protein identification using mass spectrometry ( ms ) is an essential step in studies that employ proteomic methods such as two - dimensional electrophoresis ( 2-de ) , and is typically accomplished by either peptide mass fingerprinting ( pmf ) or amino acid sequencing of the peptide using tandem mass spectrometry ( ms / ms ) . in pmf analysis , proteolytic cleavage using an enzyme such as trypsin results in a collection of peptides , which serves as a unique identifier or fingerprint of the protein . the accurate determination of peptide mass - to - charge ( m / z ) ratio , typically using a matrix - assisted laser desorption / ionization time - of - flight ( maldi - tof ) mass spectrometer , allows identification of the unknown protein by matching the resulting peptide masses with the theoretical peptide masses of proteins in a database ( such as ncbi and swiss - prot ) . for peptide sequence analysis , peptides obtained by proteolytic cleavage are subjected to ms / ms to fragment the peptide along the amide backbone . the amino acid sequence of the peptide is then obtained from the differences in m / z ratios for a series of daughter ions . subsequently , the sequence ions and the intact peptide masses are matched against protein databases to identify the unknown protein . to determine the prevalence of pmf analysis as well as the type of search algorithms employed in protein identification , we surveyed articles published in proteomics ( august 2005 to july 2007 ) and proteome science ( january 2003 to september 2007 ) . out of the 581 articles surveyed , approximately 35% of the studies only used pmf - based protein identification , 32% of the studies only utilized ms / ms , and 33% of the studies used both pmf and ms / ms for protein identification . thus , although sequence information from ms / ms analysis can be used to validate pmf - based protein identification , it may not be practical when analyzing a large number of proteins and when high - throughput ms / ms instrumentation is not readily available . besides , in comparison to ms / ms analysis on a single peptide , identification of proteins using pmf has two significant advantages that are often ignored . firstly , in pmf , unsuspected post - translational modifications lead to only a marginal loss in the quality of data and do not affect the outcome . in contrast , in ms / ms , unspecified post - translational modifications can adversely affect the matching and scoring process , thus precluding unambiguous protein identification . secondly , although ms / ms analysis can be used to determine the amino acid sequence of a peptide , that peptide may be unique to a particular protein or common to a number of different proteins ( for example , enzymes from the same family ) . conversely , the use of multiple peptides for protein identification in pmf allows more extensive coverage of the protein , thereby increasing the confidence in a positive identification . thus , in some cases the ms / ms analysis on a single peptide may actually be less specific than pmf . a pertinent question when using pmf for protein identification is that : at what point does one use ms / ms analysis for protein identification ? a commonly used approach is to use pmf as an initial step , and then use ms / ms to corroborate proteins that are considered ambiguous . however , this brings up an interesting question : when can one conclude protein identification using pmf to be the traditional definition of  unambiguous  ( having or exhibiting no ambiguity or uncertainty ) can hardly be applied to pmf - based protein identification , which is probability based and there always remains a chance of obtaining a false positive protein match . so when used in the context of protein identification , a pragmatic definition of  unambiguous  would be  having or exhibiting little ambiguity or uncertainty  . without accompanying amino acid sequence data , can one increase the confidence in protein identification using pmf data ? western blotting can be used to confirm protein identification ; however , this technique is not high - throughput , limited by the availability of antibodies , and impractical for analyzing large number of samples . presently , there are huge disparities in criteria used to identify proteins using pmf . ( 1 ) selected a molecular weight search ( mowse ) score of 71 or more as significant for a protein match , while hoffrogge et al . ( 2 ) used a mowse score of greater than 69 as significant . on the other hand , it has been suggested that the mowse score should be 50 more than the significant threshold level for protein identification ( 3 ) . because the mowse score depends on the number of sequences in a database , for species with small database sizes this approach would not be appropriate . naranjo et al . ( 4 ) categorized a match as successful if a protein had a score greater than the significant threshold of the mascot algorithm ( 5 . , 6 . ; www.matrixscience.com ) and was the top match in both mascot and profound 7 . algorithms . whereas proteins with a mascot score of at least 95 or a profound score of 2.2 have been considered significant by others ( 9 ) . also , guipaud et al . ( 10 ) and sinclair et al . ( 11 ) , in addition to the significant scores of protein algorithms , incorporated sequence coverage , number of peptides matched , and the congruence of isoelectric point ( pi ) and molecular mass of the protein with the sequence database in the evaluation of pmf data . consequently , definitive guidelines for what constitutes a positive match are lacking . to this end , we propose a value - based