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What are Signature Peptides

What are Signature Peptides?

Signature peptides are unique tags or biomarkers, detected as molecular markers or as unique sequence tags. Signature peptides are useful tools for biomarker discovery and measurements.

Proteomic research involves the large-scale study of proteins in living organism. One important area of proteomics is the quantitative determinations of the protein content at a certain developmental or disease stage of an organism, including the human proteome. For this, absolute quantification is needed. Recent advancements made in mass spectrometry-based technologies has now enabled targeted protein quantification. However, many proteomic studies report only relative quantification and many methods for relative quantification now exist.

Absolute quantification is needed for biomarker analysis and system biology research. Typically quantitative proteomic approaches involve mass spectrometric determination of signature peptides which are usually enzymatically derived together with their isotope-labeled analogs. In general, tryptic peptides of target proteins are used. Unique peptide sequences are important for protein identification and selected signature peptides can be used as peptide or protein biomarkers.

A web tool called Unimap allows in-silico searching for signature peptides to find

(i)    a given molecular mass that is a unique molecular mass present or found in
       one human protein,

(ii)  a given peptide sequence or sequences found exclusively in one human protein,
       and 

(iii)  a specific protein for which unique masses or peptide sequences exist.

Already many novel protein candidates associated with various diseases have been identified. But because of the complexity of biological systems, the heterogeneity of human samples, and the lack of universal standardized quantitative technologies, biomarker validations have been challenging.

The human genome sequencing project has transformed biomedical research in the last decade. Also, a draft map of the human proteome was published in 2004 (Kim et al.). Proteomic profiling of 30 histological normal human samples resulted in the identification of 30,057 proteins encoded by 17,294 genes. A large number of peptides sequences were identified. These genes accounted for approximately 84% of the total annotated protein-coding genes in humans. The resulting peptide data is available as an interactive web-based resource. This data set is thought to complement human genome and transcriptome research which hopefully accelerates biomedical research in health and disease possibly leading to new and better therapeutic approaches.

For the experimental identification of signature peptides, data-dependent mass spectrometry experiments are performed. Different mass spectrometry platforms or workflows can be used. A typical setup consists of an on-line nanoLC chromatography system coupled to a mass spectrometer. The following platforms are examples: micro- or nano-LC systems coupled to Orbitrap type mass spectrometers (Thermo), to QTOF mass spectrometers (Agilent and Waters), to ion-trap mass spectrometers (Bruker), to TripleTOF mass spectrometers (Sciex), or to  MALDI-TOF/TOFs (ABI) or similar MALDI-MS instruments.

An example of this approach is the empirical peptide selection work flow for robust protein quantification reported by Fu et al. in 2015 (on-line publication). The research group compared the relative SRM signal intensity of 12 uromodulin-derived peptides between tryptic digests of 9 urine samples. Absolute quantification was performed using stable isotope–labeled peptides as internal standards. A standard curve needed to be prepared from a tryptic digest of purified uromodulin. The research group showed that the comparison of the peptide abundance of several peptides derived from the same target protein allows selection of signature peptides to detect and quantify proteins in biological samples, in this case, uromodulin. Also, the research group showed that one cannot take shortcuts in peptide selection if the development of a robust assay is desired.  

Uromodulin, UMOD or Tamm-Horsfall glycoprotein, was selected because it is the most abundant protein in healthy human urine. The uromodulin protein is encoded by the UMOD gene. Under physiological conditions, uromodulin is the most abundant protein in the mammalian urine. Uromodulin is thought to act as an inhibitor of calcium crystallization in renal fluids and its excretion in urine provides defense against urinary tract infections caused by uropathogenic bacteria. Gene defects of the UMOD gene are associated with the renal disorders medullary cystic kidney disease-2 (MCKD2), glomerulocystic kidney disease with hyperuricemia and isosthenuria (GCKDHI), and familial juvenile hyperuricemic nephropathy (FJHN). The gene is alternatively spliced. 

Fu et al. argue that exact quantification of urinary uromodulin can act as a biomarker for susceptibility to chronic kidney disease and hypertension. Uromodulin signature peptides can be potentially used as future diagnostic biomarkers for monitoring blood pressure-lowering treatments.


Uromodulin signature peptides selected by Fu et al. (2016) as biomarker peptides.

