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Optimizing peptide yields from human hair
Author(s) -
Steenstra Joshua Adam,
Clifford Patricia,
Parker Glendon
Publication year - 2012
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.26.1_supplement.776.11
Subject(s) - sample (material) , absorbance , computer science , protocol (science) , identity (music) , peptide , degree (music) , function (biology) , yield (engineering) , biological system , chromatography , chemistry , biology , biochemistry , materials science , microbiology and biotechnology , physics , medicine , alternative medicine , pathology , acoustics , metallurgy
Hair is physically robust and persists in the environment for long periods. It is a common component of crime scenes, yet it is not often analyzed because the DNA information is absent or difficult to obtain. Hair, a complex biological connection between the crime scene and the suspect, is now only rarely used in crime scene investigation. This project seeks to examine the use of protein in developing a measure of identity, we currently have a power of discrimination of 1 in 15,500. The degree of identity between an individual and a hair sample is a function of proteomic information obtained. The purpose and question of our research is to determine if there is a more efficient way to optimize protein yields from hair. To maximize the surface area to volume ratio, a hair is ground into powder. The sample is then digested and a fraction of the sample is then loaded onto a HPLC and a profile is measured, both total absorbance and peak complexity. This corresponds to how many different polypeptides and how much polypeptide is present in the sample. A range of conditions will be tested to optimize the protocol. We have already determined that mass spectrophotometry compatible detergents increase peptide yield. We anticipate developing a robust protocol that optimizes peptide yields from hair samples. This will maximize the amount of information from each sample to achieve the greatest attainable degree of identity that is consistently reliable. This methodology will prove useful in optimizing the degree of information extracted from forensically relevant material.