Predicting lysine‐malonylation sites of proteins using sequence and predicted structural features

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 stru...

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Bibliographic Details
Published in:Journal of computational chemistry 2018-08, Vol.39 (22), p.1757-1763
Main Authors: Taherzadeh, Ghazaleh, Yang, Yuedong, Xu, Haodong, Xue, Yu, Liew, Alan Wee‐Chung, Zhou, Yaoqi
Format: Article
Language:English
Subjects:
Bacteria
Correlation coefficient
Correlation coefficients
Lysine
lysine‐malonylation sites prediction
Machine learning
Physicochemical properties
post translational modification
Predictions
Proteins
support vector machines
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Summary: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. Malonylation is a recently discovered post‐translational modification in which a malonyl group attaches to a lysine amino acid of a protein. Identifying malonylation sites in a protein sequence is the first step toward discovering its biological function. Thus, making “educated” computational prediction prior to experimental studies is necessary. All existing prediction tools infer malonylation sites from sequence‐derived features of proteins. Here, we developed a method employing sequence and predicted structural features of proteins which outperforms existing methods significantly.
ISSN:0192-8651
1096-987X
DOI:10.1002/jcc.25353