many proteomics experiments are performed 
 on the peptide level 
 ( namely , bottom - up proteomics ) . for this purpose ,
the proteome is 
 enzymatically digested , and the peptide and peptide fragment masses 
 are measured by tandem mass spectrometry after online separation by 
 reverse - phase liquid chromatography ( lc  ms / ms ) . for the analysis 
 of lc 
ms / ms data , peptides are statistically scored by search 
 engines such as mascot , sequest , omssa , maxquant , x!tandem , ms - gf+ , ms amanda , myrimatch , or comet .
all of these tools compare input peptide ms / ms 
 spectra to theoretical spectra generated by the in silico digest of 
 protein databases .
the peptides can , dependent on the settings of 
 the search engine , contain missed cleavage sites and variable or fixed 
 amino acid modifications .
all of the in silico generated spectra constitute 
 the so - called search space ( i.e. , spectra of all peptides and variants 
 thereof that are considered a potential match to the input spectrum ) .
the search engine settings and database used 
 are crucial for the quality of the results obtained because discrepancies 
 between biological sample content and peptides present in the search 
 space can lead to several undesirable effects . for the statistical 
 evaluation of the results , in most cases , false discovery rates ( fdr ) 
 are calculated .
fdr is defined as the ratio of false positives to 
 the total number of assignments ( false positives + true positives ) 
 and is often estimated with the help of decoy databases ( i.e. , randomized 
 or reversed databases ) .
usually , 
 most of the proteins not contained in the target database 
 are sample contaminants from different species , either introduced 
 by accident or as a reagent during sample preparation .
the former 
 can be reduced by careful sample handling , and among the best known 
 examples is human keratin .
the latter type of common contaminants 
 in proteomics experiments includes proteases , which are added to the 
 sample to digest proteins to the more readily analyzed peptides .
the most widely used protease is trypsin due to its specificity , 
 efficiency , availability , and relatively low cost .
trypsin cleaves 
 the amino acid sequences c - terminal to lysine and arginine 
 residues ( with the exception of the occurrence of n - terminal proline ) 
 and gives rise to peptides of suitable length and charge for lc 
however , trypsin is also subject 
 to autolysis because it contains 11 lysine and 4 arginine residues . because trypsin is used in high quantities to 
 promote efficient digestion ( trypsin to sample protein ratio : 1:201:100 ) , 
 trypsin - derived peptides are highly abundant and result in high - quality 
 spectra during ms / ms . in the case where the trypsin sequence is not 
 present in the database
it is therefore widely accepted in 
 the community to concatenate a contaminant list that includes trypsin 
 with sample specific databases for the reasons given above .
the groups 
 of ron beavis ( http://www.thegpm.org/crap/ ) and matthias 
 mann ( http://www.maxquant.org/downloads.htm ) make their 
 list of contaminants publicly available .
although the database should 
 cover all possible components of a sample , care should be taken not 
 to choose a too - large database ( e.g. , the entire uniprot trembl database ) 
 because large databases not only increase the search time but also , 
 more importantly , increase the number of random peptide hits .
for the prevention of the autolysis of 
 trypsin and the associated 
 loss of activity , the protease is often 
 modified by acetylation ( new england biolabs ) or reductive methylation 
 ( promega , sigma - aldrich ) of lysines , the latter being the most common 
 approach .
both modifications yield a trypsin that is more resistant 
 to autolysis while retaining its catalytic function . here
, we show that the most widely employed trypsin 
 preparations 
 are chemically modified and that including native trypsin in the contaminant 
 database is not sufficient to prevent false - positive identifications 
 of trypsin - derived spectra . instead , it is necessary to add modified 
 trypsin to the list of contaminants .
we present a new database search 
 strategy with a minimal increase in search space and the concomitant 
 loss of statistical significance .
finally , this approach can be easily 
 implemented into existing workflows for many widely used search engines .
the list of common repository of adventitious proteins ( crap ) 
 was downloaded from ftp://ftp.thegpm.org/fasta/crap ( version : 
 january 30 , 2015 ) .
