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Volume 10 Supplement 7

UT-ORNL-KBRIN Bioinformatics Summit 2009

  • Meeting abstract
  • Open Access

Prediction of peptide drift time in ion mobility-mass spectrometry

  • 1,
  • 2,
  • 2,
  • 3 and
  • 1Email author
BMC Bioinformatics200910 (Suppl 7) :A1

https://doi.org/10.1186/1471-2105-10-S7-A1

©  Wang et al; licensee BioMed Central Ltd. 2009

  • Published:

Keywords

  • Peptide
  • Artificial Neural Network
  • Hide Layer
  • Charge State
  • Protein Identification

Background

Understanding the proteome, the structure and function of each protein, and the interactions among proteins will give clues to search useful targets and biomarkers for pharmaceutical design. Peptide drift time prediction in IMMS will improve the confidence of peptide identification by limiting the peptide search space during MS/MS database searching and therefore reducing false discovery rate (FDR) of protein identification. A peptide drift time prediction method was proposed here using an artificial neural networks (ANN) regression model. We test our proposed model on three peptide datasets with different charge state assignment (see Table 1). The results can be found in Figure 1, where a higher prediction performance was achieved, over 0.9 for CI and C2, as well as 0.75 for C3.
Table 1

Experimental datasets with different charge state assignment

Dataset

Charge state assignment

Number of peptides

C1

+1

212

C2

+2

306

C3

+3

77

Figure 1
Figure 1

Fraction of peptides vs. prediction accuracy variation threshold. The diagram shows the number of peptides which can be predicted in different accuracy variation levels.

Conclusion

In this study, an ANN regression model was developed to predict peptide drift time in IMMS. Three peptide datasets with different peptide charge states were used to train the predictor to capture the differences of drift time among the varied peptides. The high performance of predictor indicated the capacity of our proposed method. In addition, a simple net architecture, which consisted of an input layer with four neurons, a hidden layer with four nodes and an output layer with one neuron, make our model more effective for application of protein identification.

Declarations

Acknowledgements

This project was funded by 1R41RR024306.

Authors’ Affiliations

(1)
Department of Chemistry, University of Louisville, Louisville, KY 40292, USA
(2)
Predictive Physiology and Medicine Inc, Bloomington, IN 47403, USA
(3)
Department of Chemistry, Indiana University, Bloomington, IN 47405, USA

References

  1. Petritis K, Kangas LJ, Yan B, Strittmatter EF, Monroe M, Qian W, Adkins JN, Moore RJ, Xu Y, Lipton MS: Improved peptide elution time prediction for reversed-phase liquid chromatography-MS by incorporating peptide sequence information. Analytical Chemistry 2006, 78: 5026–5039. 10.1021/ac060143pPubMed CentralView ArticlePubMedGoogle Scholar
  2. McLean JA, Ruotolo BT, Gillig KJ, Russell DH: Ion mobility-mass spectrometry: a new paradigm for proteomics. International Journal of Mass Spectrometry 2005, 240: 301–315. 10.1016/j.ijms.2004.10.003View ArticleGoogle Scholar
  3. Oh C, Zak SH, Mirzaei H, Buck C, Regnier FE, Zhang X: Neural network prediction of peptide separation in strong anion exchange chromatography. Bioinformatics 2007, 23: 114–118. 10.1093/bioinformatics/btl561View ArticlePubMedGoogle Scholar

Copyright

© Wang et al; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd.

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BMC Bioinformatics

ISSN: 1471-2105

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