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A high confidence, manually validated human blood plasma protein reference set.

Schenk S, Schoenhals GJ, de Souza G, Mann M - BMC Med Genomics (2008)

Bottom Line: Both instruments allow the measurement of peptide masses in the low ppm range.Furthermore, we employed a statistical score that allows database peptide identification searching using the products of two consecutive stages of tandem mass spectrometry (MS3).The combination of MS3 with very high mass accuracy in the parent peptide allows peptide identification with orders of magnitude more confidence than that typically achieved.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biochemistry and Molecular Biology, Bioinformatics, University of Southern Denmark, Campusvej 55, 5230 Odense M,Denmark. sschenk@bmb.sdu.dk

ABSTRACT

Background: The immense diagnostic potential of human plasma has prompted great interest and effort in cataloging its contents, exemplified by the Human Proteome Organization (HUPO) Plasma Proteome Project (PPP) pilot project. Due to challenges in obtaining a reliable blood plasma protein list, HUPO later re-analysed their own original dataset with a more stringent statistical treatment that resulted in a much reduced list of high confidence (at least 95%) proteins compared with their original findings. In order to facilitate the discovery of novel biomarkers in the future and to realize the full diagnostic potential of blood plasma, we feel that there is still a need for an ultra-high confidence reference list (at least 99% confidence) of blood plasma proteins.

Methods: To address the complexity and dynamic protein concentration range of the plasma proteome, we employed a linear ion-trap-Fourier transform (LTQ-FT) and a linear ion trap-Orbitrap (LTQ-Orbitrap) for mass spectrometry (MS) analysis. Both instruments allow the measurement of peptide masses in the low ppm range. Furthermore, we employed a statistical score that allows database peptide identification searching using the products of two consecutive stages of tandem mass spectrometry (MS3). The combination of MS3 with very high mass accuracy in the parent peptide allows peptide identification with orders of magnitude more confidence than that typically achieved.

Results: Herein we established a high confidence set of 697 blood plasma proteins and achieved a high 'average sequence coverage' of more than 14 peptides per protein and a median of 6 peptides per protein. All proteins annotated as belonging to the immunoglobulin family as well as all hypothetical proteins whose peptides completely matched immunoglobulin sequences were excluded from this protein list. We also compared the results of using two high-end MS instruments as well as the use of various peptide and protein separation approaches. Furthermore, we characterized the plasma proteins using cellular localization information, as well as comparing our list of proteins to data from other sources, including the HUPO PPP dataset.

Conclusion: Superior instrumentation combined with rigorous validation criteria gave rise to a set of 697 plasma proteins in which we have very high confidence, demonstrated by an exceptionally low false peptide identification rate of 0.29%.

No MeSH data available.


Related in: MedlinePlus

Histogram showing the number of proteins identified versus the number of validated, non-redundant peptides found for each protein, across all experiments. The number of validated, non-redundant peptides used to identify each protein was calculated and the proteins with identical numbers of peptides were plotted in the same group, indicated on the X-axis. For example, there are a total of 70 proteins that were identified with a single peptide. The inset depicts in detail the number of proteins that were identified with 1–20 peptides. Proteins identified with more than 20 peptides were categorized into groups as indicated.
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Figure 1: Histogram showing the number of proteins identified versus the number of validated, non-redundant peptides found for each protein, across all experiments. The number of validated, non-redundant peptides used to identify each protein was calculated and the proteins with identical numbers of peptides were plotted in the same group, indicated on the X-axis. For example, there are a total of 70 proteins that were identified with a single peptide. The inset depicts in detail the number of proteins that were identified with 1–20 peptides. Proteins identified with more than 20 peptides were categorized into groups as indicated.

Mentions: By combining the eight plasma experiments as listed in Table 1 and after removal of redundancy, we identified 1193 distinct proteins with a valid primary accession number. The combination of rigorous data validation coupled with the removal of immunoglobulin-related proteins for separate analysis reduced this number to 697, which we refer to as our 'stringently validated high confidence protein set'. 70 of these 697 proteins were identified with 1 peptide (Figure 1) and 84 proteins were identified with exactly 2 peptides. The remaining 541 proteins were identified with 3 or more peptides. 314 proteins were identified with 3–10 peptides and 229 proteins had 11 or more peptides. 12 proteins were identified with over 100 peptides (Table 2). The protein identified with the highest number of peptides was apolipoprotein B-100 (505 validated non-redundant peptides), while albumin, a protein known to be extremely abundant in blood plasma, was identified with 94 validated, non-redundant peptides.


