protein post - translational 
 modifications ( ptms ) modulate critical 
 biological processes such as protein signaling , localization , and 
 degradation and have been implicated in a wide variety of pathologies . 
 despite their importance
, the comprehensive identification and discovery 
 of ptms in complex biological samples has continued to pose a difficult 
 challenge for proteomics technologies .
we have recently described a global ptm ( g - ptm ) identification 
 strategy that enables the rapid and confident identification of numerous 
 ptm types in a single - pass database search .
identification 
 is accomplished by searching for the presence or absence of ptms exclusively 
 at curated sites designated in the uniprot repository . however , as 
 such lists of curated ptms are at present quite incomplete , the g - ptm 
 strategy necessarily misses many important ptms .
for example , hydroxyproline 
 is known to be a prevalent ptm in type i collagen , but 
 only four of many hydroxyproline sites in type i collagen are included 
 in the human uniprot database . furthermore , despite containing 470 
 ptm types as of may 2015 ( http://www.uniprot.org/docs/ptmlist ) , the uniprot database is still missing many additional novel ptms , 
 chemical derivatives , and sample - specific amino acid variants , which 
 thereby precludes the g - ptm strategy from identifying these modifications . to search for both known and unknown ptm types at once , several 
 unrestrictive ptm identification approaches , including wide mass tolerance 
 searches , have been devised .
however , these strategies suffer from limitations such as not being 
 readily applicable to large proteomic data sets , requiring the detection 
 of both modified and unmodified forms of a peptide in the sample or 
 sacrificing either sensitivity or confidence of modified peptide detection .
we describe here a global ptm discovery ( g - ptm - d ) strategy that 
 combines the ability to discover uncurated / unexpected modifications 
 offered by an unrestrictive approach , with the high confidence afforded 
 by the g - ptm approach for identifying curated ptms .
g - ptm - d searches 
 for modifications only at amino acid residue positions corresponding 
 to either curated ptms from the uniprot repository or potential modifications 
 discovered on specific peptides from an initial search using a wide 
 mass tolerance . limiting modifications to defined positions in this 
 manner enables the discovery of a large variety of ptms while maintaining 
 high confidence for all peptide and ptm identifications .
a ptm - curated database 
 in extensible markup language ( xml ) format 
 was first downloaded from http://www.uniprot.org / proteomes/. a perl script xml_trimming.pl 
 was first run to delete irrelevant information in the database and 
 retain only the sequence and ptm information to speed up the downstream 
 search .
the software program morpheus ( revision 149 ) ( freely available at http://cwenger.github.io/morpheus/ ) utilized the trimmed uniprot xml database along with data files 
 in either .raw or .mzml format . when an xml database is specified 
 in morpheus , all curated modifications are automatically extracted , 
 added to the variable modifications box , and selected . during the 
 search process
, all protein sequences are read , along with the locations 
 of selected uniprot variable modifications . the precursor mass tolerance 
 ( monoisotopic )
was set to either 1000 or 200 da for jurkat , 
 200 da for four common human cell lines , and 1000 da for 
 matrigel .
other settings were as follows : protease = trypsin ( no proline 
 rule ) ; maximum missed cleavages = 2 ; initiator methionine behavior 
 = variable ; fixed modifications = carbamidomethylation of c ; variable 
 modifications = oxidation of m , and others automatically selected 
 after adding the xml database ; maximum variable modification isoforms 
 per peptide = 1024 ; precursor monoisotopic peak correction = disabled ; 
 product mass tolerance =  0.01 da ( monoisotopic ) ; maximum false 
 discovery rate = 1% . the morpheus 
 output file called psms.tsv generated during stage 1 contains each 
 peptide spectral match ( psm ) with its associated attributes including 
 the actual precursor mass error ( m ) .
was created 
 and contained a list of the chosen modifications , including the name 
 of the modification type ( e.g. , phosphoserine ) , the modified amino 
 acid full name ( e.g. , serine ) , as well as the monoisotopic mass difference 
 ( e.g. , 79.966330 ) .
