Bioinformatic identification of novel methyltransferases

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.