HMMatch: Peptide Identification by Spectral Matching of Tandem Mass Spectra Using Hidden Markov Models

Abstract

Peptide identification by tandem mass spectrometry is the dominant proteomics workflow for protein characterization in complex samples. The peptide fragmentation spectra generated by these workflows exhibit characteristic fragmentation patterns that can be used to identify the peptide. In other fields, where the compounds of interest do not have the convenient linear structure of peptides, fragmentation spectra are identified by comparing new spectra with libraries of identified spectra, an approach called spectral matching. In contrast to sequence-based tandem mass spectrometry search engines used for peptides, spectral matching can make use of the intensities of fragment peaks in library spectra to assess the quality of a match. We evaluate a hidden Markov model approach (HMMatch) to spectral matching, in which many examples of a peptide's fragmentation spectrum are summarized in a generative probabilistic model that captures the consensus and variation of each peak's intensity. We demonstrate that HMMatch has good specificity and superior sensitivity, compared to sequence database search engines such as X! Tandem. HMMatch achieves good results from relatively few training spectra, is fast to train, and can evaluate many spectra per second. A statistical significance model permits HMMatch scores to be compared with each other, and with other peptide identification tools, on a unified scale. HMMatch shows a similar degree of concordance with X!Tandem, Mascot, and NIST's MS Search, as they do with each other, suggesting that each tool can assign peptides to spectra that the others miss. Finally, we show that it is possible to extrapolate HMMatch models beyond a single peptide's training spectra to the spectra of related peptides, expanding the application of spectral matching techniques beyond the set of peptides previously observed.

Machine learning reveals sex‐specific 17β‐estradiol‐responsive expression patterns in white perch (Morone americana) plasma proteins

“…Normalized spectral counts (NSpC) were calculated using the total spectral counts within the replicates of each sample and followed with the maximum spectral counts obtained from each IC and E 2 ‐induced plasma replicate analysis for each of the genders. These NSpC for each of the three technical replicates per biological sample pool were exported from ProteoIQ and transformed to account for zero values [log 10 (y + 1), where y = NSpC] . Proteins with ≥ 0.25 average NSpC (NSpC avg ) across all twelve technical replicates (three technical replicates per treatment) were analyzed by two‐way ANOVA .…”

Spectral library searching in proteomics

“…HMMatch, published by Wu et al. in 2007 was the first published proteomics spectral library search engine that does not utilize the dot‐product as a similarity metric . Instead, HMMatch relies on a hidden Markov model that is built on the distribution of peaks and their intensities among the spectra from which the consensus spectrum was derived.…”

Tandem mass spectral libraries of peptides and their roles in proteomics research

“…Pepitome (Dasari et al, ) introduced a probabilistic scoring function that combined three scores: the tail probability of the given match under the null hypothesis of random peak matching; a Kendall–Tau rank correlation function that measures the difference in intensity ranks of matched peaks; and the probability of obtaining the observed mass errors based on the mass resolution of the instrument. HMMatch (Wu, Tseng, & Edwards, ) developed a hidden Markov model approach to evaluate SSMs, in which many examples of a peptide's fragmentation spectrum were summarized in a generative probabilistic model that captured both mean and variation of each peak's intensity. Tremolo (Wang and Bandeira, ) attempted to establish an empirical distribution describing the natural variations in intensities in replicate spectra, and then used it to develop a “spectral library generating function” to calculate the expected distribution of correct matches.…”