an aa_name_to_letter.txt file contained a list 
 of the 20 common amino acid full names ( e.g. , serine ) and corresponding 
 abbreviations ( e.g. , s ) .
the psms.tsv file , the two .txt files , and 
 the original xml database were all used as inputs to a perl script 
 xml_addopensearchresult.pl .
the perl script reads in each psm along 
 with its m . if the m of a peptide is within 0.02 
 da from a ptm type ( e.g. , phosphoserine , phosphothreonine , and phosphotyrosine ) 
 specified in the sub_ptmlist.txt file , it assigns this ptm type ( e.g. , 
 phosphorylation ) to every amino acid ( e.g. , serine , threonine , and 
 tyrosine ) in that particular peptide by writing these potential modification 
 identities and positions in the new xml database .
the .txt files , 
 perl scripts , morpheus software , and a user instruction document are 
 placed in supplementary software .
note 
 that this software package includes 11 regular modifications in the 
 sub_ptmlist_regular.txt to circumvent the manual construction and 
 examination of the m histogram and to automatically add potential 
 sites of these common modifications to the new xml database ( i.e. , 
 streamlined g - ptm - d workflow ) .
the output from running the perl 
 script described in stage 2 is a new xml file that contains not only 
 the uniprot curated ptms but also potential modifications identified 
 by the wide precursor tolerance search .
a second - round morpheus search 
 was performed with this new database , 10 ppm precursor mass 
 tolerance and 0.01 da product mass tolerance .
the matrigel spectra were first searched 
 against a protein database ( downloaded from uniprot in fasta format ) 
 with the pfind search engine .
carbamidomethylation 
 of cysteine was specified as a fixed modification and oxidation of 
 methionine as variable .
next , pmatch ( version 1.5 ) was used for library 
 construction from the identified spectra , followed by search of all 
 spectra against the library , with precursor mass tolerance of 500 
 da .
moda version 1.23 was used to search the 
 matrigel spectra with the following parameters : auto parent mass correction 
 enabled ; fragment ion mass tolerance = 0.01 da ; minimum / maximum modification 
 size = 200/+200 da ; enzyme = trypsin , kr / c ; fixed modification 
 = c , 57.0215 ; highresolution = on .
three separate data sets 
 with deep proteome 
 coverage were used to evaluate the performance of the g - ptm - d strategy . sample preparation 
 and ms analysis of human jurkat cells
the 28 ms raw files consisting 
 of 490  057 ms / ms scans are available via ftp from the peptideatlas 
 data repository by accessing the following 
 link : http://www.peptideatlas.org/pass/pass00215 .
the curated database was the homo sapiens reference 
 proteome from uniprot ( downloaded on december 23 , 2013 ) , limited to 
 those proteins with mrna transcript abundances exceeding 0.1 transcripts 
 per million .
this data set was used to 
 illustrate the g - ptm - d workflow in detail and to compare g - ptm - d with 
 a variable phosphorylation search and a g - ptm search .
sample preparation 
 and ms analyses of human hek293 , a549 , hela , and k562 cell lysates 
 were previously reported .
the curated 
 database was the homo sapiens reference proteome 
 from uniprot ( downloaded on may 15 , 2015 ) .
these data sets were analyzed 
 with the streamlined g - ptm - d workflow ( i.e. , automatically adding 
 11 regular modification types in the supplemented databases , without 
 construction of the m histogram ) and were used to demonstrate 
 the wide applicability of g - ptm - d and its improved performance compared 
 with the g - ptm strategy .
the ms raw files consisting of 107  162 ms / ms scans 
 from six peptide fractions are available via ftp from the peptideatlas 
 data repository by accessing the following link : http://www.peptideatlas.org/pass/pass00557 .
( the six raw files used are the ones with  matrigel01  
 in the file name . )
the curated database was the mus musculus reference proteome from uniprot ( downloaded on april 20 , 2015 ) . 

