Improving Peptide-Spectrum Matching by Fragmentation Prediction Using Hidden Markov Models

Research output: Contribution to journalJournal article

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Improving Peptide-Spectrum Matching by Fragmentation Prediction Using Hidden Markov Models. / Kirik, Ufuk; Refsgaard, Jan C.; Jensen, Lars J.

In: Journal of Proteome Research, Vol. 18, No. 6, 2019, p. 2385-2396.

Research output: Contribution to journalJournal article

Harvard

Kirik, U, Refsgaard, JC & Jensen, LJ 2019, 'Improving Peptide-Spectrum Matching by Fragmentation Prediction Using Hidden Markov Models', Journal of Proteome Research, vol. 18, no. 6, pp. 2385-2396. https://doi.org/10.1021/acs.jproteome.8b00499

APA

Kirik, U., Refsgaard, J. C., & Jensen, L. J. (2019). Improving Peptide-Spectrum Matching by Fragmentation Prediction Using Hidden Markov Models. Journal of Proteome Research, 18(6), 2385-2396. https://doi.org/10.1021/acs.jproteome.8b00499

Vancouver

Kirik U, Refsgaard JC, Jensen LJ. Improving Peptide-Spectrum Matching by Fragmentation Prediction Using Hidden Markov Models. Journal of Proteome Research. 2019;18(6):2385-2396. https://doi.org/10.1021/acs.jproteome.8b00499

Author

Kirik, Ufuk ; Refsgaard, Jan C. ; Jensen, Lars J. / Improving Peptide-Spectrum Matching by Fragmentation Prediction Using Hidden Markov Models. In: Journal of Proteome Research. 2019 ; Vol. 18, No. 6. pp. 2385-2396.

Bibtex

@article{b488196a0eaa4b299f7489b7e2d6ed3f,
title = "Improving Peptide-Spectrum Matching by Fragmentation Prediction Using Hidden Markov Models",
abstract = "Tandem mass spectrometry has become the method of choice for high-throughput, quantitative analysis in proteomics. Peptide spectrum matching algorithms score the concordance between the experimental and the theoretical spectra of candidate peptides by evaluating the number (or proportion) of theoretically possible fragment ions observed in the experimental spectra without any discrimination. However, the assumption that each theoretical fragment is just as likely to be observed is inaccurate. On the contrary, MS2 spectra often have few dominant fragments. Using millions of MS/MS spectra we show that there is high reproducibility across different fragmentation spectra given the precursor peptide and charge state, implying that there is a pattern to fragmentation. To capture this pattern we propose a novel prediction algorithm based on hidden Markov models with an efficient training process. We investigated the performance of our interpolated-HMM model, trained on millions of MS2 spectra, and found that our model picks up meaningful patterns in peptide fragmentation. Second, looking at the variability of the prediction performance by varying the train/test data split, we observed that our model performs well independent of the specific peptides that are present in the training data. Furthermore, we propose that the real value of this model is as a preprocessing step in the peptide identification process. The model can discern fragment ions that are unlikely to be intense for a given candidate peptide rather than using the actual predicted intensities. As such, probabilistic measures of concordance between experimental and theoretical spectra will leverage better statistics.",
author = "Ufuk Kirik and Refsgaard, {Jan C.} and Jensen, {Lars J.}",
year = "2019",
doi = "10.1021/acs.jproteome.8b00499",
language = "English",
volume = "18",
pages = "2385--2396",
journal = "Journal of Proteome Research",
issn = "1535-3893",
publisher = "American Chemical Society",
number = "6",

}

RIS

TY - JOUR

T1 - Improving Peptide-Spectrum Matching by Fragmentation Prediction Using Hidden Markov Models

AU - Kirik, Ufuk

AU - Refsgaard, Jan C.

AU - Jensen, Lars J.

PY - 2019

Y1 - 2019

N2 - Tandem mass spectrometry has become the method of choice for high-throughput, quantitative analysis in proteomics. Peptide spectrum matching algorithms score the concordance between the experimental and the theoretical spectra of candidate peptides by evaluating the number (or proportion) of theoretically possible fragment ions observed in the experimental spectra without any discrimination. However, the assumption that each theoretical fragment is just as likely to be observed is inaccurate. On the contrary, MS2 spectra often have few dominant fragments. Using millions of MS/MS spectra we show that there is high reproducibility across different fragmentation spectra given the precursor peptide and charge state, implying that there is a pattern to fragmentation. To capture this pattern we propose a novel prediction algorithm based on hidden Markov models with an efficient training process. We investigated the performance of our interpolated-HMM model, trained on millions of MS2 spectra, and found that our model picks up meaningful patterns in peptide fragmentation. Second, looking at the variability of the prediction performance by varying the train/test data split, we observed that our model performs well independent of the specific peptides that are present in the training data. Furthermore, we propose that the real value of this model is as a preprocessing step in the peptide identification process. The model can discern fragment ions that are unlikely to be intense for a given candidate peptide rather than using the actual predicted intensities. As such, probabilistic measures of concordance between experimental and theoretical spectra will leverage better statistics.

AB - Tandem mass spectrometry has become the method of choice for high-throughput, quantitative analysis in proteomics. Peptide spectrum matching algorithms score the concordance between the experimental and the theoretical spectra of candidate peptides by evaluating the number (or proportion) of theoretically possible fragment ions observed in the experimental spectra without any discrimination. However, the assumption that each theoretical fragment is just as likely to be observed is inaccurate. On the contrary, MS2 spectra often have few dominant fragments. Using millions of MS/MS spectra we show that there is high reproducibility across different fragmentation spectra given the precursor peptide and charge state, implying that there is a pattern to fragmentation. To capture this pattern we propose a novel prediction algorithm based on hidden Markov models with an efficient training process. We investigated the performance of our interpolated-HMM model, trained on millions of MS2 spectra, and found that our model picks up meaningful patterns in peptide fragmentation. Second, looking at the variability of the prediction performance by varying the train/test data split, we observed that our model performs well independent of the specific peptides that are present in the training data. Furthermore, we propose that the real value of this model is as a preprocessing step in the peptide identification process. The model can discern fragment ions that are unlikely to be intense for a given candidate peptide rather than using the actual predicted intensities. As such, probabilistic measures of concordance between experimental and theoretical spectra will leverage better statistics.

U2 - 10.1021/acs.jproteome.8b00499

DO - 10.1021/acs.jproteome.8b00499

M3 - Journal article

C2 - 31074280

VL - 18

SP - 2385

EP - 2396

JO - Journal of Proteome Research

JF - Journal of Proteome Research

SN - 1535-3893

IS - 6

ER -

ID: 219533755