Probabilistic Approach to Predicting Substrate Specificity of Methyltransferases

Teresa Szczepińska, Jan Kutner, Michał Kopczyński, Krzysztof Pawłowski, Andrzej Dziembowski, Andrzej Kudlicki, Krzysztof Ginalski, Maga Rowicka

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


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.

Original languageEnglish (US)
Article numbere1003514
JournalPLoS computational biology
Issue number3
StatePublished - Mar 2014

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics


Dive into the research topics of 'Probabilistic Approach to Predicting Substrate Specificity of Methyltransferases'. Together they form a unique fingerprint.

Cite this