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

BMC Bioinformatics

Proceedings

Peng Chen¹  Chi Li²  Jun Zhang³  Zhiwei Ji⁴  Shuping Deng⁴  Bing Wang⁵ 

[1] Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, 23955-6900, Thuwal, Saudi Arabia;Department of Medicine, University of Louisville, 40202, Louisville, KY, USA;School of Electronic Engineering & Automation, Anhui University, 230601, Hefei, Anhui, China;School of Electronics and Information Engineering, Tongji University, 201804, Shanghai, China;The Advanced Research Institute of Intelligent Sensing Network, Tongji University, 201804, shanghai, China;The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, 201804, Shanghai, China;School of Electronics and Information Engineering, Tongji University, 201804, Shanghai, China;

关键词: Support Vector Regression;    Drift Time;    High Charge State;    Less Square Support Vector Regression;    Drift Cell;   

DOI  :  10.1186/1471-2105-14-S8-S9

来源: Springer

【 摘 要 】

BackgroundIon 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.ResultsIn 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.ConclusionsOur 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.

【 授权许可】

CC BY   
© Wang et al.; licensee BioMed Central Ltd. 2013