Computational Prediction and Analysis for Tyrosine
Post-Translational Modifications via Elastic Net
Posted on 2018-05-18 - 18:21
The tyrosine residue
has been identified as suffering three major
post-translational modifications (PTMs) including nitration, sulfation,
and phosphorylation, which could be involved in different physiological
and pathological processes. Multiple tyrosine residues of the whole
protein may be modified concurrently, where PTM of a single tyrosine
may affect modification of other neighboring tyrosine residues. Hence,
it is significant and beneficial to predict nitration, sulfation,
and phosphorylation of tyrosine residues in the whole protein sequence.
Here, we introduce elastic net to perform feature selection and develop
a predictor named TyrPred for predicting nitrotyrosine, sulfotyrosine,
and kinase-specific tyrosine phosphorylation sites on the basis of
support vector machine. We critically evaluate the performance of
TyrPred and compare it with other existing tools. The satisfying results
show that using elastic net to mine important features for training
can considerably improve the prediction performance. Feature optimization
indicates that evolutionary information is significant and contributes
to the prediction model. The online tool is established at http://computbiol.ncu.edu.cn/TyrPred. We anticipate that TyrPred can provide useful complements to the
existing approaches in this field.
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Cao, Man; Chen, Guodong; Wang, Lina; Wen, Pingping; Shi, Shaoping (2018). Computational Prediction and Analysis for Tyrosine
Post-Translational Modifications via Elastic Net. ACS Publications. Collection. https://doi.org/10.1021/acs.jcim.7b00688
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AUTHORS (5)
MC
Man Cao
GC
Guodong Chen
LW
Lina Wang
PW
Pingping Wen
SS
Shaoping Shi