in the post - genomic era , much attention has been paid to understanding the dynamics of the proteome , transcriptional regulation and post - translational modification of proteins ( ptms ) .
numerous ptms supply the proteome with structural and functional diversity , and govern cellular plasticity and dynamics .
types of ptms include phosphorylation ( 1,2 ) , sumoylation ( 3 ) , ubiquitination and methylation .
compared to well - known and extensively studied protein phosphorylation ( 1,2 ) , protein methylation attracts much less attention , despite the fact that it was discovered nearly half a century ago ( 4 ) .
protein methylation can modify the nitrogen atoms of either the backbone or side - chain ( n - methylation ) in several types of amino acids , such as lysine , arginine , histidine , alainine and asparagine , etc ( 511 ) .
also , methylation occurs at cysteine residues as s - methylation ( 12 ) . in this field ,
lysine residues can be mono- , di- or tri - methylated by histone lysine methyltransferases ( hkmts ) ( 5,8,10,11 ) .
the methylation of lysine has been mostly studied in h3 and h4 histone proteins , which play essential roles in many biological processes , such as heterochromatin compaction , x - chromosome inactivation and transcriptional silencing or activation ( 5,10,11 ) .
furthermore , the hkmts also modify a variety of non - histone proteins with diverse functions ( 5,8,10,11 ) .
for example , set9 methylates a transcription factor taf10 to increase its interacting affinity for rna polymerase ii , which is implicated in transcriptional regulation of taf10 target genes ( 13 ) .
in addition , methylation of p53 by set9 in vivo increases its stability and regulates the expression of p53-dependent genes ( 14 ) .
furthermore , the activity of lysine methylation of cytosolic ezh2-containing methyltransferase complex is essential for receptor - induced actin organization and proliferation ( 15 ) .
protein methylation can also occur on the guanidino nitrogen atoms of arginine ( 6,7,9,10,16 ) .
histone code together with lysine methylation ( 5,11 ) , the substrates of prmts are much more diverse than hkmts ( 6,7,10 ) .
indeed , arginine methylation plays important roles in numerous cellular processes , including rna processing , transcriptional regulation , signal transduction and dna repair ( 6,7,10 ) .
for example , arginine methylation of spt5 regulates its binding with rna polymerase ii to modulate the transcriptional elongation ( 17 ) . and pmrt1 methylates nip45 , the nuclear factor of activated t cell ( nfat ) cofactor protein , to play an essential role in cytokine gene transcription ( 18 ) .
in addition , as a potential role  arginine protection , prmts may modify and protect the arginines against endogenous reactive methylglyoxal ( 9 ) .
recent study shows that lsd1 ( lysine - specific demethylase 1 ) is responsible for the demethylation of histone h3 lysine 4 ( 5 ) .
very recently , it has been verified that jhdm1 ( jmjc domain - containing histone demethylase 1 ) is responsible for the demethylation of lysine 36 ( 19 ) .
furthermore , peptidyl - arginine deiminase pad4 is able to deiminate both unmodified arginine and monomethylarginine residues in histones into citrullines ( 6 ) .
identification of methylated proteins with their sites will be a foundation of understanding the molecular mechanism of protein methylation . besides the conventional experimental methods , such as mutagenesis of potential methylated residues , methylation - specific antibodies ( 20 ) and mass - spectrometry ( 2123 ) have also been deployed .
therefore computational prediction of methylation sites is much more desirable for its convenience and fast speed .
unfortunately , although many methods with satisfying accuracies have been developed to predict phosphorylated protein sites ( 2,24 ) , only one work , which focuses on only disordered regions of considered proteins , has been published on prediction of methylation sites ( 25 ) . in this work ,
we provide a novel online tool for protein methylation site prediction of memo , protein methylation modification prediction , employing the algorithm of support vector machines ( svms ) ( 26 ) ( ) . we have collected all annotated methylated residues in swiss - prot version 48 ( 27 ) .
then we have combined the two datasets into an integrated positive ( + ) dataset for training after homology reducing . limited by the available data , only methylated lysines and arginines
, we have implemented the prediction system in a web server , which is now available at : .
first , we obtained the dataset of methylation sites from the feature table of swiss - prot ( version 48 ) ( 27 ) .
we obtained 328 positive ( + ) sites , including lysines ( 148 items ) , arginines ( 76 items ) , histidines , asparagines and other kinds of residues ( supplementary table 1 ) .
methylation lysine and  methylation arginine for information on lysine and arginine methylation , respectively .
from 1700 scientific articles , we collected 107 and 264 experimentally verified methylation sites for lysine and arginine , respectively .
in addition , we combined the manually curated data and the data from swiss - prot into an integrated positive ( + ) dataset . as a result , only lysines ( 227 items ) and arginines ( 273 items ) had enough data entries to train and test the svm models .
in the present work we focused on methylated lysine and arginine residues , and did not take other amino acids into consideration . the positive ( + ) dataset for training might contain some homologous sites from homologous proteins . if the testing data were highly homologous to the training data , the prediction accuracy would be overestimated . to avoid the overestimation , we clustered the protein sequences from positive(+ ) dataset with a threshold of 30% identity by blastclust , one program in the blast package ( 28 ) .
