the dominant means 
 for identification of proteins in complex mixtures 
 is bottom - up proteomics . in this approach , 
 a mixture of proteins from the sample of interest is cleaved into 
 peptides , typically using trypsin , and analyzed by liquid chromatography  mass 
 spectrometry ( lc  ms ) . fragmentation of the peptides within 
 the mass spectrometer yields product - ion mass spectra , which are compared 
 to theoretical mass spectra produced in silico based upon a generic 
 reference protein database of the organism under study . statistical 
 analysis of the results provides a list of peptides identified in 
 the sample , subject to a specified false discovery rate ( fdr ) . proteins present in the sample are then inferred 
 from the identified peptides in a process referred to as protein inference . implementations of this approach are routinely able to identify 
 thousands of proteins in yeast , human , or other organisms . the strategy can reveal differences in protein expression in different 
 cell types or in response to cellular growth conditions or treatment 
 with drugs . while the bottom - up 
 strategy is powerful and widely practiced , 
 it does suffer from major shortcomings . proteins produced from the 
 same gene can vary substantially in their molecular structure : genetic 
 variations , splice variants , rna edits , and post - translational modifications 
 ( ptms ) all give rise to different forms of the proteins , referred 
 to as knowledge 
 of the proteoforms that are present in a system under study is absolutely 
 essential to understanding that system , as the different proteoforms 
 often have dramatically different functional behavior , and regulation of their production is a central 
 aspect of pathway control . one recent example is the finding that 
 intact and clipped human histones differ in post - translational modification 
 patterns and that these combinations 
 of sequence - length and ptm differences have functional consequences . bottom - up strategies are unable to identify proteoforms for two reasons : 
 first , the digestion of the proteins into peptides means that information 
 is lost as to the protein context within which that peptide is found , 
 making impossible the identification of the parent proteoform from 
 which each peptide is derived ; and second , the databases used for 
 peptide identification do not generally contain information regarding 
 amino - acid variant or modified peptides , causing such peptides to 
 be effectively invisible in the absence of specialized search strategies , which can introduce problems with search time and false identifications . one way in which these issues have been addressed is through the 
 alternative strategy of  top - down  proteomics . in top - down proteomics , the entire intact protein 
 is subjected to fragmentation , and the proteoform is identified from 
 the parent mass and fragmentation products ; in the ideal case , this 
 can yield a precise identification of the proteoform , including the 
 nature and positions of ptms . while historically such efforts have 
 largely been limited to the exhaustive study of individual purified 
 protein species , recent work has extended 
 the approach to highly complex samples such as yeast and human cell lysates . however , 
 the highly complex nature and voluminous quantities of the data produced , 
 as well as the need for long ms analysis times to produce data of 
 sufficient sensitivity and resolution for proteoform identification , 
 make this approach , at present , a highly specialized endeavor . we present here an alternative proteomic approach that utilizes 
 proteoform intact mass and lysine count determinations , not tandem 
 ms , to reveal proteoforms and proteoform families . a proteoform family , 
 a concept we introduce here , is a set of proteoforms derived from 
 a single gene . individual proteoforms in a proteoform family frequently 
 differ from one another by single post - translational modifications 
 or amino acid differences but can also differ by larger changes due 
 to splice variation or protein truncation . for example , all of the 
 many different post - translational variants of histone h4 are members 
 of a single proteoform family . a complex proteomic sample such as we devised 
 a computational process for the determination of the proteoform families 
 present in a complex sample . the families are constructed from knowledge 
 of just two pieces of information for each proteoform , the accurate 
