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. Ma...

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Autores principales:	Teresa Szczepińska, Jan Kutner, Michał Kopczyński, Krzysztof Pawłowski, Andrzej Dziembowski, Andrzej Kudlicki, Krzysztof Ginalski, Maga Rowicka
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/ea542f2c61514b8b97d558b23c4cb478
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spelling	oai:doaj.org-article:ea542f2c61514b8b97d558b23c4cb4782021-11-18T05:53:04ZProbabilistic approach to predicting substrate specificity of methyltransferases.1553-734X1553-735810.1371/journal.pcbi.1003514https://doaj.org/article/ea542f2c61514b8b97d558b23c4cb4782014-03-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24651469/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358We 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.Teresa SzczepińskaJan KutnerMichał KopczyńskiKrzysztof PawłowskiAndrzej DziembowskiAndrzej KudlickiKrzysztof GinalskiMaga RowickaPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 10, Iss 3, p e1003514 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Teresa Szczepińska Jan Kutner Michał Kopczyński Krzysztof Pawłowski Andrzej Dziembowski Andrzej Kudlicki Krzysztof Ginalski Maga Rowicka Probabilistic approach to predicting substrate specificity of methyltransferases.
description	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.
format	article
author	Teresa Szczepińska Jan Kutner Michał Kopczyński Krzysztof Pawłowski Andrzej Dziembowski Andrzej Kudlicki Krzysztof Ginalski Maga Rowicka
author_facet	Teresa Szczepińska Jan Kutner Michał Kopczyński Krzysztof Pawłowski Andrzej Dziembowski Andrzej Kudlicki Krzysztof Ginalski Maga Rowicka
author_sort	Teresa Szczepińska
title	Probabilistic approach to predicting substrate specificity of methyltransferases.
title_short	Probabilistic approach to predicting substrate specificity of methyltransferases.
title_full	Probabilistic approach to predicting substrate specificity of methyltransferases.
title_fullStr	Probabilistic approach to predicting substrate specificity of methyltransferases.
title_full_unstemmed	Probabilistic approach to predicting substrate specificity of methyltransferases.
title_sort	probabilistic approach to predicting substrate specificity of methyltransferases.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/ea542f2c61514b8b97d558b23c4cb478
work_keys_str_mv	AT teresaszczepinska probabilisticapproachtopredictingsubstratespecificityofmethyltransferases AT jankutner probabilisticapproachtopredictingsubstratespecificityofmethyltransferases AT michałkopczynski probabilisticapproachtopredictingsubstratespecificityofmethyltransferases AT krzysztofpawłowski probabilisticapproachtopredictingsubstratespecificityofmethyltransferases AT andrzejdziembowski probabilisticapproachtopredictingsubstratespecificityofmethyltransferases AT andrzejkudlicki probabilisticapproachtopredictingsubstratespecificityofmethyltransferases AT krzysztofginalski probabilisticapproachtopredictingsubstratespecificityofmethyltransferases AT magarowicka probabilisticapproachtopredictingsubstratespecificityofmethyltransferases
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Probabilistic approach to predicting substrate specificity of methyltransferases.

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