Phytoene synthase (PSY) is the crucial plastidial enzyme in the carotenoid biosynthetic pathway. However, its post-translational regulation remains elusive. Likewise, Clp protease constitutes a central part of the plastid protease network, but its substrates for degradation are not well known. In this study, we report that PSY is a substrate of the Clp protease. PSY was uncovered to physically interact with various Clp protease subunits (i.e., ClpS1, ClpC1, and ClpD). High levels of PSY and several other carotenogenic enzyme proteins overaccumulate in the clpc1, clpp4, and clpr1-2 mutants. The overaccumulated PSY was found to be partially enzymatically active. Impairment of Clp activity in clpc1 results in a reduced rate of PSY protein turnover, further supporting the role of Clp protease in degrading PSY protein. On the other hand, the ORANGE (OR) protein, a major post-translational regulator of PSY with holdase chaperone activity, enhances PSY protein stability and increases the enzymatically active proportion of PSY in clpc1, counterbalancing Clp-mediated proteolysis in maintaining PSY protein homeostasis. Collectively, these findings provide novel insights into the quality control of plastid-localized proteins and establish a hitherto unidentified post-translational regulatory mechanism of carotenogenic enzymes in modulating carotenoid biosynthesis in plants.

Protein–metabolite interactions are of crucial importance for all cellular processes but remain understudied. Here, we applied a biochemical approach named PROMIS, to address the complexity of the protein–small molecule interactome in the model yeast Saccharomyces cerevisiae. By doing so, we provide a unique dataset, which can be queried for interactions between 74 small molecules and 3982 proteins using a user-friendly interface available at https://promis.mpimp-golm.mpg.de/yeastpmi/. By interpolating PROMIS with the list of predicted protein–metabolite interactions, we provided experimental validation for 225 binding events. Remarkably, of the 74 small molecules co-eluting with proteins, 36 were proteogenic dipeptides. Targeted analysis of a representative dipeptide, Ser-Leu, revealed numerous protein interactors comprising chaperones, proteasomal subunits, and metabolic enzymes. We could further demonstrate that Ser-Leu binding increases activity of a glycolytic enzyme phosphoglycerate kinase (Pgk1). Consistent with the binding analysis, Ser-Leu supplementation leads to the acute metabolic changes and delays timing of a diauxic shift. Supported by the dipeptide accumulation analysis our work attests to the role of Ser-Leu as a metabolic regulator at the interface of protein degradation and central metabolism.

Small molecules are not only intermediates of metabolism, but also play important roles in signaling and in controlling cellular metabolism, growth, and development. Although a few systematic studies have been conducted, the true extent of protein–small molecule interactions in biological systems remains unknown. PROtein–metabolite interactions using size separation (PROMIS) is a method for studying protein–small molecule interactions in a non‐targeted, proteome‐ and metabolome‐wide manner. This approach uses size‐exclusion chromatography followed by proteomics and metabolomics liquid chromatography–mass spectrometry analysis of the collected fractions. Assuming that small molecules bound to proteins would co‐fractionate together, we found numerous small molecules co‐eluting with proteins, strongly suggesting the formation of stable complexes. Using PROMIS, we identified known small molecule–protein complexes, such as between enzymes and cofactors, and also found novel interactions. © 2019 The Authors. Basic Protocol 1: Preparation of native cell lysate from plant material Support Protocol: Bradford assay to determine protein concentration Basic Protocol 2: Separation of molecular complexes using size‐exclusion chromatography Basic Protocol 3: Simultaneous extraction of proteins and metabolites using single‐step extraction protocol Basic Protocol 4: Metabolomics analysis Basic Protocol 5: Proteomics analysis

Metabolite-protein interactions affect and shape diverse cellular processes. Yet, despite advances, approaches for identifying metabolite-protein interactions at a genome-wide scale are lacking. Here we present an approach termed SLIMP that predicts metabolite-protein interactions using supervised machine learning on features engineered from metabolic and proteomic profiles from a co-fractionation mass spectrometry-based technique. By applying SLIMP with gold standards, assembled from public databases, along with metabolic and proteomic data sets from multiple conditions and growth stages we predicted over 9,000 and 20,000 metabolite-protein interactions for Saccharomyces cerevisiae and Arabidopsis thaliana, respectively. Extensive comparative analyses corroborated the quality of the predictions from SLIMP with respect to widely-used performance measures (e.g. F1-score exceeding 0.8). SLIMP predicted novel targets of 2’, 3’ cyclic nucleotides and dipeptides, which we analysed comparatively between the two organisms. Finally, predicted interactions for the dipeptide Tyr-Asp in Arabidopsis and the dipeptide Ser-Leu in yeast were independently validated, opening the possibility for future applications of supervised machine learning approaches in this area of systems biology.

Co-fractionation mass spectrometry (CF-MS)-based approaches enable cell-wide identification of protein-protein and protein-metabolite complexes present in the cellular lysate. CF-MS combines biochemical separation of molecular complexes with an untargeted mass-spectrometry-based proteomics and/or metabolomics analysis of the obtained fractions, and is used to delineate putative interactors. CF-MS data are a treasure trove for biological discovery. To facilitate analysis and visualization of original or publically available CF-MS datasets, we designed PROMISed, a user-friendly tool available online via https://myshiny.mpimp-golm.mpg.de/PDP1/ or as a repository via https://github.com/DennisSchlossarek/PROMISed . Specifically, starting with raw fractionation profiles, PROMISed (i) contains activities for data pre-processing and normalization, (ii) deconvolutes complex fractionation profiles into single, distinct peaks, (iii) identifies co-eluting protein–protein or protein–metabolite pairs using user-defined correlation methods, and (iv) performs co-fractionation network analysis. Given multiple CF-MS datasets, for instance representing different environmental condition, PROMISed allows to select for proteins and metabolites that differ in their elution profile, which may indicate change in the interaction status. But it also enables the identification of protein–protein and protein–metabolite pairs that co-elute together across multiple datasets. PROMISed enables users to (i) easily adjust parameters at each step of the analysis, (ii) download partial and final results, and (iii) select among different data-visualization options. PROMISed renders CF-MS data accessible to a broad scientific audience, allowing users with no computational or statistical background to look for novel protein–protein and protein–metabolite complexes for further experimental validation.