 proteoform mass and the number of lysine residues it contains . proteoforms 
 are considered to be related , and thus members of the same family , 
 if their lysine counts are identical and their intact masses differ 
 by the mass of known modifications or amino acid changes ( in this 
 initial study , we have not yet attempted to include larger changes 
 such as splice variation or protein truncation ) . identification of 
 any given member of the family then identifies all members of the 
 family . this initial identification is obtained by matching the accurate 
 mass and lysine count of the experimentally observed proteoform to 
 values calculated from a protein reference database and looking for 
 exact matches within a small mass tolerance . this strategy of using 
 the identification of one proteoform to leverage the identification 
 of many related proteoforms distinguishes this approach from both 
 top - down and bottom - up proteomics , which are based solely upon the 
 identification of individual proteins . in addition , because all members 
 of a family are identified and visualized together , the relative abundances 
 of the related forms are easily compared ( see below ) . the representation 
 and visualization of proteoform families described here nicely parallels 
 related work in the field that connects 
 individual proteoforms and proteoform interaction networks to ptm 
 and disease metadata . this provides an incredible bridge between the 
 experimental process of proteoform identification and the relationship 
 between proteoform observations and the presence of particular disease 
 states . the experimental workflow for 
 identifying proteoforms is straightforward 
 ( see the materials and methods section in the supplemental text ) . briefly , accurate proteoform masses are 
 determined by standard lc  ms analysis of undigested protein 
 mixtures in an orbitrap mass spectrometer , and the lysine count is 
 determined using the neucode stable isotope labeling by amino acids 
 in cell culture ( silac ) isotopic tagging method . we note that the use of intact mass determination and amino 
 acid count for protein identification has been previously reported . we cultured yeast with media containing either of two isotopically 
 heavy forms of lysine : c6n2-lysine ( + 8.0142 da ) or h8-lysine ( + 8.0502 
 da ) . pairs 
 of identical proteoforms produced upon mixing and lysing cells from 
 both cultures have a monoisotopic mass difference equal to 36 mda 
 times the number of lysines in the proteoform . for these experiments , 
 cells grown in media enriched with experiments here were limited to analysis of proteins below 30  000 
 da because of the mass range limitation of the mass spectrometer employed 
 ( thermo ltq orbitrap velos ) . cells are lysed , and a soluble protein 
 cleared lysate is prepared , followed by gel electrophoretic separation 
 into 12 molecular weight fractions , which are analyzed by lc - ms . the resultant mass spectra ( 28  847 in 
 the present study ) are processed in a multistep data - analysis pipeline 
 to provide proteoform identifications ( an example is shown in figure 1 ) . the first step 
 in the pipeline is charge - state deconvolution and deisotoping to yield 
 proteoform monoisotopic intact mass values . protein mass spectra produced 
 by electrospray ionization are highly complex . each individual protein in addition , the 
 natural abundance c , h , n , o , and s atoms in each proteoform yield 
 multiple different isotopologues . therefore , mass spectra must be 
 deconvoluted to eliminate the charge - state differences and deisotoped 
 to eliminate the isotopologue effect to obtain a single monoisotopic 
 mass for each proteoform . next , we paired together mass values that 
 were neucode - light and neucode - heavy isotopologues of one another . the stringent pairing criteria include : a small mass difference of 
 < 6 da , an intensity ratio between 1.4:1 and 6:1 ( based on the expected 
 mixing ratio of 2:1 ) , and also observation in the same spectrum and 
 the same charge states ( see the materials and methods section in the supplemental text ) . first , it greatly increases the confidence that the mass values correspond 
 to actual proteoforms from the sample . second , the number of lysines 
 present in each protein is determined from the mass difference between 
 the doublet peaks for the two proteoform isotopologues using the 36 
 mda per lysine conversion factor . overall , this yielded a set of 70  564 
 intact masses with associated lysine counts ( supplemental table s-1 ) , of which 8637 were nonredundant and thus likely 