the protein identifiers were then uploaded to uniprot 
 retrieve / id mapping tool to generate a fasta file in uniprotkb format 
 ( february 2 , 2015 ) .
databases from 
 search strategies a  e were reversed using the compomics dbtoolkit 
 4.2.3 .
swiss - prot_human or swiss - prot_yeast 
 was used as the database ; carbamidomethylation of cysteines was set 
 as a fixed modification .
oxidation of methionine and protein n - terminal 
 acetylation were set as variable modifications .
swiss - prot_human or swiss - prot_yeast 
 with concatenated crap was used as the database .
oxidation of methionine 
 and protein n - terminal acetylation were set as variable modifications .
swiss - prot_human or swiss - prot_yeast 
 with concatenated crap was used as the database .
oxidation of methionine 
 and protein n - terminal acetylation were set as variable modifications . 
 for data set 2 , acetylation of lysine
was allowed as an additional 
 variable modification ; for all other data sets , dimethylation of lysine 
 was allowed as an additional variable modification .
swiss - prot_human or swiss - prot_yeast 
 with concatenated crap was used as the database .
oxidation of methionine 
 and protein n - terminal acetylation were set as variable modifications . 

swiss - prot_human 
 or swiss - prot_yeast 
 with concatenated crap was used as the database .
furthermore , modified 
 trypsin was added to the database ( where all lysines ( k ) were replaced 
 by dimethylated lysines ( j ) to detect dimethylated lysines in trypsin ) . 

oxidation of methionine , protein n - terminal acetylation , loss of one 
 methylation of dimethylated lysines ( j ) ( to detect monomethylated 
 lysines in trypsin ) , and loss of dimethylation of dimethylated lysines 
 ( j ) ( to detect unmethylated lysines in trypsin ) were set as variable 
 modifications .
data set 1 : saccharomyces 
 cerevisiae ; pxd002726 data set 2 : phosphorylation 
 sites of numb ( human cell line ) with mouse recombinant protein numb ; 
 pxd000997 data set 3 : human breast 
 cancer study ; pxd000246 data set 
 4 : identified spectra from 209 public human data sets 
 from the pride repository ( see supporting table s4 ) data set 5 : human brain 611 reference map ; 
 prd000151 database search was performed using mascot 
 ver .
2.4.1 or ms amanda standalone_windows_1.0.0.3948 customized to 
 contain dimethylated lysine j = 156.125710 ( kindly provided by viktoria 
 dorfer ) .
data were searched against human and s. cerevisiae swiss - prot database as specified in the database section . for fdr 
 estimation , a target 
peptides 
 mass tolerance was set to 10 ppm ( or 100 ppm for data set 5 ) and fragment 
 mass tolerance to 0.8 da .
carbamidomethylation of cysteine was set as a fixed modification , 
 and oxidation of methionine was set as a variable modification . to 
 detect peptides containing peptide c - terminal artificial amino acid 
 j , we changed the cleavage specificity for trypsin to j , k , and r , 
 not after p. furthermore , to detect mixed ( i.e. , peptides including 
 methylated and unmodified lysine ) and monomethylated peptides , we 
 added the loss of monomethylation ( 14.0153759 da ) and loss of dimethylation 
 ( 28.0307517 da ) of the artificial amino acid ( j ) as variable modifications .
data set 4 was searched using x!tandem ( version :  piledriver  ( 2015.04.01.1 ) ) . because x!tandem 
 restricts the amino acids that may be redefined ,  o  
 was used to represent dimethylated lysine .
the mass of o was set to 
 156.12571 using the  protein , modified residue mass file
carbamidomethylation of cysteine was set as a fixed modification , 
 and oxidation of methionine , n - terminal acetylation and a loss of 
 28.0307517 da on  o representing loss of dimethylation 
 and a loss of 14.0153759 da on 
parent - mass error was 
 set to 2 da , and fragment mass error was set to 0.4 da .