this data set was used to compare the performance of g - ptm - d with 
 pmatch and moda .
a 1% global 
 fdr at the psm 
 level was applied when reporting the results .
this means that the 
 ratio of the number of decoy psms to the number of target psms is 
 0.01 .
the fdr for the modified peptides is the ratio of the 
 number of modified decoy psms to the number of modified target psms 
 from the list of all psms meeting the 1% global fdr cutoff .
posterior error probability ( pep ) is a local false discovery rate 
 ( fdr ) representing the probability that individual peptides are false . 

the pep for the modified peptides at a certain score is the ratio 
 of the number of modified decoy psms to the number of modified target 
 psms among the psms that have a score within half of the score bin 
 size from that particular score ( e.g. , scores 8.5 to 9.5 were binned 
 and plotted as score 9 for figures 2c and 6c ) .
peptides that contain 
 only carbamidomethylation of cysteine or oxidation of methionine were 
 not considered as  modified  .
in the 
 g - ptm - d strategy , a protein database that contains both the protein 
 sequences and the curated ptms is employed for a first - round g - ptm 
 search using a wide ( e.g  1000 da ) precursor mass tolerance 
 ( figure 1 ) .
as previously 
 described , the g - ptm search approach differs 
 from the traditional variable modification search approach in that 
 it considers only previously curated ptms at specific amino acid residue 
 positions , evaluating the data for either the presence or absence 
 of the ptms at those specific residues .
the output file from this 
 first - round wide tolerance search contains each psm with its associated 
 attributes including the precursor mass error m ( i.e. , the 
 difference between the measured experimental mass of the peptide and 
 the theoretical mass of the highest scoring peptide from the database ) . 

a histogram of all m values from the entire search reveals 
 numerous peaks , which correspond to various modification types . for 
 example
, peptides with m corresponding to the + 79.966 da peak 
 in the m histogram are identified as having a probable phosphorylation . 
 for each of those peptides ,
a phosphorylation site is added to the 
 original database for each serine , threonine , and tyrosine in that 
 peptide .
this process is repeated for all of the peaks in the histogram 
 having a m readily attributable to a modification .
finally , 
 the modified database , containing both the uniprot - curated ptms of 
 the original search and the newly added potential modifications ( with 
 both identity and possible locations ) , is used to conduct a second - round 
 g - ptm search with the usual narrow precursor mass tolerance , resulting 
 in the identification of a myriad site - specific modifications .
g - ptm - d workflow , 
 illustrated with results from the jurkat cell 
 data set . an expanded view ( 160 da ) of the histogram of precursor 
 mass error ( m ) searched with 1000 da precursor mass tolerance 
 is displayed here ; the full histogram and a comparison to a 200 
 da search are shown in supplementary figure s1 .
the numbers of modified sites for each of the 27 identified modification 
 types are also displayed .
the performance of the g - ptm - d search strategy was evaluated 
 by 
 searching a deep proteomic data set obtained from human jurkat cells 
 ( experimental procedures ) .
the m histogram 
 shown in figure 1 is 
 from the g - ptm - d search of this data set with 1000 da precursor 
 tolerance .
dozens of peaks in the histogram rise up well above the 
 noise and are readily matched to the masses of known modifications . 

this first - round wide precursor mass tolerance search yielded 45  198 
 newly identified positions of potential modification , which were added 
 to the 22  550 curated ptm positions already present in the 
 original uniprot repository .
database 
 was then used for the second - round search , yielding 16  677 
 site - specific modifications , comprising 27 different types ( figure 1 ; supplementary table s1 ) .
in addition to modifications that 
 sometimes result from sample handling ( e.g. , deamidation ) and electrospray 
 ionization mass spectrometry ( e.g. , ammonia loss , water loss , metal 
 adducts ) , numerous biologically significant modifications were observed , 
 including ptms ( e.g. , phosphorylation , formylation , methylation , and 
 acetylation ) , cotranslational modifications ( e.g. , n - terminal acetylation ) , 
 and amino acid variants .