 to correspond to unique proteoforms ( supplemental table s-2 ) . ( a ) 
 an lc  ms 
 chromatogram for one gel electrophoresis fraction of neucode silac 
 yeast . ( b ) a full - scan mass spectrum obtained at a resolution of 100  000 . ( c ) an expanded view of the mass spectrum showing one charge - state 
 envelope containing multiple isotope peaks for each of the two isotopologues . ( d ) a further expanded view displaying the  light  ( left 
 peak ) and  heavy  ( right peak ) isotopologues ; the spacing 
 between these two peaks is used to determine the number of lysines 
 in this proteoform . we sought to identify the known yeast proteins to which these 8637 
 proteoforms correspond . this is not possible to achieve by direct 
 comparison of the uniprot database entries with the experimental data 
 because of the wide variety of possible post - translational modifications , 
 which change the intact proteoform masses . we devised a three - stage 
 strategy to address this problem ( figure 2 ) . in stage 1 , experimentally observed proteoforms 
 are identified by pairing them with their theoretical counterparts 
 ( experimental  theoretical ( et ) pairs ) ; in stage 2 , pairs of 
 proteoforms that differ from one another by the mass of well - known 
 protein modifications are identified by pairing them with one another 
 ( experimental  experimental ( ee ) pairs ) ; and in stage 3 , all 
 et and ee pairs sharing a common proteoform are joined together to 
 form proteoform families . three - stage strategy for elucidating proteoform 
 families . in the 
 first stage , experimental intact masses , en , are compared to theoretical masses , tn , ( having the same lysine count ) to create et pairs for certain mass 
 differences ( e.g. , 42 da ) . in the second stage , , the pairs are clustered together to 
 produce proteoform families , two examples of which are shown here . in stage 1 of the strategy , et 
 pairs are identified by comparing 
 experimental masses with theoretical masses from the uniprot entries 
 having the same lysine count . for each of the 8637 observed proteoforms , 
 we determined the uniprot entries ( including single annotated ptms 
 when present ) falling within 500 da and calculated the differences 
 between the experimental and theoretical proteoform masses . the most intense peaks in the histogram 
 correspond to the mass differences associated with frequent protein 
 modifications . note that several of the major peaks have satellites 
 within one or two da , which are likely due to well - known challenges 
 in the deisotoping of mass spectra of intact proteins . we selected 13 of these mass differences that 
 met an average false discovery rate ( fdr ) of 21% , ranging from 8 to 
 35% ( see below for a discussion of fdr ) . this threshold was selected 
 because it captured the major peaks in the histogram corresponding 
 to known prevalent modifications . ( a ) mass differences 
 between 
 experimental masses and theoretical ones calculated from uniprot entries , 
 which have the same number of lysines . ( b ) mass differences between 
 pairs of experimental observations , again stipulating the same lysine 
 count . . a total of 31 of the 88 mass differences 
 ( highlighted in pink ) were directly attributable to known modifications 
 ( e.g. , oxidation , methylation , and acetylation ) or amino acid losses 
 at one of the proteoform termini . another 34 peaks were adjacent to 
 these 31 primary mass shifts , up to 2 da away , and were attributed 
 to misassignment of the proteoform monoisotopic mass . several other 
 mass shifts ( e.g. , 46 and 72 da ) were included in the construction 
 of proteoform families because they exceeded the threshold but they 
 remain unidentified . these two mass shifts in particular are absent 
 in the compendium of modifications at unimod.org . see also the supplemental tables s-9 and s-11 for the complete list of et and ee mass 
 differences to 500 da . in stage 2 of the strategy , ee pairs are identified by comparing 
 all experimental masses of the same lysine count with one another . for each of the 8637 observed proteoforms , we identified the sets 
 of observed proteoforms having the same lysine count and then calculated 
 all pairwise mass differences within each set . a histogram of the 
 aggregated results for all mass differences below 200 da is shown 
 in figure 3b . peaks 
 highlighted in the histogram include ptms , amino acid losses , and 
