Experience from different fields of life sciences suggests that accessible, complete reference maps of the components of the system under study are highly beneficial research tools. Examples of such maps include libraries of the spectroscopic properties of molecules, or databases of drug structures in analytical or forensic chemistry. Such maps, and methods to navigate them, constitute reliable assays to probe any sample for the presence and amount of molecules contained in the map. So far, attempts to generate such maps for any proteome have failed to reach complete proteome coverage1,2,3. Here we use a strategy based on high-throughput peptide synthesis and mass spectrometry to generate an almost complete reference map (97% of the genome-predicted proteins) of the Saccharomyces cerevisiae proteome. We generated two versions of this mass-spectrometric map, one supporting discovery-driven (shotgun)3,4 and the other supporting hypothesis-driven (targeted)5,6 proteomic measurements. Together, the two versions of the map constitute a complete set of proteomic assays to support most studies performed with contemporary proteomic technologies. To show the utility of the maps, we applied them to a protein quantitative trait locus (QTL) analysis7, which requires precise measurement of the same set of peptides over a large number of samples. Protein measurements over 78 S. cerevisiae strains revealed a complex relationship between independent genetic loci, influencing the levels of related proteins. Our results suggest that selective pressure favours the acquisition of sets of polymorphisms that adapt protein levels but also maintain the stoichiometry of functionally related pathway members.
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This project has been funded in part by ETH Zurich, the Swiss National Science Foundation (3100A0-107679), the National Heart, Lung and Blood Institute, National Institutes of Health (N01-HV-28179), the National Science Foundation MRI (grant 0923536), the Luxembourg Centre for Systems Biomedicine and the University of Luxembourg, and by SystemsX.ch, the Swiss initiative for systems biology. P.P. is supported by a Foerderungsprofessur grant from the Swiss National Science Foundation (PP00P3_133670), by a European Union Seventh Framework Program Reintegration grant (FP7-PEOPLE-2010-RG-277147) and by a Promedica Stiftung (2-70669-11), H. L. is supported by the University Grant Council of the Hong Kong Special Administrative Region Government, China (HKUST DAG08/09.EG02). A.B. is supported by the Klaus Tschira Foundation and by a European Union FP7 HEALTH grant (HEALTH-F4-2008-223539). R.A. is supported by the European Research Council (ERC-2008-AdG 233226) and by SystemsX.ch, the Swiss Initiative for Systems Biology.
This file contains the Supplementary Data used in this study.
About this article
Nature Methods (2017)