Peptide identification

Abstract

Peptides are identified from a list of candidates using collision-induced dissociation tandem mass spectrometry data. A probabilistic model for the occurrence of spectral peaks corresponding to frequently observed partial peptide fragment ions is applied. As part of the identification procedure, a probability score is produced that indicates the likelihood of any given candidate being the correct match. The statistical significance of the score is known without necessarily having reference to the actual identity of the peptide. In one form of the invention, a genetic algorithm is applied to candidate peptides using an objective function that takes into account the number of shifted peaks appearing in the candidate spectrum relative to the test spectrum.

What is claimed is:  
     
         1 . A method of quantifying the relationship between a test spectrum and a reference spectrum from a mass spectrometry device, where the reference spectrum is related to a known peptide sequence, comprising: 
 constructing from the test spectrum a peak table comprising a list locations of peaks in the reference spectrum that have a magnitude greater than a predetermined threshold magnitude;    determining fingerprint data F for a peptide of length P, wherein said fingerprint data comprises p r,i  for all r and i, such that 
 r is a C- or N-terminus sequence C y  or N y  of length y taken from C- or N-terminus sequences C 1 , C 2 , . . . C P , or N 1 , N 2 , . . . N P  of the peptide, respectively,  
 i is an ion type from 1, 2, . . . I, and  
 p r,i  reflects the fraction of replicate spectra in which the peak corresponding to sequence r and ion type i is expected to be observed; and  
   calculating a probability P{H A |x} that the known peptide sequence is present in the sample as a function of fingerprint data F.    
     
     
         2 . The method of  claim 1 , wherein said calculating is performed as a function of a value s that quantifies variability in peak location for all r, i.  
     
     
         3 . The method of  claim 2 , wherein said value s is specified by the tolerance value of a testing instrument.  
     
     
         4 . The method of  claim 1 , wherein said predetermined threshold significance is based on the likelihood of a peak occurring by random chance at a given location.  
     
     
         5 . The method of  claim 4 , wherein said predetermined threshold significance is the likelihood of a peak occurring by random chance at a given location.  
     
     
         6 . The method of  claim 5 , wherein said predetermined threshold significance is a predetermined offset from the likelihood of a peak occurring by random chance at a given location.  
     
     
         7 . The method of  claim 1 , further comprising calculating a probability P{H 0 |x} that the known peptide sequence is not present in the sample as a function of fingerprint data F.  
     
     
         8 . The method of  claim 7 , further comprising calculating a likelihood ratio L of P{H A |x} to P{H 0 |x}.  
     
     
         9 . The method of  claim 8 , further comprising calculating a logarithm λ of the likelihood ratio L.  
     
     
         10 . The method of  claim 1 , wherein at least one of said p r,i  is determined experimentally from actual replicate spectra.  
     
     
         11 . The method of  claim 1 , wherein at least one of said p r,i  is determined mathematically from r and i.  
     
     
         12 . The method of  claim 1 , wherein said fingerprint data F comprises a set of triples (l r,i , s r,i , p r,i ), such that 
 l r,i  is the peak location from the peak table corresponding to sequence r and ion type i, and    s r,i  reflects the variability in the peak location measurement.    
     
     
         13 . An apparatus comprising a processor and a memory in communication with said processor, wherein said memory contains programming instructions executable by said processor to: 
 acquire a spectrum s 0 , as from tandem mass spectrometry, representative of a sample P 0 ;    acquire a spectrum s j , for all j=1, 2, . . . N, as from tandem mass spectrometry, representative of each of a plurality of known peptides P 1 , P 2 , . . . P N ;    acquire a probability p r,i,  which reflects the fraction of replicate spectra in which the peak corresponding to sequence r and ion type i is expected to be observed, for each j, 
 r, a C- or N-terminus sequence C y  or N y  of length y taken from C- or N-terminus sequences C 1 , C 2 , . . . C P , or N 1 , N 2 , . . . N P  of the peptide, respectively, and  
 i, an ion type from 1, 2, . . . I, and  
   for each j, calculate a probability P j {H A |x} that the known peptide P j  is present in the sample as a function of the p r,i  for that j.    
     
     
         14 . The method of  claim 13 , wherein for each j the probability P j {H A |x} is calculated also as a function of the variability in the peak location measurement associated with the spectrum s j .  
     
     
         15 . The method of  claim 13 , wherein for each j the probability P j {H A |x} is calculated also as a function of the peak locations in the spectrum s j .  
     
     
         16 . The method of  claim 13 , wherein at least one spectrum s j  is obtained experimentally from actual peptide P j .  
     
     
         17 . The method of  claim 13 , wherein at least one spectrum s j  is obtained algorithmically from information about theoretical peptide P j .  
     
     
         18 . A method of scoring the relationship between a plurality of candidate peptides and a sample, comprising: 
 generating a list of candidate peptides; and    scoring each candidate peptide in the list independently of said generation.    
     
     
         19 . The method of  claim 18 , wherein said scoring is performed as a function of the coincidence of peaks in a mass spectrometry spectrum corresponding to the candidate peptide with peaks in a mass spectrometry spectrum corresponding to the sample.  
     
     
         20 . The method of  claim 18 , wherein said scoring comprises calculating a probability that the known peptide sequence is present in the sample as a function of 
 l r,i , the peak location from the peak table corresponding to sequence r and ion type i,    s r,i , which reflects the variability in the peak location measurement, and    p r,i , which reflects the fraction of replicate spectra in which the peak corresponding to ion r and ion type i is expected to be observed, for all    rε{C- and N-terminus sequences C y  and N y  of length y taken from C- or N-terminus sequences C 1 , C 2 , . . . C P , or N 1 , N 2 , . . . N P , of the known peptide sequence, respectively}, and    iε{ion types from 1, 2, . . . I}.    
     
     
         21 . A method of finding one or more possible matching peptides to a test peptide associated with a tandem mass spectrometry test spectrum s, comprising: 
 selecting a function f that takes spectra s 1  and s 2  as input, where f includes at least one term comprising the number n of peaks that appear in both s1 and a shifted copy of s 2 ; and    performing a genetic algorithm on a plurality of candidate peptides using f as the objective function and using s as either s 1  or s 2 .    
     
     
         22 . The method of  claim 21 , wherein said performing comprises: 
 generating a second plurality of candidate peptides from a first plurality of candidate peptides, wherein said generating comprises calculating f(s, t) for each t in a set of spectra representing the first plurality of candidate peptides.    
     
     
         23 . The method of  claim 21 , wherein said performing comprises determining n for some s 1  and s 2  by: 
 creating an m 1 ×m 2  matrix M, where: 
 m 1  is the number of peaks in s 1 ;  
 m 2  is the number of peaks in s 2 ; and  
 the cell of M at row i, column j, holds a number representative of the signed difference between the location of peak i in s 1  and peak j in s 2 ; and  
   assigning n to be the number of non-distinct values in M.    
     
     
         24 . The method of  claim 21 , wherein said performing comprises determining n for some s 1  and s 2  by: 
 creating an m 1 ×m 2  matrix M, where: 
 m 1  is the number of peaks in s 1 ;  
 m 2  is the number of peaks in s 2 ; and  
 the cell of M at row i, column j, holds a number representative of the signed difference between the location of peak i in s 1  and peak j in s 2 ; and  
   assigning n to be the maximum number of times a non-distinct value appears in M.