title: Probabilistic approach to predicting substrate specificity of methyltransferases.
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.
authors.affiliation: Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland; Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, Texas, United States of America; Institute for Translational Sciences, University of Texas Medical Branch, Galveston, Texas, United States of America; Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland; Institute of Genetics and Biotechnology, Faculty of Biology, University of Warsaw, Warsaw, Poland.
authors.forename: Teresa
authors.initials: T
authors.lastname: Szczepińska
authors.affiliation: Laboratory of Bioinformatics and Systems Biology, Centre of New Technologies, University of Warsaw, Warsaw, Poland.
authors.forename: Jan
authors.initials: J
authors.lastname: Kutner
authors.affiliation: Laboratory of Bioinformatics and Systems Biology, Centre of New Technologies, University of Warsaw, Warsaw, Poland.
authors.forename: Michał
authors.initials: M
authors.lastname: Kopczyński
authors.affiliation: Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland; Warsaw University of Life Sciences, Warsaw, Poland.
authors.forename: Krzysztof
authors.initials: K
authors.lastname: Pawłowski
authors.affiliation: Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland; Institute of Genetics and Biotechnology, Faculty of Biology, University of Warsaw, Warsaw, Poland.
authors.forename: Andrzej
authors.initials: A
authors.lastname: Dziembowski
authors.affiliation: Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, Texas, United States of America; Institute for Translational Sciences, University of Texas Medical Branch, Galveston, Texas, United States of America; Sealy Center for Molecular Medicine, University of Texas Medical Branch, Galveston, Texas, United States of America.
authors.forename: Andrzej
authors.initials: A
authors.lastname: Kudlicki
authors.affiliation: Laboratory of Bioinformatics and Systems Biology, Centre of New Technologies, University of Warsaw, Warsaw, Poland.
authors.forename: Krzysztof
authors.initials: K
authors.lastname: Ginalski
authors.affiliation: Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, Texas, United States of America; Institute for Translational Sciences, University of Texas Medical Branch, Galveston, Texas, United States of America; Sealy Center for Molecular Medicine, University of Texas Medical Branch, Galveston, Texas, United States of America.
authors.forename: Maga
authors.initials: M
authors.lastname: Rowicka
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods; D005656:Fungal Proteins / Q000737:chemistry*; D007526:Isoelectric Point; D016013:Likelihood Functions; D008780:Methyltransferases / Q000737:chemistry*; D008962:Models, Theoretical; D011336:Probability; D011485:Protein Binding; D017510:Protein Folding; D012313:RNA / Q000737:chemistry; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D013379:Substrate Specificity
chemical_list: D005656:Fungal Proteins; D012313:RNA; D008780:Methyltransferases