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This article is part of the supplement: Proceedings of the 2012 International Conference on Intelligent Computing (ICIC 2012)

Open Access Proceedings

Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features

Bing Wang123*, Jun Zhang4, Peng Chen5, Zhiwei Ji3, Shuping Deng3 and Chi Li6

Author Affiliations

1 The Advanced Research Institute of Intelligent Sensing Network, Tongji University, shanghai, 201804, China

2 The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, China

3 School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China

4 School of Electronic Engineering & Automation, Anhui University, Hefei, Anhui 230601, China

5 Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia

6 Department of Medicine, University of Louisville, Louisville, KY 40202, USA

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BMC Bioinformatics 2013, 14(Suppl 8):S9  doi:10.1186/1471-2105-14-S8-S9

Published: 9 May 2013



Ion mobility-mass spectrometry (IMMS), an analytical technique which combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS), can rapidly separates ions on a millisecond time-scale. IMMS becomes a powerful tool to analyzing complex mixtures, especially for the analysis of peptides in proteomics. The high-throughput nature of this technique provides a challenge for the identification of peptides in complex biological samples. As an important parameter, peptide drift time can be used for enhancing downstream data analysis in IMMS-based proteomics.


In this paper, a model is presented based on least square support vectors regression (LS-SVR) method to predict peptide ion drift time in IMMS from the sequence-based features of peptide. Four descriptors were extracted from peptide sequence to represent peptide ions by a 34-component vector. The parameters of LS-SVR were selected by a grid searching strategy, and a 10-fold cross-validation approach was employed for the model training and testing. Our proposed method was tested on three datasets with different charge states. The high prediction performance achieve demonstrate the effectiveness and efficiency of the prediction model.


Our proposed LS-SVR model can predict peptide drift time from sequence information in relative high prediction accuracy by a test on a dataset of 595 peptides. This work can enhance the confidence of protein identification by combining with current protein searching techniques.