ProtIdent: A web server for identifying proteases and their types by fusing functional domain and sequential evolution information. Proteases are vitally important to life cycles and have become a main target in drug development. According to their action mechanisms, proteases are classified into six types: (1) aspartic, (2) cysteine, (3) glutamic, (4) metallo, (5) serine, and (6) threonine. Given the sequence of an uncharacterized protein, can we identify whether it is a protease or non-protease? If it is, what type does it belong to? To address these problems, a 2-layer predictor, called “ProtIdent”, is developed by fusing the functional domain and sequential evolution information: the first layer is for identifying the query protein as protease or non-protease; if it is a protease, the process will automatically go to the second layer to further identify it among the six types. The overall success rates in both cases by rigorous cross-validation tests were higher than 92%. ProtIdent is freely accessible to the public as a web server at

References in zbMATH (referenced in 21 articles )

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  1. 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)
  2. Lyons, James; Biswas, Neela; Sharma, Alok; Dehzangi, Abdollah; Paliwal, Kuldip K.: Protein fold recognition by alignment of amino acid residues using kernelized dynamic time warping (2014)
  3. Mishra, Pooja; Nath Pandey, Paras: Elman RNN based classification of proteins sequences on account of their mutual information (2012)
  4. Yang, Lianping; Zhang, Xiangde; Zhu, Hegui: Alignment free comparison: similarity distribution between the DNA primary sequences based on the shortest absent word (2012)
  5. Chou, Kuo-Chen: Some remarks on protein attribute prediction and pseudo amino acid composition (2011)
  6. Kavousi, Kaveh; Moshiri, Behzad; Sadeghi, Mehdi; Araabi, Babak N.; Moosavi-Movahedi, Ali Akbar: A protein fold classifier formed by fusing different modes of pseudo amino acid composition via PSSM (2011)
  7. Lin, Hao; Ding, Hui: Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition (2011)
  8. 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)
  9. Esmaeili, Maryam; Mohabatkar, Hassan; Mohsenzadeh, Sasan: Using the concept of Chou’s pseudo amino acid composition for risk type prediction of human papillomaviruses (2010)
  10. Huang, Wei; Zhang, Jianmin; Wang, Yurong; Huang, Dan: A simple method to analyze the similarity of biological sequences based on the fuzzy theory (2010)
  11. Ji, Guoli; Wu, Xiaohui; Shen, Yingjia; Huang, Jiangyin; Quinn Li, Qingshun: A classification-based prediction model of messenger RNA polyadenylation sites (2010)
  12. Nanni, Loris; Brahnam, Sheryl; Lumini, Alessandra: High performance set of PseAAC and sequence based descriptors for protein classification (2010)
  13. Nanni, Loris; Shi, Jian-Yu; Brahnam, Sheryl; Lumini, Alessandra: Protein classification using texture descriptors extracted from the protein backbone image (2010)
  14. Yang, Jie; Li, Jia-Huang; Wang, Jin; Zhang, Chen-Yu: Molecular modeling of BAD complex resided in a mitochondrion integrating glycolysis and apoptosis (2010)
  15. Yang, Lianping; Zhang, Xiangde; Wang, Tianming: The Burrows-Wheeler similarity distribution between biological sequences based on Burrows-Wheeler transform (2010)
  16. Anand, Ashish; Suganthan, P. N.: Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates (2009)
  17. Jahandideh, Samad; Hoseini, Somayyeh; Jahandideh, Mina; Hoseini, Afsaneh; Miri Disfani, Fatemeh: (\gamma)-turn types prediction in proteins using the two-stage hybrid neural discriminant model (2009)
  18. Shao, Xiaojian; Tian, Yingjie; Wu, Lingyun; Wang, Yong; Jing, Ling; Deng, Naiyang: Predicting DNA- and RNA-binding proteins from sequences with kernel methods (2009)
  19. Vilar, Santiago; González-Díaz, Humberto; Santana, Lourdes; Uriarte, Eugenio: A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer (2009)
  20. Yang, Jian-Yi; Peng, Zhen-Ling; Yu, Zu-Guo; Zhang, Rui-Jie; Anh, Vo; Wang, Desheng: Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation (2009)

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