Skip to main content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

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

Published

Author(s)

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

Keywords

Genetically Variant Peptide, hair protein extraction, cuticular keratins, peptide mass spectral library, trace detection
Created October 31, 2019, Updated January 7, 2020