a total of 
 two missed cleavages were allowed , the specificity of trypsin was 
 adapted as described before , and refinement mode was disabled .
fdr for separate 
 target  decoy searches ( data set 3 ) was calculated for mascot ion scores as 
 fdr being equal to the number of decoy hits divided by the number 
 of target hits , with score increments of 0.01 until fdr was smaller 
 than 1% .
log(2 ) 
 score distribution for separate decoy and target database 
 searches of data set 3 using different search strategies . left panel : 
 decoy database searches ; right panel : target database searches .
a : 
 search strategy a , swiss - prot_human ; b : search strategy b , swiss - prot_human 
 plus list of common contaminants ; c : search strategy c , swiss - prot_human 
 plus common contaminants and additional variable modification dimethyl 
 ( k ) ; d : search strategy d , swiss - prot_human plus common contaminants 
 and additional variable modifications methyl ( k ) and dimethyl ( k ) . 

e : search strategy e , swiss - prot_human plus common contaminants and 
 methylated lysines of trypsin considered as artificial amino acids . 
 marked in black
are peptides that pass the fdr 1% cutoff ( mascot ion 
 score of 20.50 for searches a , b , and e , 20.94 for search c , and 22.21 
 for search d ) . *
gel bands were 
 cut out and destained using 50% acetonitrile in 100 mm ammonium bicarbonate . 

after dehydration with 100% acetonitrile , gel pieces were dried and 
 then reduced with 10 mm dithiothreitol for 30 min at 56 c and 
 subsequently alkylated with 55 mm iodoacetamide for 20 min at 37 c 
 in the dark .
gel pieces were dehydrated and dried again and then incubated 
 with 0.19 g of sequencing - grade modified trypsin ( promega , 
 v5111 ) overnight .
peptides were extracted in four sequential steps : 
 15 l of 25 mm ammonium bicarbonate , 150 l of acetonitrile , 
 40 l of 5% formic acid , and 150 l of acetonitrile .
the dried peptide extracts were 
 resuspended in 5% acetonitrile and 0.1% trifluoroacetic acid , and 
 peptides were separated on an acclaim pepmap rslc c18 , 2 m , 
 100  , 75 m i.d .
 50 cm column employing a dionex 
 ultimate 3000 rslc coupled to a ltq - orbitrap velos ( thermo fisher 
 scientific ) .
data - dependent 
 top 10 cid ms and ms / ms data were acquired in the orbitrap analyzer 
 at a resolution of 60  000 and in the ion trap employing normal 
 scan rate settings , respectively .
the mass spectrometry proteomics 
 data was deposited to the proteomexchange consortium ( http://proteomecentral.proteomexchange.org ) 
 via the pride partner repository with the data set identifier pxd002726 
 and 10.6019/pxd002726 .
despite the above - mentioned chemical modifications , 
 peptides originating from trypsin are among the most commonly high - scoring 
 identified peptides in bottom up searches .
this can be seen when looking 
 at the pride cluster resource , where 
 many of the largest clusters containing several thousand spectra represent 
 peptides from trypsin ( e.g. ,
these unmodified peptides are the fraction 
 of peptides that were not modified during reductive dimethylation , 
 which could be due to the native state of trypsin during the modification . 
 assuming that the majority of accessible lysines are methylated but 
 still many cleavage events occur , it is reasonable to expect peptides 
 containing methylated lysines upstream of the unmodified lysine at 
 the cleavage site .
we therefore reanalyzed several of our own 
 data sets as well as publicly deposited data sets to assess the presence 
 of methylated peptides and the impact of different search strategies . 

