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PUBLICATIONS

Sensitive Method for the Confident Identification of Genetically Variant Peptides in Human Hair

Published

Author(s)

Zheng Zhang, Meghan Burke, William E. Wallace, Yuxue Liang, Sergey L. Sheetlin, Yuri Mirokhin, Dmitrii Tchekhovskoi, Stephen Stein

Abstract

Recent reports have demonstrated that genetically variant peptides (GVPs) derived from human hair shaft proteins can be used to differentiate individuals of different biogeographic origin (Parker, G.J.; et al. PLos One. 2016, e0160653). We report a direct extraction method for hair shaft proteins that is more sensitive than previously published methods regarding to GVP detection (Parker, G.J.; et al. PLos One. 2016, e0160653 and Wong, S.Y.; et al. PLos One. 2016, e0164993). It involves a one-step for protein extraction and was found to provide reproducible results. Together with the construction of a human hair specific peptide mass spectral library including previously reported GVPs, it provides a means of sensitive and reliable analysis of the human hair proteome requiring trace amounts of sample with capability of identifying low abundance GVPs. In this report we also describe a mass-spectral library procedure for the quick and accurate identification of the peptides, including GVPs, in hair protein digests. Also, we show that GVP identification can depend significantly on the sample preparation method and propose several rules for confident identification.
Citation
Journal of Forensic Sciences
Pub Type
Journals

Download Paper

https://doi.org/10.1111/1556-4029.14229
Local Download

Keywords

Genetically Variant Peptide, hair protein extraction, cuticular keratins, peptide mass spectral library, trace detection
Forensic Science and Analytical chemistry

Citation

Zhang, Z. , Burke, M. , Wallace, W. , Liang, Y. , Sheetlin, S. , Mirokhin, Y. , Tchekhovskoi, D. and Stein, S. (2019), Sensitive Method for the Confident Identification of Genetically Variant Peptides in Human Hair, Journal of Forensic Sciences, [online], https://doi.org/10.1111/1556-4029.14229, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=928094 (Accessed October 27, 2025)

Issues

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Created October 30, 2019, Updated February 19, 2020
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