PoPS: a computational tool for modeling and predicting protease specificity. Proteases play a fundamental role in the control of intra- and extra-cellular processes by binding and cleaving specific amino acid sequences. Identifying these targets is extremely challenging. Current computational attempts to predict cleavage sites are limited, representing these amino acid sequences as patterns or frequency matrices. Here we present PoPS, a publicly accessible bioinformatics tool () that provides a novel method for building computational models of protease specificity, which while still being based on these amino acid sequences, can be built from any experimental data or expert knowledge available to the user. PoPS specificity models can be used to predict and rank likely cleavages within a single substrate, and within entire proteomes. Other factors, such as the secondary or tertiary structure of the substrate, can be used to screen unlikely sites. Furthermore, the tool also provides facilities to infer, compare and test models, and to store them in a publicly accessible database.
References in zbMATH (referenced in 3 articles )
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- Xu, Shaobing; Li, Shengbo; Cheng, Bo: Theory and application of Legendre pseudo-spectral method for solving optimal control problems (2014)
- Zhang, Wenyi; Xu, Xin; Jia, Longjia; Ma, Zhiqiang; Luo, Na; Wang, Jianan: The prediction of calpain cleavage sites with the mrmr and IFS approaches (2013) ioport
- Boyd, Sarah E.; Pike, Robert N.; Rudy, George B.; Whisstock, James C.; La Banda, Maria Garcia De; Wong, Limsoon; Li, Ming; Wooley, John: Pops: A computational tool for modeling and predicting protease specificity (2005) ioport