the first data set we examined 
 was a set of in gel digests from s. cerevisiae prepared in our laboratory ( data set 1 , pxd002726 ) , which was analyzed 
 with different search strategies ( table 1 ) .
initially , we searched only against the 
 swiss - prot_yeast database without accounting for potential contaminants 
 ( search strategy a ) .
second , we included the list of common contaminants 
 ( search strategy b ) . and third , to find dimethylated trypsin , we allowed 
 dimethylation of lysine as a variable modification ( search strategy 
 c ) . search c identified porcine trypsin with a higher score than search 
 b because more peptides could be identified .
we also identified numerous 
 peptides with dimethylated lysines at their c - termini , challenging 
 the assumption of complete inhibition of tryptic activity at the c - terminal 
 of dimethylated lysines ( figure 1 ) .
moreover , we performed a search with the additional 
 variable modification monomethylation of lysine ( search strategy d ) 
 to check for incomplete conversion during chemical stabilization . 

indeed , peptides harboring monomethylated lysines were identified , 
 being a fraction of roughly 30% of total methylated lysines ( supporting table s1 ) . when analyzing a trypsin 
 self - digest
, we could identify the same peptides , confirming our assumption 
 that the methylated peptides indeed originated from trypsin ( supporting table s2 ) . using a similar approach , 
 we also found acetylated peptides from trypsin in a human epithelial 
 cell data set ( data set 2 , pxd000997 ) 
 ( supporting table s3 ) .
the yeast data 
 set ( data set 1 ) 
 was searched against swiss - prot_yeast ( search strategy a ) or swiss - prot_yeast 
 plus common repository of adventitious proteins ( search strategy b ) 
 to identify yeast and known contaminant proteins from our laboratory . 

carbamidomethyl ( c ) as a fixed modification and oxidation ( m ) as a 
 variable modification were set for all searches .
dimethylation ( k ) 
 was set as a variable modification in the search strategy c , and both 
 monomethylation ( k ) and dimethylation ( k ) were set as variable modifications 
 in search strategy d. shown are the highest scoring proteins for each 
 search strategy .
methylated peptides of trypsin ( highlighted in bold ) can indeed be found in samples digested with modified trypsin when 
 the appropriate search settings are used ( searches c and d ; see figure 1 and supporting table s1 ) , making it the top - scoring 
 protein hit in this in - gel digest .
blue : sequence coverage of 
 peptides identified in data set 1 by search c. peptides are listed 
 in supporting table s1 .
. 
 other colors : methylated lysine . in 
 searches c and d ( by allowing variable modifications of lysine ) , 
 we identified both methylated lysines and unmodified lysines in trypsin - derived 
 peptides .
although the strategy led to an increased score of porcine 
 trypsin ( table 1 ) , 
 this approach has a severe drawback .
the most severe 
 is the impact on result statistics because more random matches will 
 occur when all possible permutations are tested .
enlargement of the 
 database leads to a rise of false - positive hits , increasing both the 
 number of false positives but also false negatives when applying the 
 same fdr threshold .
however , ignoring 
 the modified peptides can have the detrimental effect of leading to 
 the identification of false positives . to circumvent this
, we 
 devised a method that considers the chemically modified tryptic autolysis 
 peptides without unnecessarily inflating search space .
we envisaged 
 introducing dimethylated lysine as an artificial amino acid into the 
 database to be able to limit its occurrence to the actually modified 
 protein in the database .
mascot allows the easy introduction of artificial 
 amino acids via the unimod.xml file and the two capital letters j 
 and o for user - defined amino acids .
we therefore introduced the artificial 
 amino acid j , having a total monoisotopic mass of 156.125710 da ( j 
 = k + dimethyl ( + c2h4 , + 28.0313 ) ) .
for a copy 
 of the porcine trypsin entry , we then replaced all lysines ( k ) with 
 dimethylated lysines ( j ) ( fictional accession number 012345 , try_art ) 
 and added it to the database including the contaminants .
in addition , 
 the cleavage specificity of trypsin was set to the c - terminal of k , 
 r , and j ( except when followed by p ) . using the database including 
 artificial trypsin and the search settings explained above
, we successfully 
 identified peptides of trypsin containing dimethylated and unmodified 
 lysines in data set 1 . to identify mixed peptides , including both 
 dimethylated and unmodified lysines within one peptide
, we chose a 
 rather counterintuitive approach : we included the loss of dimethylation 
 of dimethylated lysines as a variable modification .
this approach 
 limits the increase of search space to the smallest degree necessary 
 ( i.e. , only peptides that contain j are multiplied while still covering 
 all possible combinations of dimethylated and unmethylated lysines ) . 

