Changes in protein conformation can affect protein function, but methods to probe these structural changes on a global scale in cells have been lacking. To enable large-scale analyses of protein conformational changes directly in their biological matrices, we present a method that couples limited proteolysis with a targeted proteomics workflow. Using our method, we assessed the structural features of more than 1,000 yeast proteins simultaneously and detected altered conformations for ~300 proteins upon a change of nutrients. We find that some branches of carbon metabolism are transcriptionally regulated whereas others are regulated by enzyme conformational changes. We detect structural changes in aggregation-prone proteins and show the functional relevance of one of these proteins to the metabolic switch. This approach enables probing of both subtle and pronounced structural changes of proteins on a large scale.

The rigorous testing of hypotheses on suitable sample cohorts is a major limitation in translational research. This is particularly the case for the validation of protein biomarkers where the lack of accurate, reproducible and sensitive assays for most proteins has precluded the systematic assessment of hundreds of potential marker proteins described in the literature. Here, we describe a high throughput method for the development and refinement of selected reaction monitoring (SRM) assays for human proteins. The method was applied to generate such assays for more than 1000 cancer-associated proteins, which are functionally related to candidate cancer driver mutations. We used the assays to determine the detectability of the target proteins in two clinically relevant samples, plasma and urine. 182 proteins were detected in depleted plasma, spanning five orders of magnitude in abundance and reaching below a concentration of 10 ng/mL. The narrower concentration range of proteins in urine allowed the detection of 408 proteins. Moreover, we demonstrate that these SRM assays allow the reproducible quantification of 34 biomarker candidates across 84 patient plasma samples. Through public access to the entire assay library, which will also be expandable in the future, researchers will be able to target their cancer-associated proteins of interest in any sample type using the detectability information in plasma and urine as a guide. The generated reference map of SRM assays for cancer-associated proteins is a valuable resource for accelerating and planning biomarker verification studies.

The consistent detection and quantification of protein post-translational modifications (PTMs) across sample cohorts is an essential prerequisite for the functional analysis of biological processes. Data-independent acquisition (DIA), a bottom-up mass spectrometry based proteomic strategy, exemplified by SWATH-MS, provides complete precursor and fragment ion information of a sample and thus, in principle, the information to identify peptidoforms, the modified variants of a peptide. However, due to the convoluted structure of DIA data sets the confident and systematic identification and quantification of peptidoforms has remained challenging. Here we present IPF (Inference of PeptidoForms), a fully automated algorithm that uses spectral libraries to query, validate and quantify peptidoforms in DIA data sets. The method was developed on data acquired by SWATH-MS and benchmarked using a synthetic phosphopeptide reference data set and phosphopeptide-enriched samples. The data indicate that IPF reduced false site-localization by more than 7-fold in comparison to previous approaches, while recovering 85.4% of the true signals. IPF was applied to detect and quantify peptidoforms carrying ten different types of PTMs in DIA data acquired from more than 200 samples of undepleted blood plasma of a human twin cohort. The data approportioned, for the first time, the contribution of heritable, environmental and longitudinal effects on the observed quantitative variability of specific modifications in blood plasma of a human population.

In yeast, the glucose-induced degradation-deficient (GID) E3 ligase selectively degrades superfluous gluconeogenic enzymes. Here, we identified all subunits of the mammalian GID/CTLH complex and provide a comprehensive map of its hierarchical organization and step-wise assembly. Biochemical reconstitution demonstrates that the mammalian complex possesses inherent E3 ubiquitin ligase activity, using Ube2H as its cognate E2. Deletions of multiple GID subunits compromise cell proliferation, and this defect is accompanied by deregulation of critical cell cycle markers such as the retinoblastoma (Rb) tumor suppressor, phospho-Histone H3 and Cyclin A. We identify the negative regulator of pro-proliferative genes Hbp1 as a bonafide GID/CTLH proteolytic substrate. Indeed, Hbp1 accumulates in cells lacking GID/CTLH activity, and Hbp1 physically interacts and is ubiquitinated in vitro by reconstituted GID/CTLH complexes. Our biochemical and cellular analysis thus demonstrates that the GID/CTLH complex prevents cell cycle exit in G1, at least in part by degrading Hbp1.

Although chromosome partitioning during mitosis is well studied, the molecular mechanisms that allow proper segregation of cytoplasmic organelles in human cells are poorly understood. Here we show that mitochondria interact with growing microtubule tips and are transported towards the daughter cell periphery at the end of mitosis. This phenomenon is promoted by the direct and cell cycle-dependent interaction of the mitochondrial protein Miro and the cytoskeletal-associated protein Cenp-F. Cenp-F is recruited to mitochondria by Miro at the time of cytokinesis and associates with microtubule growing tips. Cells devoid of Cenp-F or Miro show decreased spreading of the mitochondrial network as well as cytokinesis-specific defects in mitochondrial transport towards the cell periphery. Thus, Miro and Cenp-F promote anterograde mitochondrial movement and proper mitochondrial distribution in daughter cells.