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 http://www.csbio.sjtu.edu.cn/bioinf/Protease.
Keywords for this software
References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- 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)
- Mishra, Pooja; Nath Pandey, Paras: Elman RNN based classification of proteins sequences on account of their mutual information (2012)
- Yang, Lianping; Zhang, Xiangde; Zhu, Hegui: Alignment free comparison: similarity distribution between the DNA primary sequences based on the shortest absent word (2012)
- Lin, Hao; Ding, Hui: Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition (2011)