Abstract
In current proteome research, the most widely used method for protein mixture identification is probably peptide sequencing. Peptide sequencing is based on tandem Mass Spectrometry (MS/MS) data. The disadvantage is that MS/MS data only sequences a limited number of peptides and leaves many more peptides uncovered.
Peptide Mass Fingerprinting (PMF) has been widely used to identify single purified proteins from single-stage MS data. Unfortunately, this technique is less accurate than the peptide sequencing method and can not handle protein mixtures, which hampers the widespread use of PMF.
In this paper, we tackle the problem of protein mixture identification from an optimization point of view. We show that some simple heuristics can find good solutions to the optimization problem. As a result, we obtain much better identification results than previous methods. Through a comprehensive simulation study, we identify a set of limiting factors that hinder the performance of PMF-based protein mixture identification. We argue that it is feasible to remove these limitations and PMF can be a powerful tool in the analysis of protein mixtures, especially in the identification of low-abundance proteins which are less likely to be sequenced by MS/MS scanning.
Availability: The source codes, data and supplementary documents are available at http://bioinformatics.ust.hk/PMFMixture.rar
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Yates, J.R., Speicher, S., Griffin, P.R., Hunkapiller, T.: Peptide mass maps: a highly informative approach to protein identification. Anal. Biochem. 214(2), 297–408 (1993)
Perkins, D.N., Pappin, D.J.C., Creasy, D.M., Cottrell, J.S.: Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20(18), 3551–3567 (1999)
Clauser, K.R., Baker, P., Burlingame, A.L.: Role of accurate mass measurement (±10 ppm) in protein identification strategies employing MS or MS/MS and database searching. Anal. Chem. 71(14), 2871–2882 (1999)
Zhang, W., Chait, B.T.: Profound: an expert system for protein identification using mass spectrometric peptide mapping information. Anal. Chem. 72(11), 2482–2489 (2000)
Aebersold, R., Mann, M.: Mass spectrometry-based proteomics. Nature 422, 198–207 (2003)
Jensen, O.N., Podtelejnikov, A.V., Mann, M.: Identification of the components of simple protein mixtures by high-accuracy peptide mass mapping and database searching. Anal. Chem. 69(23), 4741–4750 (1997)
Park, Z.Y., Russell, D.H.: Identification of individual proteins in complex protein mixtures by high-resolution,high-mass-accuracy MALDI TOF-mass spectrometry analysis of in-solution thermal denaturation/enzymatic digestion. Anal. Chem. 73(11), 2558–2564 (2001)
Eriksson, J., Fenyö, D.: Protein identification in complex mixtures. J. Proteome Res. 4(2), 387–393 (2005)
Lu, B., Motoyama, A., Ruse, C., Venable, J., Yates, J.R.: Improving protein identification sensitivity by combining MS and MS/MS information for shotgun proteomics using LTQ-Orbitrap high mass accuracy data. Anal. Chem. 80(6), 2018–2025 (2008)
Samuelsson, J., Dalevi, D., Levander, F., Rögnvaldsson, T.: Modular, scriptable and automated analysis tools for high-throughput peptide mass fingerprinting. Bioinformatics 20(18), 3628–3635 (2004)
Monroe, M.E., Tolic, N., Jaitly, N., Shaw, J.L., Adkins, J.N., Smith, R.D.: VIPER: an advanced software package to support high-throughput LC-MS peptide identification. Bioinformatics 23(15), 2021–2023 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
He, Z., Yang, C., Yang, C., Qi, R.Z., Tam, J.PM., Yu, W. (2009). Optimization-Based Peptide Mass Fingerprinting for Protein Mixture Identification. In: Batzoglou, S. (eds) Research in Computational Molecular Biology. RECOMB 2009. Lecture Notes in Computer Science(), vol 5541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02008-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-02008-7_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02007-0
Online ISBN: 978-3-642-02008-7
eBook Packages: Computer ScienceComputer Science (R0)