 other protein modifications commonly observed in protein mass spectrometry . we selected the 88 mass differences that met an average false discovery 
 rate ( fdr ) of 22% ( ranging from 5 to 36% ; see below for a discussion 
 of fdr ) . the larger number of significant mass differences observed 
 for ee pairs ( 88 ) than for et pairs ( 13 ) is due in part to the multiplicative 
 effect of the monoisotopic errors . for example , we may see a proteoform 
 with a monoisotopic mass of 10  000 da and a missed monoisotopic 
 mass for that same proteoform at 10  001 da . the oxidized version 
 of these two forms would have monoisotopic masses of 10  016 
 and 10  017 da , respectively . the ee mass differences for all 
 four species would be 1 , 15 , 16 , and 17 da , with relative intensities 
 of 1:1:2:1 . stage 2 yielded 11  213 ee pairs . in the third stage of analysis , proteoform families are formed 
 by joining together all et and ee pairs sharing a common proteoform . each pair consists of two nodes ( masses of the two proteoforms ) and 
 one edge ( the mass difference between the two proteoforms ) . all pairs 
 having a common node are joined together to form discrete proteoform 
 families . this process yielded 1178 proteoform families ranging in 
 size from 2 to 150 members , as displayed in figure 4a . the proteoform families are represented 
 as collections of nodes and edges , where each node corresponds to 
 a particular proteoform with an associated intact mass and lysine 
 count , and the edges correspond to the mass differences between related 
 proteoforms . the red nodes represent the mass and lysine count of 
 an unmodified ( base ) protein from a protein reference database ( uniprot ) , 
 the green nodes represent the mass and lysine count of a uniprot - curated 
 post - translational modification of the base reference protein entry 
 ( base + ptm ) , and the blue nodes represent experimental mass and lysine 
 count observations from the yeast lysate sample . the area of each 
 blue node is proportional in size to the number of times that proteoform 
 was observed experimentally , providing a crude measure of abundance . 
 in the simple proteoform family shown in figure 4b for negative cofactor 2 complex subunit 
  , for example , there are four nodes and three edges . the red 
 and green nodes represent the uniprot entries for the base and phosphorylated 
 protein , respectively , and the two blue nodes correspond to the experimentally 
 observed mass and lysine count pairs for both proteoforms . there are 
 two zero da mass difference edges shown , connecting the uniprot entries 
 with the experimental observations for those proteoforms , and one 
 80 da mass difference edge connecting the two experimentally observed 
 proteoforms , corresponding to the mass added upon phosphorylation . figure 4c  e shows 
 three other proteoform families of increasing complexity , showing 
 multiple methylations of 60s ribosomal protein l12-a , multiple acetylations 
 of histone h2b.1 , and a pattern of amino acid losses from the n - terminal 
 degradation of 60s ribosomal protein l40 . the ability shown here to 
 identify and visualize the members of proteoform families provides 
 a powerful and unprecedented new view of proteome complexity at the 
 intact proteoform level , information that is critical to understanding 
 biological systems and pathways . theoretical unmodified proteins ( red nodes ) are labeled 
 with their uniprot accession number . theoretical modified proteins 
 ( green nodes ) are labeled with their accession number and a ptm known 
 to occur on that protein . experimentally observed proteoforms ( blue 
 nodes ) are labeled with their intact mass and the number of times 
 it was detected . the area of each blue node is proportional in size 
 to the number of times that proteoform was observed experimentally ; 
 however , to facilitate visualization , all nodes corresponding to 110 
 observations were given the same ( minimum ) size . proteoforms are connected 
 by select mass differences ( edges ) indicated by black lines with orange 
 mass - difference values . of the total 8637 
 proteoforms observed , 2378 were not associated with any other proteoform 
 or a uniprot accession number and hence are not members of families 
 ( orphans ) . the rest of the proteoforms formed 1178 proteoform families 
 composed of 1460 proteoforms belonging to 199 families that correspond 
 to a known protein ( i.e. , are associated with a single uniprot accession 
 number ) ; 802 proteoforms in 27 families that leave some ambiguity 
