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

Meeting abstract

Prediction of peptide drift time in ion mobility-mass spectrometry

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

¹Department of Chemistry, University of Louisville, Louisville, KY 40292, USA

²Predictive Physiology and Medicine Inc. Bloomington, IN 47403, USA

³Department of Chemistry, Indiana University, Bloomington, IN 47405, USA

author email corresponding author email

from UT-ORNL-KBRIN Bioinformatics Summit 2009
Pikeville, TN, USA. 20–22 March 2009

BMC Bioinformatics 2009, 10(Suppl 7):A1doi:10.1186/1471-2105-10-S7-A1

The electronic version of this abstract 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.

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.

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