g - ptm - d can provide amino acid specificity 
 information for m 
 values that do not correspond to known modifications .
the process 
 for deriving the likely amino acid residue(s ) of modifications having 
 a certain m is as follows : the modification is assigned to 
 any amino acid in the peptides that have that m ; all such modifications 
 are incorporated into the new database , the second - round search with 
 regular mass tolerance is performed , and the occurrences of the modification 
 on different amino acids are examined ( supplementary notes ) .
the jurkat data set was further used to compare 
 performance of g - ptm - d with a variable phosphorylation search (  vphospho  ) , 
 and a 
g - ptm  search that only uses the curated ptm 
 information from uniprot .
compared with the other two searches , g - ptm - d 
 identified nearly triple the number of modified proteins and unique 
 peptides and increased the number of modified peptide psms by more 
 than 6-fold ( figure 2a ) .
( the overlap between the 7278 modified 
 spectra identified by g - ptm and the 45  687 modified spectra 
 identified by g - ptm - d is shown in supplementary figure s2a . )
notably , the fdr for modified peptides identified 
 by g - ptm - d was only 0.43% ( figure 2b ) , which is even below the global fdr of 1% ( for all 
 peptides , both modified and unmodified ) .
in contrast , the fdr for 
 phosphorylated peptides identified using the variable phosphorylation 
 strategy was much worse ( 11% ) .
pep values were calculated from the 
 numbers of target and decoy spectral matches having nearly the same 
 morpheus score ( experimental procedures ) . 

these pep values are plotted in figure 2c as a function of the morpheus score , the peptide spectrum matching score provided by the morpheus 
 search algorithm .
note that pep is a local fdr , representing the probability 
 that individual peptides with a given score are false . at the lowest morpheus score ( 9 ) that meets 
 1% global fdr ,
the probability is only 0.036 ( 3.6% ) that the modified 
 peptides from g - ptm - d are incorrect , whereas the probability is substantially 
 higher ( 60.1% ) for phosphorylated peptides from the variable modification 
 search . for the 6347 modified spectra identified by both g - ptm and 
 g - ptm - d ( supplementary figure s2a ) , the 
 pep values from g - ptm - d are generally smaller , indicating higher confidence 
 ( supplementary figure s2b ) .
pep as a function 
 of the ratio of matching products ( i.e. , the ratio of the number of 
 matching product ions to the number of all product ions in a ms / ms 
 spectrum ) also showed higher confidence for modified peptide identification 
 with g - ptm - d ( supplementary figure s3 ) .
results 
 from three types of searches of the jurkat cell data set : 
 a vphospho search ( using the uniprot fasta database with phosphorylation 
 as a variable modification ) , a g - ptm search ( using the ptm - curated 
 uniprot database ) , and a g - ptm - d search . ( a ) numbers of modified proteins , 
 unique peptides , and psms for each search .
the 45  687 modified 
 psms identified by g - ptm - d are shown in supplementary table s2 , with a hyperlink to the ms - viewer report for each 
 psm . ( b )
( c ) posterior 
 error probability ( pep ) for modified peptides as a function of the 
 morpheus score .
all three of these search strategies employ the 
 target - decoy approach 
 for calculations of fdr . in brief ,
decoy protein sequences are generated 
 on - the - fly by reversing the order of the amino acid residues ( unmodified 
 or modified ) for each protein sequence , and ptms move with their companion 
 amino acid .
we searched data sets from four additional human cell lines , hek293 , 
 a549 , hela , and k562 ( experimental procedures ) , to demonstrate the target - decoy approach with g - ptm - d .
the numbers 
 of unique peptide hits ( all or modified , target or decoy ) are plotted 
 against morpheus score in supplementary figure s4 .
the distribution of scores for target and modified - target 
 peptides is bimodal , with the lower group of scores overlapping with 
 the decoy peptide distributions , while the higher group of scores 
 primarily corresponds to target peptides .