scoring system that provides guidance on evaluating when pmf - based protein identification can be deemed sufficient without accompanying amino acid sequence data from ms / ms analysis . to construct this value - based scoring system , we have used parameters that are considered important in substantiating protein identification , such as congruence of the observed pi and molecular mass with the protein sequence database , percent sequence coverage ( percentage of the theoretical protein that is covered by the experimental peptide masses ) , number of peptides matched , and the matching scores from two different protein search engines . since pmf - based protein identification is probability - based , the choice of protein search algorithms is of paramount importance . to date , thirteen different pmf search algorithms have been reported ( 12 ) . there are several ways that these protein search engines differ from one another , but the fundamental difference is in the scoring algorithm that is employed . some of the protein search engines calculate the probability of obtaining an incorrect match and report significance threshold scores to increase the confidence in a protein match , whereas others just rank possible protein matches without reporting a significance level . in our sampling of the literature , mascot was the most commonly used protein search engine ( 67% ) , followed by ms - fit ( 13 ) ( 14% ) and profound ( 12% ) . mascot and ms - fit implement a probability - based scoring approach using mowse ; however , while mascot reports scores that correspond to a 5% significance level , ms - fit does not provide a significance level for its protein matches . profound , on the other hand , uses the bayesian probability and reports a significance threshold score ( z score ) . incidentally , most studies that employed ms - fit or profound also used mascot for database searching . the threshold mowse score in mascot is reported as 10  lg(p ) , where p (  value / number of database sequences ) is the probability that the observed match is a random event 5 . , 6 .. accordingly , the threshold mowse score depends on two parameters : the number of sequences in a database and the  value used for searches . we plotted the mowse scores for a number of different theoretical database sequences , ranging from 10,000 to 1,000,000 at various  values ( 0.05 , 0.01 , and 0.001 ) ( figure 1 ) . since the significance threshold score depends on the number of sequences in the database , as the number of protein sequences increase , the threshold would also increase . this issue is of greater relevance when it comes to the identification of proteins from organisms whose sequences have not been completely determined . also , post - translational processing of proteins would increase the number of protein sequences in the databases , even for completely sequenced genomes . for instance , if the searched database contains 20,000 sequences , a protein match would be considered positive at the default  value of 0.05 when the mowse score exceeds the threshold of 56 . however , this protein match would no longer be positive if the number of sequences in the database were to increase . on the other hand , if the search was performed at a lower  value ( for example 0.01 ) , the protein match would still retain its significance ( at an  of 0.05 ) even if there was a five - fold increase in the number of sequences in the future . therefore , based on the distribution profile of threshold mowse scores ( figure 1 ) , we suggest performing pmf searches in mascot using an  value of 0.01 , as opposed to the default  value of 0.05 . for organisms whose complete sequence information is not available , it would be better to carry out and report protein searches at an  value of 0.001 . this ensures that the matching would still be significant at an  value of 0.05 , even if there was a vast increase in the number of sequences in the future ( figure 1 ) . furthermore , a decrease in  value decreases the risk of type 1 error (  error ) associated with the search , and thus decreases the incidence of false protein identification with pmf . although this would increase the type 2 error (  error ) resulting in an increase of false negatives ( number of proteins that can not be identified ) , we feel it is preferable to be highly averse to making a type 1 error as the goal is to correctly identify proteins . unlike the mowse score used in mascot , the significance threshold of profound , z score , is constant and is independent of the number of sequences available in the database . the z score is used as a measure of probability of a random protein match and represents the distance of a sample from the mean in units of standard deviation . a z score of 1.65 (  = 0.05 , one tail ) or lower signifies that the protein match is likely to be random and a score greater than 1.65 indicates that the protein match is significant 7 . , 8 .. we recommend using two different protein search engines that employ dissimilar algorithms for protein identification as this should reduce the likelihood of false protein identification . as reporting of significance threshold scores help to assess the quality of a protein match , we suggest the use of mascot and profound for pmf searches . in addition to the scoring of protein matching with algorithms such as mascot and profound , other factors that assist in substantiating protein identification include the percent sequence coverage and the number of peptides matched . despite the expected variation in percent coverage based on the size of the protein ( the sequence coverage