Optimization-Based Peptide Mass Fingerprinting for Protein Mixture Identification
Part of the
Lecture Notes in Computer Science
book series (LNCS, volume 5541)
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
KeywordsSequence Coverage Single Protein Protein Mixture Mass Error Peptide Mass Fingerprint
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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)CrossRefGoogle Scholar
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)CrossRefPubMedGoogle Scholar
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)CrossRefPubMedGoogle Scholar
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)CrossRefPubMedGoogle Scholar
Aebersold, R., Mann, M.: Mass spectrometry-based proteomics. Nature 422, 198–207 (2003)CrossRefPubMedGoogle Scholar
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)CrossRefPubMedGoogle Scholar
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)CrossRefPubMedGoogle Scholar
Eriksson, J., Fenyö, D.: Protein identification in complex mixtures. J. Proteome Res. 4(2), 387–393 (2005)CrossRefPubMedGoogle Scholar
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)CrossRefPubMedPubMedCentralGoogle Scholar
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)CrossRefPubMedGoogle Scholar
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)CrossRefPubMedGoogle Scholar
© Springer-Verlag Berlin Heidelberg 2009