Title
PROSPECT: A web server for predicting protein histidine phosphorylation sites
Abstract
Background: Phosphorylation of histidine residues plays crucial roles in signaling pathways and cell metabolism in prokaryotes such as bacteria. While evidence has emerged that protein histidine phosphorylation also occurs in more complex organisms, its role in mammalian cells has remained largely uncharted. Thus, it is highly desirable to develop computational tools that are able to identify histidine phosphorylation sites. Result: Here, we introduce PROSPECT that enables fast and accurate prediction of proteome-wide histidine phosphorylation substrates and sites. Our tool is based on a hybrid method that integrates the outputs of two convolutional neural network (CNN)-based classifiers and a random forest-based classifier. Three features, including the one-of-K coding, enhanced grouped amino acids content (EGAAC) and composition of k-spaced amino acid group pairs (CKSAAGP) encoding, were taken as the input to three classifiers, respectively. Our results show that it is able to accurately predict histidine phosphorylation sites from sequence information. Our PROSPECT web server is user-friendly and publicly available at http://PROSPECT.erc.monash.edu/. Conclusions: PROSPECT is superior than other pHis predictors in both the running speed and prediction accuracy and we anticipate that the PROSPECT webserver will become a popular tool for identifying the pHis sites in bacteria.
Year
DOI
Venue
2020
10.1142/S0219720020500183
JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY
Keywords
DocType
Volume
Protein phosphorylation,histine phosphorylation,bioinformatics,sequence analysis,deep learning,pattern recognition
Journal
18
Issue
ISSN
Citations 
4
0219-7200
1
PageRank 
References 
Authors
0.36
0
9
Name
Order
Citations
PageRank
Zhen Chen110.36
Pei Zhao2221.39
Fuyi Li39711.25
André Leier419719.87
Tatiana T. Marquez-Lago5779.01
Geoffrey I. Webb63130234.10
Abdelkader Baggag783.82
Halima Bensmail823519.80
Jiangning Song937441.93