the same approach can also be used to identify monomethylated lysines 
 by allowing the loss of one methyl group for j ( because j minus methyl 
 is equal to k plus methyl ) .
in contrast to the addition of two global 
 variable modifications , we could not observe any impact on search 
 specificity with this strategy ( search strategy e ) , as is detailed 
 in the next section .
we next 
 assessed the influence of the different search parameters on peptide 
 spectrum matches ( psms ) , peptide scores , and protein scores using 
 five raw files from a human breast - cancer data set ( data set 3 , pxd000246 ) .
search strategy a identified a lower number of peptides 
 because 
 it was unable to assign spectra to contaminants .
search strategy b , 
 representing the community standard to employ a contaminant database 
 without considering modifications of trypsin , yielded a higher number 
 of total psms as well as more psms after applying a 1% fdr cutoff . 
 to visualize the impact of the increased search space in search strategies 
 c and d , we depict histograms of mascot ion scores of a representative 
 raw file ( site1_cell_line_erneg_her2neg_pool1_1d_rep1.raw ) from data 
 set 3 in figure 2 . 

it is clearly visible that both the decoy and the target populations 
 contain more hits when employing search strategies c and d. less obviously , 
 the mean of the log(2 ) decoy scores is shifted to slightly higher 
 values ( 1.47 ( a , b , and e ) versus 1.64 ( c ) ) .
these effects are further 
 amplified when adding another variable modification ( search strategy 
 d ; figure 2 ; mean 1.95 ) . 
 because of the bimodal distribution of the target populations even 
 after log(2 ) transformations , the kruskal 
wallis test was employed 
 for statistical testing and showed highly significant changes ( p < 10 ) in both score distributions 
 for search strategies c and d. calculating the 1% fdr score threshold 
 for the target  decoy pairs , search strategies a , b , and e required 
 an ion score of 20.50 to pass the cutoff , while this threshold was 
 increased to 20.94 in search strategy c and 22.21 in search strategy 
 d. although search strategies c and d lead to the highest number of 
 total psms , the number of psms passing the 1% fdr threshold decreases 
 significantly ( see figure 2 , supporting figure s1 ) .
naturally , 
 this also reduces the average protein score ( average protein scores 
 were 262.0 , 262.3 , 256,6 , 252,5 and 262.7 for searches a , b , c , d , 
 and e , respectively , with only the scores from search c and d being 
 significantly lower than the average scores from all other searches ) 
 ( figure s1 ) . in summary , the use of global 
 variable modifications leads to more total psms but at the expense 
 of specificity , as can be seen after applying 1% fdr .
the use 
 of the artificial amino acid based search strategy e did not yield 
 a statistically significant improvement over search strategy b in 
 either total psms or psms after applying 1% fdr .
however , individual 
 spectra were incorrectly assigned to unrelated proteins by search 
 strategy b rather than modified trypsin , as revealed by search strategy 
 e. this is depicted in figure 3 , showing which fractions of false positives of search strategy 
 a can be explained by the common contaminant approach ( i.e. , search 
 strategy b ( 23% ) and the artificial amino acid search strategy e ( additional 
 7% , total 30% ) ) . to rule out search - engine - specific effects , we processed 
 the same data set with a customized version of ms amanda , a scoring algorithm especially suited for data 
 sets with high - resolution ms / ms spectra . employing search strategy 
 b
however , with 
 both search engines , search strategy e resulted in much higher probabilities 
 for the dimethylated trypsin derived peptide to be the correct identification , 
 as shown for one example spectrum in figure 4 .
fractions of false positives explained by different 
 search strategies 
 ( data set 3 ) .
search strategy b reveals that 23% of all false positives 
 from search strategy a are caused by common - contaminant - derived peptides 
 ( including unmodified trypsin ) .
search strategy e identifies an additional 
 7% in this data set ( overall range 066% and mean 6.6% in all 
 human pride data sets ( supporting table s4 ) ) , which are exclusively caused by methylated trypsin peptides . individual spectrum misassigned by two separate 
 search engines 
 despite including contaminants in the database ( data set 3 ) .
