Êtes-vous un étudiant de l'EPFL à la recherche d'un projet de semestre?
Travaillez avec nous sur des projets en science des données et en visualisation, et déployez votre projet sous forme d'application sur Graph Search.
Site-specific transcription factors (TFs) play an essential role in mammalian development and function as they are vital for the majority of cellular processes. Despite their biological importance, TF proteomic data is scarce in the literature, likely due to difficulties in detecting peptides as the abundance of TFs in cells tends to be low. In recent years, significant improvements in MS-based technologies in terms of sensitivity and specificity have increased the interest in developing quantitative methodologies specifically targeting relatively lowly abundant proteins such as TFs in mammalian models. Such efforts would be greatly aided by the availability of TF peptide-specific information as such data would not only enable improvements in speed and accuracy of protein identifications, but also ameliorate cross-comparisons of quantitative proteomics data and allow for a more efficient development of targeted proteomics assays. However, to date, no comprehensive TF proteotypic peptide database has been developed. To address this evident lack of TF peptide data in public repositories, we are generating a comprehensive, experimentally derived TF proteotypic peptide spectral library dataset based on in vitro protein expression. Our library currently contains peptide information for 89 TFs and this number is set to increase in the near future. All MS data have been deposited in the ProteomeXchange with identifier PXD001212 (http://proteomecentral.proteomexchange.org/dataset/PXD001212).
Suliana Manley, Beat Fierz, David Michael Suter, Christian Sieben, Aleksandr Benke, Andrea Callegari