ArticlePLoS computational biology2014
Probabilistic approach to predicting substrate specificity of methyltransferases.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The protein methylation network in yeast: A landmark in completeness for a eukaryotic post-translational modification.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Emergence of the primordial pre-60S from the 90S pre-ribosome.Cell reports · 2022Article
- Expression and analysis of the SAM-dependent RNA methyltransferase Rsm22 from Saccharomyces cerevisiae.Acta crystallographica. Section D, Structural biology · 2021Article
- Arginine Methylation Regulates Ribosome CAR Function.International journal of molecular sciences · 2021Article
- Predicting proteome dynamics using gene expression data.Scientific reports · 2018Article
- Determining the Mitochondrial Methyl Proteome in Saccharomyces cerevisiae using Heavy Methyl SILAC.Journal of proteome research · 2016Article
- The activity of a yeast Family 16 methyltransferase, Efm2, is affected by a conserved tryptophan and its N-terminal region.FEBS open bio · 2016Article
- Novel N-terminal and Lysine Methyltransferases That Target Translation Elongation Factor 1A in Yeast and Human.Molecular & cellular proteomics : MCP · 2016Article
- Structural basis for Sfm1 functioning as a protein arginine methyltransferase.Cell discovery · 2015Article
- A new type of protein lysine methyltransferase trimethylates Lys-79 of elongation factor 1A.Biochemical and biophysical research communications · 2014Article
- Translational roles of elongation factor 2 protein lysine methylation.The Journal of biological chemistry · 2014Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
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.
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Registered trials
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