Abstract
Methylation of DNA, protein and even RNA species are integral processes in epigenesis. Enzymes that catalyze these reactions using the donor S-adenosylmethionine fall into several structurally distinct classes. The members in each class share sequence similarity that can be used to identify additional methyltransferases. Here, we characterize these classes and in silico approaches to infer protein function. Computational methods, such as hidden Markov model profiling and the Multiple Motif Scanning program, can be used to analyze known methyltransferases and relay information into the prediction of new ones. In some cases, the substrate of methylation can be inferred from hidden Markov model sequence similarity networks. Functional identification of these candidate species is much more difficult; we discuss one biochemical approach.
Acknowledgements
We are grateful to Professor Christopher Lee for his comments on this work.
Financial & competing interests disclosure
This work was supported by National Institutes of Health Grant GM026020. Tanya Petrossian was supported by the UCLA Chemistry–Biology Interface Training Grant GM008496. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
No writing assistance was utilized in the production of this manuscript.