Abstract
Protein identification by mass spectrometry (MS) is an important technique in proteomics. By searching an MS spectrum against a given protein database, the most matched proteins are sorted using a scoring function and the top one is often considered the correctly identified protein. Peptide mass fingerprinting (PMF) is one of the major methods for protein identification using MS technology. It is faster and cheaper than the other popular technique - Tandem Mass Spectrometry. Key bioinformatics issues in PMF analysis include designing a scoring function to quantitatively measure the degree of consistency between a PMF spectrum and a protein sequence and assessing the confidence of identified proteins. In this chapter, we will introduce several scoring functions that were developed by others and us. We will also provide a new statistic model to evaluate the confidence of the score and make an improvement for ranking proteins in protein identification. Our developments have been implemented in a software package “ProteinDecision,” which is available at http://digbio.missouri.edu/ProteinDecision/.
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Acknowledgments
This work has been supported by the MU-Monsanto Program. Zhao Song is also supported by the Shumaker Fellowship. The authors like to acknowledge the Proteomics Center at the University of Missouri-Columbia for MS services. We would like to thank Beverly DaGue, Brian Mooney, David Emerich, Gary Stacey, Jay Thelen, Chi-Ren Shyu, Guohui Lin, Chao Zhang, Yu Chen, Yang Guo, and Zhihai He for helpful discussions and/or technical assistance.
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Song, Z., Chen, L., Xu, D. (2010). Bioinformatics Methods for Protein Identification Using Peptide Mass Fingerprinting. In: Hubbard, S., Jones, A. (eds) Proteome Bioinformatics. Methods in Molecular Biology™, vol 604. Humana Press. https://doi.org/10.1007/978-1-60761-444-9_2
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DOI: https://doi.org/10.1007/978-1-60761-444-9_2
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