PepHMM:  A Hidden Markov Model Based Scoring Function for Mass Spectrometry Database Search

Wan, Yunhu; Yang, Austin J.; Chen, Ting

doi:10.1021/ac051319a

Cited by 46 publications

(42 citation statements)

References 53 publications

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“…We alter the scheme slightly to perform multiple spectrum alignment. The use of HMMs for scoring peptide-spectrum alignments has previously been proposed (26). A novel part of our approach is that the HMM is not static, but is updated by model surgery, as we extend the anchor sequence.…”

Section: Multiple Spectrum Alignmentmentioning

confidence: 99%

Template Proteogenomics: Sequencing Whole Proteins Using an Imperfect Database

Castellana

Pham²,

Arnott³

et al. 2010

Molecular & Cellular Proteomics

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Database search algorithms are the primary workhorses for the identification of tandem mass spectra. However, these methods are limited to the identification of spectra for which peptides are present in the database, preventing the identification of peptides from mutated or alternatively spliced sequences. A variety of methods has been developed to search a spectrum against a sequence allowing for variations. Some tools determine the sequence of the homologous protein in the related species but do not report the peptide in the target organism. Other tools consider variations, including modifications and mutations, in reconstructing the target sequence. However, these tools will not work if the template (homologous peptide) is missing in the database, and they do not attempt to reconstruct the entire protein target sequence. De novo identification of peptide sequences is another possibility, because it does not require a protein database. However, the lack of database reduces the accuracy. We present a novel proteogenomic approach, GenoMS, that draws on the strengths of database and de novo peptide identification methods. Protein sequence templates (i.e. proteins or genomic sequences that are similar to the target protein) are identified using the database search tool InsPecT. The templates are then used to recruit, align, and de novo sequence regions of the target protein that have diverged from the database or are missing. We used GenoMS to reconstruct the full sequence of an antibody by using spectra acquired from multiple digests using different proteases. Antibodies are a prime example of proteins that confound standard database identification techniques. The mature antibody genes result from large-scale genome rearrangements with flexible fusion boundaries and somatic hypermutation. Using GenoMS we automatically reconstruct the complete sequences of two immunoglobulin chains with accuracy greater than 98% using a diverged protein database. Using the genome as the template, we achieve accuracy exceeding 97%. Molecular & Cellular Proteomics 9: 1260 -1270, 2010.Database search algorithms, such as Sequest (1), Mascot (2), and InsPecT (3), are the primary workhorses for the identification of tandem mass spectra. However, these methods are limited to the identification of spectra for which peptides are present in the database. It is well recognized that curated protein databases are, at best, an imperfect template for the extant peptides. For example, peptides arising from novel splice forms or fusion proteins would be difficult to identify using most protein databases.Recent developments have extended the identifications to peptides that have diverged from the database entry. By allowing divergence, the methods enable the identification of small-scale mutations, and post-translational modifications, albeit with some loss of sensitivity (4 -7). Among these tools, MS-Blast is able to determine a homologous protein in the related species but does not report the (diverged) protein in the target organism. The other t...

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Section: Multiple Spectrum Alignmentmentioning

confidence: 99%

Template Proteogenomics: Sequencing Whole Proteins Using an Imperfect Database

Castellana

Pham²,

Arnott³

et al. 2010

Molecular & Cellular Proteomics

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“…The mass tolerance between the measured monoisotopic mass and the selected mass was 1.5 u for the molar mass of a precursor peptide ion and 1.0 u for CID fragments. From the searched protein/peptide lists, only those peptides having a Mascot score larger than 30 were selected as showing extensive similarity at the 95% confidence level (Yang et al, 2004;Wan et al, 2006).…”

Section: Data Processingmentioning

confidence: 99%

Large-Scale Identification by Shotgun Proteomics of Proteins Expressed in Porcine Liver and Salivary Gland

et al. 2008

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Protein catalogs containing a large number of proteins expressed in a variety of organs can be powerful tools for stem-cell research, because this requires accurate knowledge about how cells differentiate. Salivary gland progenitor (SGP) cells are somatic stem cells isolated from the salivary gland that can differentiate into hepatic or pancreatic cell lineages. Their differentiation state has been assessed by the expression of major protein markers, but to use these cells in regenerative medicine, it will be necessary to establish additional means of quality assessment. We examined the use of shotgun proteomics for porcine salivary gland (a source of SGP cells) and liver (a destination of differentiated SGP cells) for determining the state of SGP cell differentiation. Protein complexes from each organ were digested into peptides and separated by two-dimensional liquid chromatography involving strong cation-exchange chromatography followed by reversed-phase liquid chromatography. The separated peptides were analyzed by on-line electrospray ionization tandem mass spectrometry using a quadrupole-time of flight mass spectrometer (ESI Q-TOF MS/ MS), and the spectra obtained were processed to search peptides against a mammalian database for protein identification. Using this method, we identified 117 proteins in porcine salivary gland and 154 proteins in porcine liver. Of these, 72 and 109 were specific to salivary gland and liver, respectively, and some of these were previously shown to be organ specific. The current study can be utilized in the future as a basis to study the pattern of differentiation in protein expression by stem cells.

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“…For a spectrum represented by a list of peaks, positive real-valued (m/z,int) pairs, ordered by m/z, the techniques described in Stein (1994), Stein and Scott (1994), Yates et al (1998), Wan et al (2006), Craig et al (2006), Frewen et al (2006), Lam et al (2006Lam et al ( , 2007, and Stein et al (2006) include intensity normalization relative to the base peak or rank, m/z binning and blurring, transformations such as the square root or logarithm of peak intensity, and elimination of insignificant ions by intensity or ranking. There seems little consensus, to date, on spectrum normalization techniques for spectral matching, although the work of Wolski et al (2005) provides some basis for evaluating the many possibilities.…”

Section: Spectrum Normalizationmentioning

confidence: 99%

“…Various projects (Bafna and Edwards, 2001;Schutz et al, 2003;Zhang, 2004;Wan et al, 2006) have sought to predict the probability of observing an ion or to predict its intensity based on intrinsic properties of the peptide or peptide fragment, including the peptide sequence, the ion cleavage position, the ion-type (chemical bond cleaved), adjacent aminoacids, and peptide charge state. While these techniques show promise, they have yet to demonstrate a significant increase peptide identification sensitivity or specificity.…”

Section: Introductionmentioning

confidence: 99%

“…The hidden Markov models are then used to evaluate additional mass spectra, and the results compared with X!Tandem, Mascot (Perkins et al, 1999), another popular sequence database search engine, and NIST's MS Search software (Mallard et al, 2005), a dot-product based spectral matching search engine recently updated to search libraries of peptide fragmentation spectra (Stein et al, 2006). We point out that this approach is different from PepHMM (Wan et al, 2006), which uses a single hidden Markov model to evaluate the quality of any peptide-spectrum match. Our results show that the HMM MS/MS Match (HMMatch) approach identifies many mass spectra left unidentified by Mascot and X!Tandem and is more flexible and robust than MS Search.…”

Section: Introductionmentioning

confidence: 99%

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

Tseng

Edwards

2007

Journal of Computational Biology

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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.

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PepHMM: A Hidden Markov Model Based Scoring Function for Mass Spectrometry Database Search

Cited by 46 publications

References 53 publications

Template Proteogenomics: Sequencing Whole Proteins Using an Imperfect Database

Template Proteogenomics: Sequencing Whole Proteins Using an Imperfect Database

Large-Scale Identification by Shotgun Proteomics of Proteins Expressed in Porcine Liver and Salivary Gland

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

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