 in identification in that they were associated with two or more accession 
 numbers ; and the remaining 3997 proteoforms in 952 families that remain 
 unidentified . of the 70  564 total experimental proteoform observations , 
 92% belong to one of the 1178 proteoform families . 1216 ( 14% ) of the 
 8637 proteoforms observed , and 253 ( 11% ) of the 2262 that were also 
 identified , had masses below 5000 da and thus might be considered 
 as peptides rather than proteins ( see supplemental table s-2 for a list of all observed and identified proteoform 
 masses , along with histograms showing their distribution as a function 
 of mass ) . the size distribution of the families is plotted in supplemental figure s-1 and shows a roughly exponential 
 decrease in frequency with increasing size . this plot reveals for 
 the first time the number of different proteoforms for a given base 
 protein , providing a new way of assessing the complexity of the entire 
 yeast proteome . most of the observed proteoforms clustered with theoretical 
 or other experimental proteoforms to make families , although some 
 did not ( i.e. ,  orphans  ) . the proteoform families are 
 categorized as identified , ambiguous , or not yet identified based 
 on containing one , two or more , or zero theoretical accession numbers , 
 respectively . the term  observation  here refers to 
 each detection of a proteoform intact mass and lysine count in any 
 of the 29  847 mass spectra collected in this study . to assess the statistical confidence associated 
 with the identifications , 
 we estimated the false discovery rate ( fdr ) for the et and ee pairs . fdr is an estimate of the fraction of false positive identifications 
 in a group of identifications . the strategies for assessing fdr for 
 each pair type are described briefly below and provided in greater 
 detail in the supporting information . fdrs for the et pairs were determined using a target - decoy strategy , 
 analogous to the widely employed estimation of fdr in bottom - up proteomics . in bottom - up proteomics , the most common method 
 of creating a decoy database for all proteins in an organism of interest 
 is to reverse all of the amino acid sequences . however , this method 
 of creating a decoy database is not useful here because all of the 
 decoy entries would have the same masses and lysine counts as the 
 true target database . we first concatenated 
 all yeast protein sequences in random order into a single continuous 
 string and then divided the string into substrings with lengths equal 
 to each of the known yeast proteins . this yields a decoy database , 
 in which the number and length of decoy protein sequences matches 
 exactly to the known set of yeast proteins , but the masses and lysine 
 counts differ . the database was further expanded to include proteoforms 
 with single post - translational modifications , one for each modification 
 annotated in the uniprot yeast protein database . we created 10 such 
 decoy databases and employed each of them for the stage 1 et identification 
 to determine the number of experimental  decoy ( ed ) pairs , which 
 represent false et connections . the fdr at each mass difference is 
 the ratio of the median number of ed pairs to the number of et pairs 
 and ranged from 8 to 35% , with an average of 21% . the primary factor 
 driving this high fdr for et pairs ( and for ee pairs below ) is mass 
 accuracy , which is limited by the instability and drift in the measurement 
 of intact mass that occurs over the course of the experiment , which 
 requires several days of instrument operation . the false discovery 
 rate is expected to drop with improvements to instrument stability , 
 such as the utilization of a lock - mass standard in the chromatographic 
 buffer for continuous mass calibration . the target - decoy strategy just described for the estimation 
 of 
 fdr for et pairs is not applicable to estimation of fdr for ee pairs 
 because no theoretical database is utilized for identifying ee pairs . a different method was needed to estimate the number of false ee pairs 
 at each of the 88 mass differences . we hypothesized that because all 
 true ee pairs are between proteoforms having the same lysine count , 
 we could use mass differences between experimental values having unequal 
 lysine count as a proxy for false - positive connections . to implement 
 this approach , we calculated the mass differences between all experimentally 
 observed proteoforms differing in lysine count by two or more lysines . 