for large proteins tends to be less and vice versa ) , , we suggest a minimum of four peptides to be matched for positive protein identification with pmf . we have transformed the commonly used parameters ( pi , molecular mass , percent sequence coverage , number of peptides matched , and significance scores of mascot and profound ) in pmf into a value - based scoring system for an objective evaluation of protein identification using pmf ( table 1 ) . with this scoring system , a combined score of 15 or more can be considered sufficient for the protein match using pmf to be unequivocal without the need for subsequent ms / ms analysis . the threshold of 15 was chosen by taking the following into account : congruence of pi and molecular mass ( 4 points ) , sequence coverage of 20% or more ( 3 points ) , minimum of four peptides matched ( 2 points ) , and protein match with algorithms mascot and profound at an  value of 0.01 ( 6 points ) . we have gone through an iterative procedure to score the parameters according to their degree of relevance in pmf - based protein identification . the value - based system was derived based on the criteria that are commonly used in the literature and also on our personal experience in protein identification . though protein algorithms mascot and profound were used to construct the scoring system , any comparable protein search algorithms can be substituted . while being conservative , the scoring system is also objective and flexible . for instance , protein modifications can alter the observed pi ( for example , deamidation ) as well as the molecular mass . however , this would not preclude an unequivocal protein determination , but rather the absence of information on pi or molecular weight can be offset by an increase in the percent sequence coverage , number of peptides matched , or use of a more stringent threshold score for mascot and profound . it should be noted that this scoring system can not substitute for the care undertaken in sample preparation and 2-de analysis . rather , it provides guidance on evaluating the need for a subsequent ms / ms analysis when using pmf . one of the possible drawbacks of this value - based scoring system is that it is conservative . therefore , while decreasing the number of false positives , it would also increase the number of false negatives ( proteins that are changed but not identified ) . that is , with this scoring system , a score of 15 or more would be considered positive . however , in reality a protein match with a score of 14 or less can still be the correct match . nevertheless , as the goal is to increase confidence in pmf - based protein identification without the accompanying need for sequence analysis , we feel it is preferable to be type 1 error averse . besides , the number of peptides matched ( as well as sequence coverage ) would vary depending on protein size . since the scoring system recommends matching a minimum of four peptides , it can affect the identification of small sized proteins . however , the lack of a recommended minimum number of peptides can be offset by other parameters ( ph , mw , and the significant scores of protein search engines ) , and thus should not affect the identification of most proteins . in addition to the factors discussed , for reliable protein identification using pmf , the search parameters ( such as error tolerance and the number of missed cleavages ) that are employed should also be optimized ; however , an elaborate discussion on these parameters is beyond the scope of the present study and readers are referred to germane articles 8 . , 18 .. though the levels of error tolerance vary across studies , a value of 100 or less is desirable . for the number of missed cleavages , a value of 1 should be optimal , although a lot of studies have used 2 missed cleavages . with regard to protein modifications , most studies employ carbamidomethylation of cysteine as a fixed modification and oxidation of methionine as a partial modification 2 . , presently , the majority of proteomic studies employ pmf . therefore , to reduce incorrect protein identification using pmf , and also to increase confidence in pmf - based protein identification without accompanying ms / ms analysis , definitive guiding principles are essential . the value - based scoring system proposed in this study is objective , flexible , and provides preliminary direction on evaluating when pmf - based protein identification can be considered sufficient without a subsequent need for amino acid sequence analysis .
identification of proteins by mass spectrometry ( ms ) is an essential step in proteomic studies and is typically accomplished by either peptide mass fingerprinting ( pmf ) or amino acid sequencing of the peptide . although sequence information from ms / ms analysis can be used to validate pmf - based protein identification , it may not be practical when analyzing a large number of proteins and when high - throughput ms / ms instrumentation is not readily available . at present , a vast majority of proteomic studies employ pmf . 
 however , there are huge disparities in criteria used to identify proteins using pmf . therefore , to reduce incorrect protein identification using pmf , and also to increase confidence in pmf - based protein identification without accompanying ms / ms analysis , definitive guiding principles are essential . to this end 
 , we propose a value - based scoring system that provides guidance on evaluating when pmf - based protein identification can be deemed sufficient without accompanying amino acid sequence data from ms / ms analysis .