* indicates 
 that the accession number for dimethylated trypsin can be set at discretion , 
 avoiding occupied accession numbers .
depicted in figure 4 is an exemplary case , which may easily lead to identifications passing 
 the 1% fdr threshold and subsequently increases the chance of wrong 
 protein inference using search strategy b. we want to emphasize that 
 search b was done using a database of appropriate size ( swiss - prot_human ) 
 and included common contaminants .
this widely used approach results 
 in statistically valid but still incorrect assignments , which were 
 revealed to actually represent dimethylated trypsin derived peptides 
 with a very high probability , employing search strategy e. to assess 
 the prevalence of these incorrectly assigned spectra , we reprocessed 
 the identified spectra from 209 public human data sets ( data set 4 ) 
 stored in the pride database ( see supporting table s4 ) .
the extracted identified 
 spectra were identified using x!tandem and the new artificial amino 
 acid based search strategy ( search strategy e ) . we were able to identify 
 methylated trypsin peptides in 38 out of these 209 experiments . for 
 the 171 experiments in which we could not identify methylated trypsin , 
 it is unclear whether unmodified trypsin was used .
it has to be noted 
 that most publications only specify the trypsin vendor , and most vendors 
 offer different types of trypsin preparations .
the number of 
 spectra identified as dimethylated trypsin peptides ranged from 1 
 to 600 per project ( see supporting table s4 ) .
we want to point out that these spectra constitute a substantial 
 amount of all false - positive peptides in community gold standard search 
 strategy b. at an fdr of 1% , the spectra reassigned by search strategy 
 e to methylated peptides originating from trypsin correspond to up 
 to 66% of all false positive hits
the distribution of unmodified to monomethylated to 
 dimethylated trypsin derived peptides was on average 25:35:40% .
it 
 is also important to highlight that we only reanalyzed the submitted , 
 identified spectra because only these may have led to incorrect identifications . 
 because of
the way data is submitted to pride , it is often not possible 
 to know whether all identifications were submitted or only the ones 
 passing the fdr threshold applied by the authors .
nevertheless , methylated 
 trypsin peptides passing 1% fdr of the cluster search do occur in 
 a considerable number of public data sets and thus lead to incorrect 
 identifications . in total , 3960 modified trypsin peptides were identified 
 from wrongly assigned spectra ( listed in table 
 s4 ) .
one interesting example was found in data set 5 from 
 human brain 
 tissue ( prd000151 ) .
when we first reanalyzed 
 this sample set with search strategy b , we were unable to reproduce 
 the original assignment of a spectrum to human trypsin isoform 2 . 
 comparing the search settings , we found that the original publication 
 used a higher parent - mass tolerance , namely 100 ppm for orbitrap data . 
 with the same parent - mass tolerance , we were also able to identify 
 the peptide from human trypsin isoform 2 , with a  parent mass 
 of approximately 25 mmu .
search e yielded a peptide from dimethylated 
 porcine trypsin and from human trypsin isoform 2 with almost identical 
 scores for both mascot and ms amanda searches ( see figure 5 ) . comparing the sequences 
 of the two peptides , we found that the human isoform contains an arginine 
 instead of the lysine at the same position in the porcine trypsin . 

