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Probabilistic approach to predicting substrate specificity of methyltransferases.

Szczepińska, Teresa and Kutner, Jan and Kopczyński, Michał and Pawłowski, Krzysztof and Dziembowski, Andrzej and Kudlicki, Andrzej and Ginalski, Krzysztof and Rowicka, Magda (2014) Probabilistic approach to predicting substrate specificity of methyltransferases. PLoS computational biology, 10 (3). e1003514. ISSN 1553-7358



We present a general probabilistic framework for predicting the substrate specificity of enzymes. We designed this approach to be easily applicable to different organisms and enzymes. Therefore, our predictive models do not rely on species-specific properties and use mostly sequence-derived data. Maximum Likelihood optimization is used to fine-tune model parameters and the Akaike Information Criterion is employed to overcome the issue of correlated variables. As a proof-of-principle, we apply our approach to predicting general substrate specificity of yeast methyltransferases (MTases). As input, we use several physico-chemical and biological properties of MTases: structural fold, isoelectric point, expression pattern and cellular localization. Our method accurately predicts whether a yeast MTase methylates a protein, RNA or another molecule. Among our experimentally tested predictions, 89% were confirmed, including the surprising prediction that YOR021C is the first known MTase with a SPOUT fold that methylates a substrate other than RNA (protein). Our approach not only allows for highly accurate prediction of functional specificity of MTases, but also provides insight into general rules governing MTase substrate specificity.

Item Type:Article
Subjects:Q Science > Q Science (General)
Q Science > QA Mathematics
Q Science > QH Natural history > QH301 Biology
ID Code:844
Deposited By: Dr Teresa Szczepinska
Deposited On:19 Dec 2014 10:20
Last Modified:29 Sep 2015 15:01

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