Anti-protist Peptides, AMPs with Chemotactic Activity, Insecticidal Peptides, and Spermicidal Peptides. Given a query peptide, how can we identify whether it is an AMP or non-AMP? If it is, can we identify which functional type or types it belong to? Particularly, how can we deal with the multi-type problem since an AMP may belong to two or more functional types? To address these problems, which are obviously very important to both basic research and drug development, a multi-label classifier was developed based on the pseudo amino acid composition (PseAAC) and fuzzy K-nearest neighbor (FKNN) algorithm, where the components of PseAAC were featured by incorporating five physicochemical properties. The novel classifier is called iAMP-2L, where ”2L” means that it is a 2-level predictor. The 1st-level is to answer the 1st question above, while the 2nd-level is to answer the 2nd and 3rd questions that are beyond the reach of any existing methods in this area. For the conveniences of users, a user-friendly web-server for iAMP-2L was established at http://www.jci-bioinfo.cn/iAMP-2L.


References in zbMATH (referenced in 20 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. Akbar, Shahid; Hayat, Maqsood: iMethyl-STTNC: identification of N(^6)-methyladenosine sites by extending the idea of SAAC into Chou’s PseAAC to formulate RNA sequences (2018)
  3. Mei, Juan; Fu, Yi; Zhao, Ji: Analysis and prediction of ion channel inhibitors by using feature selection and Chou’s general pseudo amino acid composition (2018)
  4. Sabooh, M. Fazli; Iqbal, Nadeem; Khan, Mukhtaj; Khan, Muslim; Maqbool, H. F.: Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou’s PseKNC (2018)
  5. Sankari, E. Siva; Manimegalai, D.: Predicting membrane protein types by incorporating a novel feature set into Chou’s general PseAAC (2018)
  6. Srivastava, Abhishikha; Kumar, Ravindra; Kumar, Manish: BlaPred: predicting and classifying (\beta)-lactamase using a 3-tier prediction system via Chou’s general PseAAC (2018)
  7. Tarafder, Sumit; Toukir Ahmed, Md.; Iqbal, Sumaiya; Tamjidul Hoque, Md; Sohel Rahman, M.: RBSURFpred: modeling protein accessible surface area in real and binary space using regularized and optimized regression (2018)
  8. Khan, Muslim; Hayat, Maqsood; Khan, Sher Afzal; Ahmad, Saeed; Iqbal, Nadeem: Bi-PSSM: position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins (2017)
  9. Saghapour, Ehsan; Sehhati, Mohammadreza: Prediction of metastasis in advanced colorectal carcinomas using CGH data (2017)
  10. 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)
  11. 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)
  12. Jiao, Ya-Sen; Du, Pu-Feng: Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (2016)
  13. Ali, Farman; Hayat, Maqsood: Classification of membrane protein types using voting feature interval in combination with Chou’s pseudo amino acid composition (2015)
  14. 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)
  15. Kou, Gaoshan; Feng, Yonge: Identify five kinds of simple super-secondary structures with quadratic discriminant algorithm based on the chemical shifts (2015)
  16. 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)
  17. Liu, Guoqing; Xing, Yongqiang; Cai, Lu: Using weighted features to predict recombination hotspots in \textitSaccharomycescerevisiae (2015)
  18. Nanni, Loris; Brahnam, Sheryl; Lumini, Alessandra: Prediction of protein structure classes by incorporating different protein descriptors into general Chou’s pseudo amino acid composition (2014)
  19. Feng, Peng-Mian; Ding, Hui; Chen, Wei; Lin, Hao: Naïve Bayes classifier with feature selection to identify phage virion proteins (2013)
  20. Pan, Lu-Lu; Wang, Yu; Hu, Jian-Hui; Ding, Zhao-Tang; Li, Chen: Analysis of codon use features of stearoyl-acyl carrier protein desaturase gene in \textitCamelliasinensis (2013)