interestingly , arginine and dimethylated lysine have a mass difference 
 of 24.6 mmu , almost perfectly fitting the mass difference of expected 
 to measured mass for the human isoform 2 peptide . because of the expected 
 instrument accuracy and the biological origin of the sample ( brain ) , 
 we are inclined to propose the dimethylated peptide ( with mass difference 
 1 ppm ) as the correct match .
a precursor mass  of 25 mmu as 
 for the initial match might exclude a peptide from the list of potential 
 candidates in search strategies in which stringent precursor mass 
 filtering is applied before peptide matching .
however , when using 
 wide parent - mass windows for the initial search , as suggested recently 
 by bonzon - kulichenko et al . for database - dependent scoring functions 
 ( e.g. ,
mascot expect value ) , other matches 
 can actually outscore and outrank the correct match ( as shown in figure 5 ) .
it therefore has 
 to be ensured to include all potential matches irrespective of score 
 and rank until applying post - filtering according to precursor mass 
 when using this approach .
peptide originating from dimethylated trypsin 
 assigned to human 
 trypsin isoform 2 ( data set 5 ) .
even though the score for dimethylated 
 trypsin is lower in the mascot search , the mass difference and the 
 biological origin of the sample implicate that the dimethylated trypsin 
 is the correct assignment .
analysis with ms amanda showed identical 
 scores and probabilities for both dimethylated porcine trypsin and 
 human trypsin isoform 2 .
methylation of trypsin is employed to protect 
 against autolysis 
 and concomitant enzymatic activity loss . as expected ,
our results show that the enzyme is not completely 
 modified because the modification of the natively folded protease 
 can only take place at lysine residues accessible to the reagent . 

more interestingly , we also show that methylation does not completely 
 protect against autolysis . in many of the analyzed samples
, we identified 
 peptides of trypsin that were actually methylated directly at the cleavage 
 site .
the use of appropriate databases in proteomics is crucial 
 for identifying 
 correct peptides and avoiding false - positive peptide hits that might 
 result in inferring wrong proteins .
even 
 if common contaminants are included in the database , methylated trypsin 
 derived peptides are not considered in the search space , and the presence 
 of these modified peptides leads to false - positive peptide identifications . 
 unfortunately , the most intuitive solution , to consider methylated 
 lysines in the search ( i.e. , adding variable modifications ) , has been 
 reported to result in reduced specificity of a search .
while this approach is able to efficiently reduce 
 the number of unassigned spectra , it also increases the peptide score 
 threshold at a constant fdr because of the considerably larger search 
 space , as demonstrated in this study .
peptides filtered out due to 
 the now more stringent score cutoff also can not add to protein scores , 
 which results in a lower average protein score ( supporting figure s1 ) .
the same effect can be observed 
 whenever considering variable modifications , 
 which might occur naturally or during sample preparation ( e.g. , phosphorylations , 
 carbamidomethylation artifacts , and nontryptic cleavages ) .
therefore , 
 one has to carefully balance the beneficial effect of additionally 
 assigned spectra to the detrimental effects of increased search space 
 on a sample specific basis .
this is depicted in figure 2 , where either two or three global variable 
 modifications plus one variable protein n - terminal modification are 
 used .
additional variable modifications lead to a further combinatorial 
 expansion of search space , reducing search specificity and resulting 
 in a net loss of statistically valid peptide hits .
the same is true 
 to an even bigger extent when allowing for semi- or nonspecific enzymatic 
 cleavages ( supporting figure s1 ) .
thus , we suggest including the modification of the employed digestion 
 enzymes into the contaminant database as an artificial amino acid . 
 this approach does not have significant drawbacks regarding the search 
 space , as demonstrated in this study , and can be used alongside other 
 sample - specific search parameters without a loss of search specificity . 

several search engines such as mascot , x!tandem , and comet ( see box 1 ) allow for the easy 
 introduction of non - natural amino acid , but for some , all amino acid 
 letters and masses are hard - coded
. examples for the latter are the 
 ( discontinued ) omssa engine , andromeda ( maxquant ) , sequest , and ms 
 amanda .
sequest , for example , does in our hands not accept additional 
 amino acids defined in the unimod.xml , and maxquant in its current 
 version has no option to add non - natural amino acids .
in our opinion , 
 it would be useful to either include a special letter for the omnipresent 
 case of dimethylated lysine from trypsin in each search engine or 
 to make the amino acid code customizable by the user .
