ATP-independent substrate recruitment to proteasomal degradation in mycobacteria.

Mycobacteria and other actinobacteria possess proteasomal degradation pathways in addition to the common bacterial compartmentalizing protease systems. Proteasomal degradation plays a crucial role in the survival of these bacteria in adverse environments. The mycobacterial proteasome interacts with several ring-shaped activators, including the bacterial proteasome activator (Bpa), which enables energy-independent degradation of heat shock repressor HspR. However, the mechanism of substrate selection and processing by the Bpa-proteasome complex remains unclear. In this study, we present evidence that disorder in substrates is required but not sufficient for recruitment to Bpa-mediated proteasomal degradation. We demonstrate that Bpa binds to the folded N-terminal helix-turn-helix domain of HspR, whereas the unstructured C-terminal tail of the substrate acts as a sequence-specific threading handle to promote efficient proteasomal degradation. In addition, we establish that the heat shock chaperone DnaK, which interacts with and co-regulates HspR, stabilizes HspR against Bpa-mediated proteasomal degradation. By phenotypical characterization of Mycobacterium smegmatis parent and bpa deletion mutant strains, we show that Bpa-dependent proteasomal degradation supports the survival of the bacterium under stress conditions by degrading HspR that regulates vital chaperones.

Limited Proteolysis-Mass Spectrometry to Identify Metabolite-Protein Interactions.

Metabolite-protein interactions regulate diverse cellular processes, prompting the development of methods to investigate the metabolite-protein interactome at a global scale. One such method is our previously developed structural proteomics approach, limited proteolysis-mass spectrometry (LiP-MS), which detects proteome-wide metabolite-protein and drug-protein interactions in native bacterial, yeast, and mammalian systems, and allows identification of binding sites without chemical modification. Here we describe a detailed experimental and analytical workflow for conducting a LiP-MS experiment to detect small molecule-protein interactions, either in a single-dose (LiP-SMap) or a multiple-dose (LiP-Quant) format. LiP-Quant analysis combines the peptide-level resolution of LiP-MS with a machine learning-based framework to prioritize true protein targets of a small molecule of interest. We provide an updated R script for LiP-Quant analysis via a GitHub repository accessible at https://github.com/RolandBruderer/MiMB-LiP-Quant .

Brain-enriched RagB isoforms regulate the dynamics of mTORC1 activity through GATOR1 inhibition.

Mechanistic target of rapamycin complex 1 (mTORC1) senses nutrient availability to appropriately regulate cellular anabolism and catabolism. During nutrient restriction, different organs in an animal do not respond equally, with vital organs being relatively spared. This raises the possibility that mTORC1 is differentially regulated in different cell types, yet little is known about this mechanistically. The Rag GTPases, RagA or RagB bound to RagC or RagD, tether mTORC1 in a nutrient-dependent manner to lysosomes where mTORC1 becomes activated. Although the RagA and B paralogues were assumed to be functionally equivalent, we find here that the RagB isoforms, which are highly expressed in neurons, impart mTORC1 with resistance to nutrient starvation by inhibiting the RagA/B GTPase-activating protein GATOR1. We further show that high expression of RagB isoforms is observed in some tumours, revealing an alternative strategy by which cancer cells can retain elevated mTORC1 upon low nutrient availability.

Structure of the nutrient-sensing hub GATOR2.

Mechanistic target of rapamycin complex 1 (mTORC1) controls growth by regulating anabolic and catabolic processes in response to environmental cues, including nutrients1,2. Amino acids signal to mTORC1 through the Rag GTPases, which are regulated by several protein complexes, including GATOR1 and GATOR2. GATOR2, which has five components (WDR24, MIOS, WDR59, SEH1L and SEC13), is required for amino acids to activate mTORC1 and interacts with the leucine and arginine sensors SESN2 and CASTOR1, respectively3-5. Despite this central role in nutrient sensing, GATOR2 remains mysterious as its subunit stoichiometry, biochemical function and structure are unknown. Here we used cryo-electron microscopy to determine the three-dimensional structure of the human GATOR2 complex. We found that GATOR2 adopts a large (1.1 MDa), two-fold symmetric, cage-like architecture, supported by an octagonal scaffold and decorated with eight pairs of WD40 ß-propellers. The scaffold contains two WDR24, four MIOS and two WDR59 subunits circularized via two distinct types of junction involving non-catalytic RING domains and α-solenoids. Integration of SEH1L and SEC13 into the scaffold through ß-propeller blade donation stabilizes the GATOR2 complex and reveals an evolutionary relationship to the nuclear pore and membrane-coating complexes6. The scaffold orients the WD40 ß-propeller dimers, which mediate interactions with SESN2, CASTOR1 and GATOR1. Our work reveals the structure of an essential component of the nutrient-sensing machinery and provides a foundation for understanding the function of GATOR2 within the mTORC1 pathway.

protti: an R package for comprehensive data analysis of peptide- and protein-centric bottom-up proteomics data.

Summary: We present a flexible, user-friendly R package called protti for comprehensive quality control, analysis and interpretation of quantitative bottom-up proteomics data. protti supports the analysis of protein-centric data such as those associated with protein expression analyses, as well as peptide-centric data such as those resulting from limited proteolysis-coupled mass spectrometry analysis. Due to its flexible design, it supports analysis of label-free, data-dependent, data-independent and targeted proteomics datasets. protti can be run on the output of any search engine and software package commonly used for bottom-up proteomics experiments such as Spectronaut, Skyline, MaxQuant or Proteome Discoverer, adequately exported to table format. Availability and implementation: protti is implemented as an open-source R package. Release versions are available via CRAN (https://CRAN.R-project.org/package=protti) and work on all major operating systems. The development version is maintained on GitHub (https://github.com/jpquast/protti). Full documentation including examples is provided in the form of vignettes on our package website (jpquast.github.io/protti/).