Probabilistic Approach to Predicting Substrate Specificity of Methyltransferases

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.Author Summary: Our approach is easily applicable to different organisms, because it does not rely on species-specific properties and uses mostly sequence-derived and other readily available data (e.g. isoelectric point or predicted structural fold). Tests on yeast MTases indicate that the accuracy of our predictions is ∼90%. We show that knowledge of substrate binding sites or corresponding motifs is not crucial for highly accurate general substrate specificity predictions of enzymes, and provide new insights into how such specificities are achieved at the molecular level. We predict substrate specificities not yet observed for a given class of enzymes, and experimentally verify our predictions.