A high confidence, manually validated human blood plasma protein reference set.

Schenk S, Schoenhals GJ, de Souza G, Mann M - BMC Med Genomics (2008)

Histogram showing the number of proteins identified versus the number of validated, non-redundant peptides found for each protein, across all experiments. The number of validated, non-redundant peptides used to identify each protein was calculated and the proteins with identical numbers of peptides were plotted in the same group, indicated on the X-axis. For example, there are a total of 70 proteins that were identified with a single peptide. The inset depicts in detail the number of proteins that were identified with 1–20 peptides. Proteins identified with more than 20 peptides were categorized into groups as indicated.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2563020&req=5

Figure 1: Histogram showing the number of proteins identified versus the number of validated, non-redundant peptides found for each protein, across all experiments. The number of validated, non-redundant peptides used to identify each protein was calculated and the proteins with identical numbers of peptides were plotted in the same group, indicated on the X-axis. For example, there are a total of 70 proteins that were identified with a single peptide. The inset depicts in detail the number of proteins that were identified with 1–20 peptides. Proteins identified with more than 20 peptides were categorized into groups as indicated.
Mentions: By combining the eight plasma experiments as listed in Table 1 and after removal of redundancy, we identified 1193 distinct proteins with a valid primary accession number. The combination of rigorous data validation coupled with the removal of immunoglobulin-related proteins for separate analysis reduced this number to 697, which we refer to as our 'stringently validated high confidence protein set'. 70 of these 697 proteins were identified with 1 peptide (Figure 1) and 84 proteins were identified with exactly 2 peptides. The remaining 541 proteins were identified with 3 or more peptides. 314 proteins were identified with 3–10 peptides and 229 proteins had 11 or more peptides. 12 proteins were identified with over 100 peptides (Table 2). The protein identified with the highest number of peptides was apolipoprotein B-100 (505 validated non-redundant peptides), while albumin, a protein known to be extremely abundant in blood plasma, was identified with 94 validated, non-redundant peptides.

Bottom Line: Both instruments allow the measurement of peptide masses in the low ppm range.Furthermore, we employed a statistical score that allows database peptide identification searching using the products of two consecutive stages of tandem mass spectrometry (MS3).The combination of MS3 with very high mass accuracy in the parent peptide allows peptide identification with orders of magnitude more confidence than that typically achieved.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biochemistry and Molecular Biology, Bioinformatics, University of Southern Denmark, Campusvej 55, 5230 Odense M,Denmark. sschenk@bmb.sdu.dk

ABSTRACT

Background: The immense diagnostic potential of human plasma has prompted great interest and effort in cataloging its contents, exemplified by the Human Proteome Organization (HUPO) Plasma Proteome Project (PPP) pilot project. Due to challenges in obtaining a reliable blood plasma protein list, HUPO later re-analysed their own original dataset with a more stringent statistical treatment that resulted in a much reduced list of high confidence (at least 95%) proteins compared with their original findings. In order to facilitate the discovery of novel biomarkers in the future and to realize the full diagnostic potential of blood plasma, we feel that there is still a need for an ultra-high confidence reference list (at least 99% confidence) of blood plasma proteins.

Methods: To address the complexity and dynamic protein concentration range of the plasma proteome, we employed a linear ion-trap-Fourier transform (LTQ-FT) and a linear ion trap-Orbitrap (LTQ-Orbitrap) for mass spectrometry (MS) analysis. Both instruments allow the measurement of peptide masses in the low ppm range. Furthermore, we employed a statistical score that allows database peptide identification searching using the products of two consecutive stages of tandem mass spectrometry (MS3). The combination of MS3 with very high mass accuracy in the parent peptide allows peptide identification with orders of magnitude more confidence than that typically achieved.

Results: Herein we established a high confidence set of 697 blood plasma proteins and achieved a high 'average sequence coverage' of more than 14 peptides per protein and a median of 6 peptides per protein. All proteins annotated as belonging to the immunoglobulin family as well as all hypothetical proteins whose peptides completely matched immunoglobulin sequences were excluded from this protein list. We also compared the results of using two high-end MS instruments as well as the use of various peptide and protein separation approaches. Furthermore, we characterized the plasma proteins using cellular localization information, as well as comparing our list of proteins to data from other sources, including the HUPO PPP dataset.

Conclusion: Superior instrumentation combined with rigorous validation criteria gave rise to a set of 697 plasma proteins in which we have very high confidence, demonstrated by an exceptionally low false peptide identification rate of 0.29%.

No MeSH data available.


Related in: MedlinePlus