 because this set of mass differences is vastly larger than the set 
 created when considering only experimental values with the same lysine 
 count , we selected a random subset of size equal to the number of 
 mass differences produced in the ee comparison of figure 3b . we counted the number of 
 mass differences in this subset in a small window (  0.04 da ) 
 around each of the selected ee peaks . this count provides an estimate 
 of the number of false ee connections ( experimental  false lysine 
 count ( ef ) pairs ) in each peak . the fdr at each mass difference is 
 the ratio of the number of ef pairs to the number of ee pairs and 
 ranged from 5 to 36% , with an average of 22% . we note that the 
 modest fdr values reported here ( 21% for et and 
 22% for ee ) do not compare favorably with either bottom - up proteomics , 
 which commonly reports 1% fdr values for protein identification , or 
 top - down proteomics , which commonly reports 15% fdr values 
 for protein identifications . these fdr values for the intact mass 
 and lysine count approach are highly dependent on instrumental factors 
 that can be improved , and therefore , they should not detract from 
 the importance of this new approach to proteoform and proteoform family 
 identification . we compared the identifications obtained from 
 the intact mass and 
 lysine count strategy with those obtained by top - down proteomics . briefly , we aggregated yeast top - down search results ( supplemental table s-3 ) obtained in our own laboratory 
 ( supplemental tables s-4 and s-5 ) with 
 those reported by the kelleher laboratory in the most comprehensive 
 study published to date . a detailed explanation 
 of this comparison is provided in the supplemental text and further supported by additional data found in supplemental tables s-6 through s-17 . we found 75% agreement between the proteoforms identified by top - down 
 proteomics and the ones identified by the intact mass and lysine count 
 strategy . it is of interest to note several current limitations 
 of the intact - mass 
 approach to identification of proteoforms and proteoform families , 
 which offer interesting paths forward for the further development 
 of the strategy . localization could 
 possibly be achieved using either bottom - up or top - down mass spectrometry , 
 but neither method guarantees sequence coverage over the region containing 
 the ptm . the strategy will be necessarily more difficult to implement 
 on samples from more complex organisms such as plant and mammalian 
 species because they have larger proteomes and include genetic variation 
 among individuals . thus , it will be necessary to characterize the 
 sequence variation of the individual under study using large - scale 
 genomic or transcriptomic sequence data to inform and improve the 
 proteomic analysis . efforts to accomplish this are currently an active 
 area of research , referred to as we have used this approach successfully to improve 
 bottom - up proteomic analyses in a variety of mammalian cell lines and anticipate that it will be similarly useful for proteoform family 
 analysis . the neucode silac isotopic tagging strategy employed in 
 this study to provide lysine counts for each proteoform was extremely 
 useful but also limits the approach , as it is not applicable to tissue 
 samples . however , it may be that as comprehensive proteoform databases 
 are established in higher organisms , the lysine - count parameter will 
 become less critical to the identifications and can be replaced by 
 other readily measured or calculated parameters such as chromatographic 
 retention time . only the rudimentary 
 quantification of proteoforms was accomplished in the current work 
 based on the number of times each mass and lysine count was observed . the accuracy and precision will be greatly improved by using intensity - based 
 measurements or isotopic tagging strategies such as neucode silac 
 for relative quantification . finally , we believe 
 that there is room for much improvement over the first - generation 
 bioinformatic and biostatistical approaches presented here . for instance , 
 we are devising descriptive statistical approaches that will provide 
 confidence intervals for the likelihood that each individual node 
 ( proteoform ) is included in the correct family . see the supporting information for more in - depth discussion 
 of these current limitations . we encountered a few interesting 
 phenomena having potential , yet 
 currently unknown , biological significance . first , the process used 
 for determining et and ee pairs , which involves making a histogram 
 showing the frequency of mass difference values , revealed several 
 frequent but previously unknown differences . these peaks , like those 
 revealed in similar work by ourselves and others , suggest the possibility of unknown protein modifications . two examples are the peaks at 46 and 72 da in the ee plot ( figure 3b ) . we have observed 
 these mass differences in mass - tolerant bottom - up proteomics analyses 
 of yeast . second , we observe a considerable number of proteoforms that are missing 