a 1% fdr criterion corresponds 
 to a morpheus score of approximately 9 , which falls between the decoy 
 and target groups of scores .
certain types of two - pass searches 
 have been reported to show bias 
 for identification of modified peptides in the second pass search 
 and underestimate the fdr .
these artifacts emanate from changes to 
 the target and decoy databases between passes , and they are revealed 
 by large differences in the distributions of mass errors , search scores , 
 and expectation values between modified and unmodified peptide spectral 
 matches . to investigate this issue , we graphed three distributions 
 for modified and unmodified psms resulting from g - ptm - d searches ( supplementary figure s5 and supplementary notes )
. this allowed evaluation of the extent , 
 if any , of differences for these two groups , similar to the analysis 
 performed in figure 3 from everett et al .
we observed nearly 
 complete overlap in all of the plots of the distributions of values 
 ( mass error , morpheus score , and q - value ) for the unmodified and modified psms , indicating that the g - ptm - d strategy does not have bias 
 toward identification 
 of modified peptides and further that there is no difference between 
 the fdrs of unmodified and modified peptides .
this result is likely 
 due to the fact that all protein sequences used in the database for 
 the first - round search are retained in the second - round search .
the 
 search database is merely expanded to include potential modifications 
 on certain target peptides where high - scoring matches with ptm - characteristic 
 mass errors are observed in matches from the first - round search .
the 
 unmodified sequences remain the same in the second - round search and 
 serve as a control .
numbers of peptides with modifications identified by g - ptm 
 or g - ptm - d 
 for the four human cell lines .
 others  include trimethylation , 
 carboxylation , sulfation , water loss , ammonia loss , and deamidation .
the results from the four cell 
 lines were also examined to illustrate 
 the general improvement afforded by g - ptm - d for identification of 
 modified peptides . for these data , we implemented the streamlined 
 g - ptm - d workflow ( i.e. , automatically adding 11 regular modification 
 types in the supplemented databases , without manual construction of 
 the m histogram ) . on average ,
g - ptm - d revealed additional modifications , compared 
 with g - ptm searches ( figure 3 ) .
for methylation , dimethylation , and hydroxylation , which 
 are not as well studied or curated as phosphorylation and acetylation , 
 g - ptm - d provided a remarkable increase in the number of modified peptide 
 identifications .
these results suggest that modified peptides are 
 more common than previously recognized and that many peptides are 
 routinely missed or misassigned in proteomics experiments on unenriched 
 cell lysate samples where the database search algorithm does not consider 
 ptms .
apart from a remarkable improvement in the number of modified 
 peptide 
 identifications , g - ptm - d also delivered increased / better morpheus 
 scores than the g - ptm search . among the psms identified by g - ptm - d 
 with 1% fdr in the jurkat data
set , 15% of them had higher scores 
 for the g - ptm - d search compared with 
 the g - ptm search , while the other 85% had the same score ( figure 4 ) .
all of the psms 
 with increased morpheus scores were modified , and 98.7% of them were 
 reassigned to different base peptide sequences by the g - ptm - d strategy 
 compared with the g - ptm search .
these spectra would have been incorrectly 
 identified without the more complete list of modification types provided 
 by g - ptm - d . among the relatively few 
 spectra that were assigned the same base peptide but with increased 
 score ,
some were found to have the same type and number of modifications 
 but at different locations ( see example in figure 5 ) .
these results indicate that the curated 
 site information used in a g - ptm search does not always yield the 
 correct site and g - ptm - d may find a better match .
histogram of  
 morpheus score , the difference between the 
 morpheus score by g - ptm - d and the morpheus score by g - ptm , for all 
 the jurkat spectra that were identified by g - ptm - d with 1% fdr ( orange ) , 
 for those modified ( blue ) , or for those that were assigned to different 
 base peptide sequence in g - ptm and g - ptm - d ( gray ) .
a  morpheus 
 score of zero indicates no difference between the two types of searches . 

