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

Handle URI:
http://hdl.handle.net/10754/325237
Title:
Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features
Authors:
Wang, Bing; Zhang, Jun; Chen, Peng; Ji, Zhiwei; Deng, Shuping; Li, Chi
Abstract:
Background: 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.Results: 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.Conclusions: 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. 2013 Wang et al.; licensee BioMed Central Ltd.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Wang B, Zhang J, Chen P, Ji Z, Deng S, et al. (2013) Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features. BMC Bioinformatics 14: S9. doi:10.1186/1471-2105-14-S8-S9.
Publisher:
Springer Nature
Journal:
BMC Bioinformatics
Issue Date:
9-May-2013
DOI:
10.1186/1471-2105-14-S8-S9
PubMed ID:
23815343
PubMed Central ID:
PMC3654891
Type:
Article
ISSN:
14712105
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorWang, Bingen
dc.contributor.authorZhang, Junen
dc.contributor.authorChen, Pengen
dc.contributor.authorJi, Zhiweien
dc.contributor.authorDeng, Shupingen
dc.contributor.authorLi, Chien
dc.date.accessioned2014-08-27T09:41:26Z-
dc.date.available2014-08-27T09:41:26Z-
dc.date.issued2013-05-09en
dc.identifier.citationWang B, Zhang J, Chen P, Ji Z, Deng S, et al. (2013) Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features. BMC Bioinformatics 14: S9. doi:10.1186/1471-2105-14-S8-S9.en
dc.identifier.issn14712105en
dc.identifier.pmid23815343en
dc.identifier.doi10.1186/1471-2105-14-S8-S9en
dc.identifier.urihttp://hdl.handle.net/10754/325237en
dc.description.abstractBackground: 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.Results: 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.Conclusions: 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. 2013 Wang et al.; licensee BioMed Central Ltd.en
dc.language.isoenen
dc.publisherSpringer Natureen
dc.rightsThis is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttp://creativecommons.org/licenses/by/2.0en
dc.subject10-fold cross-validationen
dc.subjectEffectiveness and efficienciesen
dc.subjectIon mobility spectrometryen
dc.subjectIon mobility-mass spectrometryen
dc.subjectPrediction performanceen
dc.subjectProtein identificationen
dc.subjectSequence informationsen
dc.subjectSupport vectors regressionen
dc.subjectForecastingen
dc.subjectIdentification (control systems)en
dc.subjectIon mobility spectrometersen
dc.subjectIonsen
dc.subjectLeast squares approximationsen
dc.subjectMass spectrometryen
dc.subjectMolecular biologyen
dc.subjectStatistical testsen
dc.subjectPeptidesen
dc.subjectpeptideen
dc.subjectproteinen
dc.subjectalgorithmen
dc.subjectchemistryen
dc.subjectmass spectrometryen
dc.subjectmethodologyen
dc.subjectproteomicsen
dc.subjectregression analysisen
dc.subjectsequence analysisen
dc.subjectsupport vector machineen
dc.subjectAlgorithmsen
dc.subjectLeast-Squares Analysisen
dc.subjectMass Spectrometryen
dc.subjectPeptidesen
dc.subjectProteinsen
dc.subjectProteomicsen
dc.subjectRegression Analysisen
dc.subjectSequence Analysis, Proteinen
dc.subjectSupport Vector Machinesen
dc.titlePrediction of peptide drift time in ion mobility mass spectrometry from sequence-based featuresen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalBMC Bioinformaticsen
dc.identifier.pmcidPMC3654891en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionAdvanced Research Institute of Intelligent Sensing Network, Tongji University, Shanghai 201804, Chinaen
dc.contributor.institutionKey Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, Chinaen
dc.contributor.institutionSchool of Electronics and Information Engineering, Tongji University, Shanghai 201804, Chinaen
dc.contributor.institutionSchool of Electronic Engineering and Automation, Anhui University, Hefei, Anhui 230601, Chinaen
dc.contributor.institutionDepartment of Medicine, University of Louisville, Louisville, KY 40202, United Statesen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorChen, Pengen

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