adaption of 
 the employed contaminant databases to include an additional trypsin 
 copy in which lysines are replaced by the letter of choice is usually 
 trivial .
however , care has to be taken that the letter is not used 
 as an ambiguity symbol or for special amino acids in the employed 
 database ( e.g. , j is used for i / l in ncbinr ) .
in general , it might 
 be worth considering increasing the namespace for amino acids to be 
 more flexible when searching for nonstandard amino acids . at the moment , 

one is usually limited to the 26 letters of the latin alphabet , 20 
 of them being already used for the major amino acids , with the employed 
 search engines . in mascot ,
four of the remaining six letters are hard - coded , 
 which leaves only two letters for the users to add amino acids such 
 as selenocysteine , pyrrolysine , or , in our case , dimethyl - lysine .
mascot : 
 nonstandard amino acids can be defined via the unimod.xml 
 file ; letters j and o are available for modification ; http://www.matrixscience.com/blog/non-standard-amino-acid-residues.html x!tandem : the protein , modified residue mass file can be used 
 to 
 change masses associated with individual letters except the ambiguity 
 letters b , j , x , z , leaving letters o and u available to define nonstandard 
 amino acids ; http://thegpm.org/tandem/api/pmrmf.html comet : user amino acids can be added by using the static modifications 
 parameter with available letters b , j , u , x , z , ( e.g. , add_b_user_amino_acid 
 = 156.125710 ) ; http://comet-s.sourceforge.net/parameters/parameters_201501/ an alternative approach could rely on the ability 
 of more recent 
 versions of search engines to provide the functionality to use multiple 
 databases in one search . if the search engine would allow the setting 
 of different variable modifications for the individual source databases , 
 one could then include a database containing only trypsin and set 
 mono- and dimethylation as variable modifications for just this one 
 database , resulting in the same search space as with our artificial 
 amino acid method .
another feasible approach would be to combine the 
 in silico generated search space with measured spectral libraries 
 from contaminants .
finally , the likely ideal solution would be to 
 store known post - translational modifications directly in the sequence 
 database file , as suggested for the hupo psi extended fasta format 
 ( peff ) .
this would not only remove false positives caused by a well - defined 
 subset of peptides but also directly allow the assigning of all peptides 
 with known post - translational modifications even without the use of 
 variable modifications .
unfortunately , this file format is not yet 
 supported by any of the search engines used in this study .
here , we show that the current state - of - the - art standard proteomic 
 data analysis has severe drawbacks and suggest a new strategy that 
 improves the accuracy of proteomics experiments
. autolysis - protected 
 trypsin ( a standard tool for proteomics ) leads to false - positive peptide 
 identifications .
when reanalyzing all publically available human proteomic 
 data sets ( pride database ) , we found hundreds of statistically significant 
 wrong assignments in more than 18% of the data sets because of methylated 
 peptides originating from trypsin , corresponding to up to 66% of all 
 false positive hits .
furthermore , we present a simple yet efficient 
 search strategy accounting for this problem that can be readily implemented 
 in many widely used search engines .
we have demonstrated that our 
 adapted search strategy employing an artificial amino acid for modified 
 trypsin yields more accurate results than the community gold standard 
 employing contaminant databases solely .
this is accomplished by limiting 
 the inflation of search space to a minimum while efficiently avoiding 
 false - positive peptide identifications .
 chemically 
 modified trypsin is a standard reagent in proteomics 
 experiments but is usually not considered in database searches . 
 modification 
 of trypsin is supposed to protect the protease against autolysis and 
 the resulting loss of activity . here 
 , we show that modified trypsin 
 is still subject to self - digestion , and , as a result , modified trypsin - derived 
 peptides are present in standard digests . 
 we depict that these peptides 
 commonly lead to false - positive assignments even if native trypsin 
 is considered in the database . moreover , we present an easily implementable 
 method to include modified trypsin in the database search with a minimal 
 increase in search time and search space while efficiently avoiding 
 these false - positive hits . 
Introduction
Experimental
Section
Results
Discussion
Conclusion