Modeling and prediction of peptide drift times in ion mobility spectrometry using sequence-based and structure-based approaches

Computers in Biology and Medicine

Volumn 41, Issue 5, 2011, Pages 272-277

  Zhang, Yiming ^a   Jin, Quan ^a   Wang, Shuting ^a   Ren, Ren ^a  

^a HANGZHOU CENTER FOR DISEASE CONTROL AND PREVENTION   (China)

Author keywords

Drift time; Ion mobility spectrometry; Peptide; Quantitative structure spectrum relationship

Indexed keywords

AMINO ACID COMPOSITIONS; CHARACTERIZATION METHODS; CROSS VALIDATION; CROSS-VALIDATIONS; DRIFT TIME; DROSOPHILA MELANOGASTER; GAUSSIAN PROCESS; INTERPRETABILITY; ION MOBILITY SPECTROMETRY; LOCAL ENVIRONMENTS; MOBILE BEHAVIOR; MODELING METHOD; MONTE CARLO; NONLINEAR SUPPORT VECTOR MACHINES; PARTIAL LEAST-SQUARES REGRESSION; PREDICTIVE POWER; PROTEASE DIGESTION; PROTEOMES; QUANTITATIVE STRUCTURES; STRUCTURAL PARAMETER; STRUCTURE-BASED;

AMINO ACIDS; CHARACTERIZATION; IONS; SPECTROMETRY;

PEPTIDES;

PROTEINASE;

AMINO ACID COMPOSITION; ARTICLE; DROSOPHILA MELANOGASTER; ION MOBILITY SPECTROMETRY; KERNEL METHOD; MONTE CARLO METHOD; PARTIAL LEAST SQUARES REGRESSION; PREDICTIVE VALUE; PRIORITY JOURNAL; PROTEIN STRUCTURE; SUPPORT VECTOR MACHINE;

ALGORITHMS; ANIMALS; COMPUTATIONAL BIOLOGY; DROSOPHILA MELANOGASTER; IONS; LEAST-SQUARES ANALYSIS; MODELS, STATISTICAL; MONTE CARLO METHOD; NORMAL DISTRIBUTION; PEPTIDES; REGRESSION ANALYSIS; SEQUENCE ANALYSIS, PROTEIN; SPECTRUM ANALYSIS;

DROSOPHILA MELANOGASTER;

EID: 79955024080     PISSN: 00104825     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2011.03.002     Document Type: Article

References (35)

