Your browser doesn't support javascript.
loading
Selecting Optimal Peptides for Targeted Proteomic Experiments in Human Plasma Using In Vitro Synthesized Proteins as Analytical Standards.
Bollinger, James G; Stergachis, Andrew B; Johnson, Richard S; Egertson, Jarrett D; MacCoss, Michael J.
Afiliación
  • Bollinger JG; Department of Genome Sciences, University of Washington, Foege Building S-113B, 3720 15th Avenue, NE, 355065, Seattle, WA, 98195-5065, USA.
  • Stergachis AB; Department of Genome Sciences, University of Washington, Foege Building S-113B, 3720 15th Avenue, NE, 355065, Seattle, WA, 98195-5065, USA.
  • Johnson RS; Department of Genome Sciences, University of Washington, Foege Building S-113B, 3720 15th Avenue, NE, 355065, Seattle, WA, 98195-5065, USA.
  • Egertson JD; Department of Genome Sciences, University of Washington, Foege Building S-113B, 3720 15th Avenue, NE, 355065, Seattle, WA, 98195-5065, USA.
  • MacCoss MJ; Department of Genome Sciences, University of Washington, Foege Building S-113B, 3720 15th Avenue, NE, 355065, Seattle, WA, 98195-5065, USA. maccoss@u.washington.edu.
Methods Mol Biol ; 1410: 207-21, 2016.
Article en En | MEDLINE | ID: mdl-26867746
ABSTRACT
In targeted proteomics, the development of robust methodologies is dependent upon the selection of a set of optimal peptides for each protein-of-interest. Unfortunately, predicting which peptides and respective product ion transitions provide the greatest signal-to-noise ratio in a particular assay matrix is complicated. Using in vitro synthesized proteins as analytical standards, we report here an empirically driven method for the selection of said peptides in a human plasma assay matrix.
Asunto(s)
Palabras clave

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Proteómica Idioma: En Revista: Methods Mol Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2016 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Proteómica Idioma: En Revista: Methods Mol Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2016 Tipo del documento: Article