if two proteins were similar with 30% identity , we re - aligned the proteins with bl2seq , another program in the blast package ( 28 ) , and checked the results manually .
if two methylation sites from the two proteins were at the same position in alignment , only one item was kept while the other one was discarded .
thus , we obtained positive ( + ) data of high quality with 156 lysine and 250 arginine - methylated sites . as described previously ( 2,24 ) , the negative (  ) sites were taken from non - annotated lysine / arginine sites in the same proteins from which ( + ) sites were chosen .
finally , memo was implemented in perl and hosted by apache running on a debian linux system .
the web server of memo ( version beta 1.0 ) has been available since jan , 2005 .
to mimic queries from biologist users , we have randomly submitted to memo three proteins , pbx4 ( q9byu1 ) , syntaxin 10 ( o60499 ) and sorting nexin ( snx)-17 ( q15036 ) from a large - scale experiment to identify the potential methylated proteins ( 20 ) , as examples to demonstrate the simplicity and accuracy of memo ( table 1 ) . with methyl - specific antibodies ,
there have been 200 putatively arginine - methylated proteins to be identified ( 20 ) .
however , the precise arginine methylation sites on these substrates still remain to be verified . from the list we have blindly taken three proteins ,
which have not been included in our training dataset , and predicted the potential arginine methylation sites in these proteins .
transcription factor pbx4 is a member of the pbx family that is implicated in a variety of developmental processes including axial patterning , hindbrain development and organogenesis ( 29,30 ) .
syntaxin 10 is a snare ( soluble n - ethylmaleimide sensitive factor attachment protein receptor ) protein . as a member of the syntaxin family , syntaxin 10 localizes to the trans - golgi network ( tgn ) and
play a potential role in regulating the expression profile of transferrin receptor ( tfr ) ( 31 ) .
snx17 is a member of the snx family that is involved in the sorting of transmembrane proteins ( 32 ) .
snx17 associates with ldl receptor - related protein ( lrp ) to modulate its cell surface levels ( 33 ) . in all three cases ,
we have employed the memo web tool to predict the arginine methylation sites on these proteins .
we have retrieved the protein sequences of the three proteins in fasta format , and pasted them in the input form of memo .
memo predicts r55 , r57 and r63 of pbx4 , r11 and r22 of syntaxin 10 and r339 , r341 , r399 and r442 of sorting nexin-17 as potentially positive hits ( table 2 ) .
interestingly , six of nine predicted sites are consistent with the methyl - specific antibody epitopes ( 20 ) .
the examples used here also shows that memo could be an important and useful computational tool for further experimental work .
our approach can be comparable with previous method ( 25 ) . more details about performance comparison
the known methylated protein residues ( supplementary table 1 ) are still far fewer than known phosphorylated residues ( 1,2 ) . in addition , the accuracies may also be affected by a lack of training data . however , as proteomic techniques improve , more and more methylation sites will be identified .
we can expect that the prediction systems could be expanded to other kinds of methylated residues besides arginine and lysine .
in addition , a powerful predictor of methylation sites in a methyltransferase family - specific fashion is also desirable .
moreover , some other machine learning methods could be applied , i.e. group - based prediction and scoring algorithm ( gps ) ( 2 ) , artificial neural networks and hidden markov models .
furthermore , evolutionary information , e.g. phylogenetic conservation between human and mouse ( 3 ) , could be integrated into the prediction system to improve its accuracy .
finally , the sequence patterns and structural specificities , which facilitate the binding between methylation sites and methyltransferases , remain to be dissected .
here we have developed a high - performance protein methylation predictor using the svms . due to the data limitation
we have implemented our method in an accessible web server , and expect that the memo may serve as a useful tool to experimentalists , who study protein methylation function and dynamics .
the predicted arginine methylation sites of pbx4 ( q9byu1 ) , syntaxin 10 ( o60499 ) and sorting nexin-17 ( q15036 ) with the methyl - specific antibody epitopes in these protein sequences memo predicts r55 , r57 and r63 of pbx4 , r11 and r22 of syntaxin 10 and r339 , r341 , r399 and r442 of sorting nexin-17 as potentially positive hits .
 protein methylation is an important and reversible post - translational modification of proteins ( ptms ) , which governs cellular dynamics and plasticity . 
 experimental identification of the methylation site is labor - intensive and often limited by the availability of reagents , such as methyl - specific antibodies and optimization of enzymatic reaction . 
 computational analysis may facilitate the identification of potential methylation sites with ease and provide insight for further experimentation . here 
 we present a novel protein methylation prediction web server named memo , protein methylation modification prediction , implemented in support vector machines ( svms ) . 
 our present analysis is primarily focused on methylation on lysine and arginine , two major protein methylation sites . 
 however , our computational platform can be easily extended into the analyses of other amino acids . 
 the accuracies for prediction of protein methylation on lysine and arginine have reached 67.1 and 86.7% , respectively . 
 thus , the memo system is a novel tool for predicting protein methylation and may prove useful in the study of protein methylation function and dynamics . 
 the memo web server is available at : . 
INTRODUCTION
IMPLEMENTATION
USAGE
DISCUSSION
CONCLUSIONS
SUPPLEMENTARY DATA
Figures and Tables