 

Peptide

Sequence

M/z mono

M/z average

DWVSV

DWVSVVTPAR

[M]    1,128.59280

[M+H]+  1,129.60008

[M]    1,129.28105

[M+H]+ 1,130.28832

YFIIQ

YFIIQDR

[M]   953.49711

[M+H]+  954.50439

[M]  954.09353

[M+H]+  955.10080

TLDEY

TLDEYWR

[M]   981.45564

[M+H]+  982.46291

[M]  982.06056

[M+H]+ 983.06784

FVGQG

FVGQGGAR

[M]   790.40863

[M+H]+  791.41591

[M]   790.87720

[M+H]+  791.88448


M/z values were calculated with the fragment ion calculator from the proteomicsToolkit
However, a researcher should always check experimentally how these peptides are detected in each individual mass spectrometer system used for the analysis. Methionine containing peptides where excluded because different levels of oxidation were observed during the study. According to Fu et al. purification of the digested peptides on an HLB microplate gave the best recoveries as validated with stable isotope-labeled peptides. The “Oasis HLB” resin from Waters consists of a strongly hydrophilic, water-wettable polymer with a unique hydrophilic-lipophilic balance. A typical workflow for the identification of signature biomarker peptides is shown below.

A Typical Peptide Selection Workflow

Theoretical = “in-silico”

In silico digestion.

Select peptides with 6 to 21 amino acids.

Identify constrained peptides for PTM and isoforms.

Eliminate peptides with methionines and cysteines.

Empirical and Experimental

Optimize trypsin digestion and peptide cleanup.

Assay 10 to 20 peptides by SRM in 10 to 20 biological samples.

Correlate (r2) peak areas for all pairs of peptides.

Select peptides with high correlation, strong signals, high signal to noise ratio, and sequences unique to the protein of interest.

Quantitative Assay

Synthesize or purchase 15N-labled internal standard peptides.

Optimize LC and SRM parameters.

Determine LLDQ and ULOQ with purified recombinant proteins.

Determine reproducibility.

Evaluate recovery.

Abbreviations: SRM,  selected reaction monitoring; MS, mass spectrometry; ARIC, Atherosclerosis Risk in Communities; SIL, stable isotope–labeled; LLOQ, lower limit of quantification.

Reference

Anastasia Alexandridou, George Th. Tsangaris, Konstantinos Vougas, Konstantina Nikita, George Spyrou; UniMaP: finding unique mass and peptide signatures in the human proteome. Bioinformatics 2009; 25 (22): 3035-3037. doi: 10.1093/bioinformatics/btp516.

Fu, Qin, Grote, Eric, Zhu, Jie, Jelinek, Christine, Köttgen, Anna, Coresh, Josef, Van Eyk, Jennifer E.; An Empirical Approach to Signature Peptide Choice for Selected Reaction Monitoring: Quantification of Uromodulin in Urine. Clinical Chemistry 2016, 62, 1, 198-207. http://clinchem.aaccjnls.org/content/62/1/198.abstract

Geng M, Ji J, Regnier FE.; Signature-peptide approach to detecting proteins in complex mixtures. J Chromatogr A. 2000 Feb 18;870(1-2):295-313.

Grant RP, Hoofnagle AN. From lost in translation to paradise found: Enabling protein biomarker method transfer by mass spectrometry. Clin Chem 2014;60:941-4.

Kim MS, Pinto SM, Getnet D et al., A draft map of the human proteome. Nature. 2014 May 29;509(7502):575-81. doi: 10.1038/nature13302.

Lee JW, Devanarayan V, Barrett YC, Weiner R, Allinson J, Fountain S, et al. Fit-for-purpose method development and validation for successful biomarker measurement. Pharmaceutical research 2006;23:312-28.

MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B, et al. Skyline: An open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 2010;26:966-8.

Sheng Pan, Ruedi Aebersold, Ru Chen, John Rush, David R. Goodlett, Martin W. McIntosh, Jing Zhang, and Teresa A. Brentnall; Mass spectrometry based targeted protein quantification: methods and applications.  J Proteome Res. 2009 February ; 8(2): 787–797. doi:10.1021/pr800538n

Uromodulin gene info: https://www.ncbi.nlm.nih.gov/gene/7369.