the positive  morpheus scores ( 15% of all assignments ) indicate 
 g - ptm - d found a better match , and all of these improved cases were 
 for modified spectra .
annotation of the same spectrum from the jurkat data set ( fraction 
 6 , spectrum number 18675 ) from ( a ) g - ptm identification , which includes 
 phosphorylation of serine 20 and ( b ) g - ptm - d identification , which 
 yields a much better match to fragment ions for this phosphorylation 
 of serine 4 .
note that 
 the g - ptm search employed the curated phosphorylation sites from uniprot , 
 which only included serine 20 for this peptide .
g - ptm - d , however , 
 was able to reassign this spectrum to phosphorylation of serine 4 , 
 which is likely the correct modification site , given the substantial 
 improvement in fragment ion matches .
the first - round search of the jurkat 28 
 peptide fractions ( 490  057 ms / ms scans ) with 1000 and 
 200 da precursor mass tolerances took 13 and 3 days , respectively , 
 on a dell precision workstation with intel xeon cpu , 2.70 ghz , and 
 a maximum of 24 threads .
the second - round search took only 1.4 h. 
 a precursor tolerance of 200 da is good enough to capture the 
 vast majority of the important and known modifications , and a total 
 analysis time of 3 days on nearly half a million ms / ms scans is reasonable , 
 considering the wealth of modification information acquired .
we searched 
 3 out of the 28 fractions with 1000 da precursor tolerance with 
 either proteome discoverer ( the search algorithm used by gygi et al . ) 
 or morpheus , with the same protein database in either fasta or xml 
 format .
the search times for morpheus ( 73 h ) and for proteome discoverer 
 ( 62 h ) were comparable . in cases where computation time / resources 
 are limited
, one could choose to perform a g - ptm search with normal 
 precursor mass tolerance and then use only the proteins identified 
 in the g - ptm search or reduce the  maximum variable modification 
 isoforms per peptide  in the morpheus graphical user interface 
 to perform the wide precursor tolerance search .
another option for 
 saving time on repeated analyses of similar types of samples is to 
 only perform the wide - tolerance search on a representative subset 
 of the samples .
then , one could use the xml database of modified peptides 
 generated from this set for narrow - tolerance g - ptm type searches of 
 all other similar samples .
we have also modified the source code of 
 the morpheus search engine so that it is able to accommodate discrete 
 precursor mass tolerance windows that correspond to known modifications 
 to significantly reduce the search time for users who are interested 
 only in certain modification types .
performing 
 the second - round search with narrow precursor mass tolerance and evaluating 
 the data for either the presence or absence of the modifications only 
 at specific locations is crucial to the confident spectral identification 
 afforded by g - ptm - d compared with other wide precursor mass tolerance 
 search strategies . in the jurkat cell data set , 
 25% ( 62  289 ) of the
249  223 psms identified at 1% fdr 
 in the second - round g - ptm - d search had not been identified in the 
 first - round wide - tolerance search .
psms ( 44  530 ) corresponded to unmodified peptides , consistent 
 with the 20% loss of unmodified psms reported by gygi and 
 coworkers in their wide - tolerance searches .
the balance of the  rescued  psms was composed of 
 17  759 psms for modified peptides . for example , one spectrum 
 was matched to a decoy ( false ) peptide during the 
 first - round search with a m of + 8.9263 da , but in the second - round 
 search , it was identified as a phosphorylated peptide from the chromosome 
 alignment - maintaining phosphoprotein 1 with a m of 0.0029 
 da ( annotated spectrum in supplementary figure s6 ) . thus the narrow - tolerance second - round search of g - ptm - d 
 rescues both unmodified and modified peptides , and it even corrects 
 some assignment errors that are introduced by the wide - tolerance search . finally , we compared the performance of g - ptm - d with two other 
 unrestrictive modification search tools , pmatch and moda .
