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This article is part of the supplement: UT-ORNL-KBRIN Bioinformatics Summit 2009

Open Access Meeting abstract

Prediction of peptide drift time in ion mobility-mass spectrometry

Bing Wang1, Steve Valentine2, Sriram Raghuraman2, Manolo Plasencia3 and Xiang Zhang1*

Author 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

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BMC Bioinformatics 2009, 10(Suppl 7):A1  doi:10.1186/1471-2105-10-S7-A1

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/10/S7/A1


Published:25 June 2009

© 2009 Wang et al; licensee BioMed Central Ltd.

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

thumbnailFigure 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.

Acknowledgements

This project was funded by 1R41RR024306.

References

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