 one or more amino acids from either the n- or c - terminus or both . proteoforms displaying this behavior were also identified by us and 
 by kelleher s group using top - down 
 proteomics . this new strategy of identifying proteoforms from 
 intact mass , 
 lysine count , and clustering into proteoform families serves to complement 
 rather than replace top - down and bottom - up proteomic approaches . we 
 found 1460 proteoforms associated with 199 single accession numbers 
 and an additional 802 proteoforms associated with two or more accession 
 numbers . these numbers compare reasonably well with the most extensive 
 top - down study in yeast to date , which reported 1103 proteoforms associated 
 with 530 accession numbers at 5% fdr , from the same type of sample 
 and gel fractionation . we also compared 
 our results with bottom - up analyses of the same samples , which yielded 
 2651 protein identifications . we found that the frequency of intact 
 proteoform identifications correlated strongly with the bottom - up 
 protein abundance as determined by spectral counting ( supplemental figure s-2 ) , indicating that the 
 more abundant proteins are more readily detected in both strategies . although it is clear that bottom - up analyses are able to identify 
 far more proteins than either intact mass or top - down analyses , they 
 are not able to reveal proteoforms . the intact mass and lysine count 
 strategy could potentially identify more proteoforms than top - down 
 proteomics within a given amount of instrument time due to the intrinsically 
 simpler nature of the data . the intact mass approach is capable of 
 identifying several proteoforms from each high - resolution full spectrum 
 scan , and there are no fragmentation spectra to acquire . however , 
 on the one hand , in top - down mass spectrometry , each identification 
 comes from a high - resolution fragmentation spectrum obtained for a 
 single selected and isolated precursor ( intact proteoform ) . on the 
 other hand , top - down analysis can yield invaluable data that can not 
 be obtained from intact mass measurements , namely the positional localization 
 of modifications or sequence variations . furthermore , the proteoform 
 family concept introduced here is not exclusive to intact mass analyses 
 but could easily be applied to top - down proteomics data to identify 
 additional proteoforms . it is thus apparent that the three proteomic 
 approaches are complementary to one another rather than competitive 
 because each is characterized by differing strengths and weaknesses . another interesting way of comparing top - down and intact mass approaches 
 is to consider  discovery  versus  scoring  
 strategies for proteomics . during the human genome project , the initial 
 phase of single nucleotide polymorphism ( snp ) analysis was a discovery 
 phase : as the dna sequence was generated from different individuals , 
 sequence differences were discovered and catalogued , leading over 
 time to vast databases containing millions of genetic variations . once these variations were known , the need for additional discovery 
 was diminished , and instead , platforms were developed to query samples 
 for already known snps or  scoring  . we envision a similar transition developing for proteoform analysis , 
 with a  discovery phase during which proteoforms are 
 identified and catalogued , populating databases that then enable simpler , 
 less expensive , and higher - throughput proteoform  scoring  
 approaches to be utilized for most biological studies . an early effort 
 at establishing such proteoform databases has recently been initiated 
 by the consortium for top - down proteomics . we posit that the intact - mass approach will function particularly 
 well for scoring proteoforms , and the proteoform family concept will 
 greatly benefit both proteoform discovery and scoring .
proteomics 
 is presently dominated by the  bottom - up  
 strategy , in which proteins are enzymatically digested into peptides 
 for mass spectrometric identification . 
 although this approach is highly 
 effective at identifying large numbers of proteins present in complex 
 samples , the digestion into peptides renders it impossible to identify 
 the proteoforms from which they were derived . 
 we present here a powerful 
 new strategy for the identification of proteoforms and the elucidation 
 of proteoform families ( groups of related proteoforms ) from the experimental 
 determination of the accurate proteoform mass and number of lysine 
 residues contained . 
 accurate proteoform masses are determined by standard 
 lc  ms analysis of undigested protein mixtures in an orbitrap 
 mass spectrometer , and the lysine count is determined using the neucode 
 isotopic tagging method . 
 we demonstrate the approach in analysis of 
 the yeast proteome , revealing 8637 unique proteoforms and 1178 proteoform 
 families . 
 the elucidation of proteoforms and proteoform families afforded 
 here provides an unprecedented new perspective upon proteome complexity 
 and dynamics .