1. Verbeck G.F., Ruotolo B.T., Sawyer H.A., Gillig K.J., Russell D.H. A fundamental introduction to ion mobility mass spectrometry applied to the analysis of biomolecules. J. Biomol. Tech. 2002, 13:56-61.
2. Bohrer B.C., Merenbloom S.I., Koeniger S.L., Hilderbrand A.E., Clemmer D.E. Biomolecule analysis by ion mobility spectrometry. Ann. Rev. Anal. Chem. 2008, 1:10.1-10.35.
3. Collins D.C., Lee M.L., Fresenius J. Electrospray ionization gas-phase electrophoresis under ambient conditions and its potential or high-speed separations. Anal. Chem. 2001, 369:225-233.
4. Myung S., Lee Y.J., Moon M.H., Taraszka J., Sowell R., Koeniger S., Hilderbrand A.E., Valentine S.J., Cherbas L., Cherbas P., Kaufmann T.C., Miller D.F., Mechref Y., Novotny M.V., Ewing M.A., Sporleder C.R., Clemmer D.E. Development of high-sensitivity ion trap ion mobility spectrometry time-of-flight techniques: a high-throughput nano-LC-IMS-TOF separation of peptides arising from a Drosophila protein extract. Anal. Chem. 2003, 75:5137-5145.
5. McLean J.A., Russell D.H. Sub-femtomole peptide detection in ion mobility-time-of-flight mass spectrometry measurements. J. Proteome Res. 2003, 2:427-430.
6. Dwivedi P., Wu P., Klopsch S.J., Puzon G.J., Xun L., Hill H.H. Metabolic profiling by ion mobility mass spectrometry (IMMS). Metabolomics 2008, 4:63-80.
7. Shvartsburg A.A., Jarrold M.F. An exact hard-spheres scattering model for the mobilities of polyatomic ions. Chem. Phys. Lett. 1996, 261:86-91.
8. Revercomb H.E., Mason E.A. Theory of plasma chromatography/gaseous electrophoresis--a review. Anal. Chem. 1975, 47:970-983.
9. Counterman A.E., Clemmer D.E. Volumes of individual amino acid residues in gas-phase peptide ions. J. Am. Chem. Soc. 1999, 121:4031-4039.
10. Zhou P., Zeng H., Tian F., Li B., Li Z. Applying novel molecular electronegativity-interaction vector (MEIV) to QSPR study on collision cross section of singly protonated peptides. QSAR Comb. Sci. 2007, 26:117-121.
11. Zhou P., Tian F., Li Z. Quantitative structure-property relationship studies for collision cross sections of 579 singly protonated peptides based on a novel descriptor as molecular graph fingerprint (MoGF). Anal. Chim. Acta 2007, 597:214-222.
12. Valentine S.J., Counterman A.E., Hoaglund-Hyzer C.S., Clemmer D.E. Intrinsic amino acid size parameters from a series of 113 lysine-terminated tryptic digest peptide ions. J. Phys. Chem. B 1999, 103:1203-1207.
13. Shvartsburg A.A., Siu K.W.M. Prediction of peptide ion mobilities via a priori calculations from intrinsic size parameters of amino acid residues. J. Am. Soc. Mass Spectrom. 2001, 12:885-888.
14. Mosier P.D., Counterman A.E., Jurs P.C. Prediction of peptide ion collision cross sections from topological molecular structure and amino acid parameters. Anal. Chem. 2002, 74:1360-1370.
15. Liu X., Liang J., Fan J., Shang Z. Prediction of ion drift times for a proteome-wide peptide set using partial least squares regression, least-squares support vector machine and gaussian process. QSAR Combinatorial Sci. 2009, 28:1386-1393.
16. Shah A.R., Agarwal K., Baker E.S., Singha M., Mayampurath A.M., Ibrahim Y.M., Kangas L.J., Monroe M.E., Zhao R., Belov M.E., Anderson G.A., Smith R.D. Machine learning based prediction for peptide drift times in ion mobility spectrometry. Bioinformatics 2010, 26:1601-1607.
17. Zhou P., Tian F., Wu Y., Li Z., Shang Z. Quantitative sequence-activity model (QSAM): applying QSAR strategy to model and predict bioactivity and function of peptides, proteins and nucleic acids. Curr. Comput. Aided Drug Des. 2008, 4:311-321.
18. Zhou P., Tian F., Lv F., Shang Z. Comprehensive comparison of eight statistical modelling methods used in quantitative structure-retention relationship studies for liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome. J. Chromatogr. A 2009, 1216:3107-3116.
19. Tian F., Zhou P., Li Z. T-scale as a novel vector of topological descriptors for amino acids and its application in QSARs of peptides. J. Mol. Struct. 2007, 830:106-115.
20. Du H., Zhang X., Wang J., Yao X., Hu Z. Novel approaches to predict the retention of histidine-containing peptides in immobilized metal-affinity chromatography. Proteomics 2008, 8:2185-2195.
21. Tian F., Yang L., Lv F., Zhou P. Modeling and prediction of retention behavior of histidine-containing peptides in immobilized metal-affinity chromatography. J. Sep. Sci. 2009, 32:2159-2169.
22. Katritzky A.R., Lobanov V.S., Karelson M. QSPR: the correlation and quantitative prediction of chemical and physical properties from structure. Chem. Soc. Rev. 1995, 24:279-287.
23. Wold S., Sjöström M., Eriksson L. PLS regression: a basic tool of chemometrics. Chemometrics Intell. Lab. Syst. 2001, 58:109-130.
24. Cortes C., Vapnik V. Support-vector networks. Mach. Learn. 1995, 20:273-297.
25. Rasmussen C.E., Williams C.K.I. Gaussian Processes for Machinelearning 2006, MIT Press, MA.
26. Tian F., Yang L., Lv F., Zhou P. Predicting liquid chromatographic retention times of peptides from the Drosophila melanogaster proteome by machine learning approach. Anal. Chim. Acta 2009, 644:10-16.
27. Obrezanova O., Csanyi G., Gola J.M.R., Segall M.D. Gaussian processes: a method for automatic QSAR modeling of ADME properties. J. Chem. Inf. Model. 2007, 47:1847-1857.
28. Zhou P., Tian F., Chen X., Shang Z. Modeling and prediction of binding affinities between the human amphiphysin SH3 domain and its peptide ligands using genetic algorithm-Gaussian processes. Biopolymers (Pept. Sci.) 2008, 90:792-802.
29. Ganeshanandam S., Krzanowski W.J. On selecting variables and assessing their performance in linear discriminant analysis. Aust. J. Stat. 1989, 31:433-447.
30. Krzanowski W.J. Selection of variables, and assessment of their performance, in mixed-variable discriminant analysis. Comput. Stat. Data Anal. 1995, 19:419-431.
31. Jonathan P., Krzanowski W.J., McCarthy W.V. On the use of cross-validation to assess performance in multivariate prediction. Stat. Comput. 2000, 10:209-229.
32. Xu Q.S., Liang Y.Z. Monte Carlo cross validation. Chemometrics Intell. Lab. Syst. 2001, 56:1-11.
33. Manchester J., Czerminski R. SAMFA: simplifying molecular description for 3D-QSAR. J. Chem. Inf. Model. 2008, 48:1167-1173.
34. Manchester J., Czerminski R. CAUTION: popular "benchmark" data sets do not distinguish the merits of 3D QSAR methods. J. Chem. Inf. Model 2009, 49:1449-1454.
35. Taraszka J.A., Kurulugama R., Sowell R.A., Valentine S.J., Koeniger S.L., Arnold R.J., Miller D.F., Kaufman T.C., Clemmer D.E. Mapping the proteome of Drosophila melanogaster: analysis of embryos and adult heads by LC-IMS-MS methods. J. Proteome Res. 2005, 4:1223-1237.