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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1002/jcc.25353
|2 doi
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|a pubmed25n0946.xml
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|a (DE-627)NLM284081302
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|a DE-627
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|c DE-627
|e rakwb
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|a eng
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1 |
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|a Taherzadeh, Ghazaleh
|e verfasserin
|4 aut
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| 245 |
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|a Predicting lysine-malonylation sites of proteins using sequence and predicted structural features
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|c 2018
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 17.09.2019
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|a Date Revised 17.09.2019
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2018 Wiley Periodicals, Inc.
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|a Malonylation is a recently discovered post-translational modification (PTM) in which a malonyl group attaches to a lysine (K) amino acid residue of a protein. In this work, a novel machine learning model, SPRINT-Mal, is developed to predict malonylation sites by employing sequence and predicted structural features. Evolutionary information and physicochemical properties are found to be the two most discriminative features whereas a structural feature called half-sphere exposure provides additional improvement to the prediction performance. SPRINT-Mal trained on mouse data yields robust performance for 10-fold cross validation and independent test set with Area Under the Curve (AUC) values of 0.74 and 0.76 and Matthews' Correlation Coefficient (MCC) of 0.213 and 0.20, respectively. Moreover, SPRINT-Mal achieved comparable performance when testing on H. sapiens proteins without species-specific training but not in bacterium S. erythraea. This suggests similar underlying physicochemical mechanisms between mouse and human but not between mouse and bacterium. SPRINT-Mal is freely available as an online server at: http://sparks-lab.org/server/SPRINT-Mal/. © 2018 Wiley Periodicals, Inc
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a lysine-malonylation sites prediction
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| 650 |
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|a post translational modification
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| 650 |
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|a support vector machines
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| 650 |
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|a Bacterial Proteins
|2 NLM
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| 650 |
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|a Malonates
|2 NLM
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| 650 |
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|a Lysine
|2 NLM
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| 650 |
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7 |
|a K3Z4F929H6
|2 NLM
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| 700 |
1 |
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|a Yang, Yuedong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xu, Haodong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xue, Yu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liew, Alan Wee-Chung
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Yaoqi
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 39(2018), 22 vom: 15. Aug., Seite 1757-1763
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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| 773 |
1 |
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|g volume:39
|g year:2018
|g number:22
|g day:15
|g month:08
|g pages:1757-1763
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|u http://dx.doi.org/10.1002/jcc.25353
|3 Volltext
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