this comparison was performed on an alternative 
 and simpler data set obtained from a matrigel sample ( experimental procedures ) to demonstrate the applicability 
 of g - ptm - d to different data sets and also to limit the cpu hours 
 needed for running pmatch and moda .
pmatch is based on an open ms / ms 
 spectral library search , and moda uses multiple sequence tags and 
 a dynamic programming spectral alignment algorithm . at 1% fdr
, g - ptm - d 
 identifies almost double and triple the number of psms compared with 
 pmatch and moda , respectively , for both modified and unmodified peptides 
 ( figure 6a ) .
the fdr and pep for modified psms identified by 
 g - ptm - d were also smaller than those for pmatch and moda ( figures 6b , c ) , indicating 
 higher confidence by g - ptm - d .
in addition , g - ptm - d detected more modification 
 types compared with pmatch and moda ( supplementary table s3 ) .
the modification masses in moda are in 1 da intervals , 
 limiting its ability to distinguish different modifications with close 
 mass shifts .
because pmatch uses a spectral library constructed from 
 identified spectra , instead of the entire database , to perform the 
 wide - tolerance search , its search speed is much faster than that of 
 g - ptm - d .
however , the use of the spectral library prevents the detection 
 of modified peptides when the unmodified forms are not identified 
 in advance .
results from searches of the matrigel data set using pmatch , moda , 
 and g - ptm - d .
( c ) posterior error 
 probability ( pep ) for modified peptides as a function of score .
note 
 that the pep values were plotted versus normalized scores to account 
 for the different score scales of morpheus , pmatch , and moda ( scores 
 were normalized to a scale from 1 to 13 ) . all results in this figure 
 met a 1% global fdr .
g - ptm - d offers a number of additional advantages compared 
 with 
 other ptm identification software tools .
the morpheus algorithm employed 
 with g - ptm - d accepts up to ten modifications per peptide , while the 
 maximum number we detected in all the data sets was seven .
thus the 
 search algorithm does not effectively limit the identification of 
 multiply modified peptides .
the fact that g - ptm - d can detect peptides 
 that contain more than one modification extends its analytical capability , 
 especially in cases where different modification types coexist within 
 a single peptide .
in addition , most other software tools ( such as 
 ms - alignment , modificomb , moda , peaksptm , deltamt , and pmatch ) require the coexistence of the modified and 
 unmodified forms of a peptide in the sample , while g - ptm - d does not . 

furthermore , modificomb and deltamt can not detect modifications when 
 the modified and unmodified forms are offline separated into different 
 fractions , while g - ptm - d is able to combine any number of fractions 
 in the same search . in summary ,
the g - ptm - d search strategy 
 is able to reveal a wide 
 variety of site - specific modifications with high confidence in deep 
 proteomic data sets from unenriched samples .
it achieves this by searching 
 for the presence or absence of modifications only at either already 
 curated or at potential new sites discovered in a wide tolerance search . 

this search strategy greatly reduces the search space compared with 
 conventional ptm variable searches and provides increased confidence 
 in the identification of modified peptides .
importantly , no pre - enrichment 
 of samples for particular ptm types is required , thus providing a 
 broad and unbiased view of a wide range of modifications .
g - ptm - d 
 provides a powerful new tool for the identification and discovery 
 of protein variation .
 a new 
 global post - translational modification ( ptm ) discovery strategy , 
 g - ptm - d , is described . 
 a proteomics database containing uniprot - curated 
 ptm information is supplemented with potential new modification types 
 and sites discovered from a first - round search of mass spectrometry 
 data with ultrawide precursor mass tolerance . a second - round search 
 employing the supplemented database conducted with standard narrow 
 mass tolerances yields deep coverage and a rich variety of peptide 
 modifications with high confidence in complex unenriched samples . 
 
 the g - ptm - d strategy represents a major advance to the previously 
 reported g - ptm strategy and provides a powerful new capability to 
 the proteomics research community . 
Introduction
Experimental
Procedures
Results and Discussion