Methylated PP2A stabilizes Gcn4 to enable a methionine-induced anabolic program.

Methionine, through S-adenosylmethionine, activates a multifaceted growth program in which ribosome biogenesis, carbon metabolism, and amino acid and nucleotide biosynthesis are induced. This growth program requires the activity of the Gcn4 transcription factor (called ATF4 in mammals), which facilitates the supply of metabolic precursors that are essential for anabolism. However, how Gcn4 itself is regulated in the presence of methionine is unknown. Here, we discover that Gcn4 protein levels are increased by methionine, despite conditions of high cell growth and translation (in which the roles of Gcn4 are not well-studied). We demonstrate that this mechanism of Gcn4 induction is independent of transcription, as well as the conventional Gcn2/eIF2α-mediated increased translation of Gcn4. Instead, when methionine is abundant, Gcn4 phosphorylation is decreased, which reduces its ubiquitination and therefore degradation. Gcn4 is dephosphorylated by the protein phosphatase 2A (PP2A); our data show that when methionine is abundant, the conserved methyltransferase Ppm1 methylates and alters the activity of the catalytic subunit of PP2A, shifting the balance of Gcn4 toward a dephosphorylated, stable state. The absence of Ppm1 or the loss of the PP2A methylation destabilizes Gcn4 even when methionine is abundant, leading to collapse of the Gcn4-dependent anabolic program. These findings reveal a novel, methionine-dependent signaling and regulatory axis. Here methionine directs the conserved methyltransferase Ppm1 via its target phosphatase PP2A to selectively stabilize Gcn4. Through this, cells conditionally modify a major phosphatase to stabilize a metabolic master regulator and drive anabolism.

Intrinsic Pleckstrin Homology (PH) Domain Motion in Phospholipase C-ß Exposes a Gßγ Protein Binding Site.

Mammalian phospholipase C-ß (PLC-ß) isoforms are stimulated by heterotrimeric G protein subunits and members of the Rho GTPase family of small G proteins. Although recent structural studies showed how Gαq and Rac1 bind PLC-ß, there is a lack of consensus regarding the Gßγ binding site in PLC-ß. Using FRET between cerulean fluorescent protein-labeled Gßγ and the Alexa Fluor 594-labeled PLC-ß pleckstrin homology (PH) domain, we demonstrate that the PH domain is the minimal Gßγ binding region in PLC-ß3. We show that the isolated PH domain can compete with full-length PLC-ß3 for binding Gßγ but not Gαq, Using sequence conservation, structural analyses, and mutagenesis, we identify a hydrophobic face of the PLC-ß PH domain as the Gßγ binding interface. This PH domain surface is not solvent-exposed in crystal structures of PLC-ß, necessitating conformational rearrangement to allow Gßγ binding. Blocking PH domain motion in PLC-ß by cross-linking it to the EF hand domain inhibits stimulation by Gßγ without altering basal activity or Gαq response. The fraction of PLC-ß cross-linked is proportional to the fractional loss of Gßγ response. Cross-linked PLC-ß does not bind Gßγ in a FRET-based Gßγ-PLC-ß binding assay. We propose that unliganded PLC-ß exists in equilibrium between a closed conformation observed in crystal structures and an open conformation where the PH domain moves away from the EF hands. Therefore, intrinsic movement of the PH domain in PLC-ß modulates Gßγ access to its binding site.

Mammalian phospholipase C.

Phospholipase C (PLC) converts phosphatidylinositol 4,5-bisphosphate (PIP(2)) to inositol 1,4,5-trisphosphate (IP(3)) and diacylglycerol (DAG). DAG and IP(3) each control diverse cellular processes and are also substrates for synthesis of other important signaling molecules. PLC is thus central to many important interlocking regulatory networks. Mammals express six families of PLCs, each with both unique and overlapping controls over expression and subcellular distribution. Each PLC also responds acutely to its own spectrum of activators that includes heterotrimeric G protein subunits, protein tyrosine kinases, small G proteins, Ca(2+), and phospholipids. Mammalian PLCs are autoinhibited by a region in the catalytic TIM barrel domain that is the target of much of their acute regulation. In combination, the PLCs act as a signaling nexus that integrates numerous signaling inputs, critically governs PIP(2) levels, and regulates production of important second messengers to determine cell behavior over the millisecond to hour timescale.

Optimization of Potent Ligands for the E3 Ligase DCAF15 and Evaluation of Their Use in Heterobifunctional Degraders.

Unlocking novel E3 ligases for use in heterobifunctional PROTAC degraders is of high importance to the pharmaceutical industry. Over-reliance on the current suite of ligands used to recruit E3 ligases could limit the potential of their application. To address this, potent ligands for DCAF15 were optimized using cryo-EM supported, structure-based design to improve on micromolar starting points. A potent binder, compound 24, was identified and subsequently conjugated into PROTACs against multiple targets. Following attempts on degrading a number of proteins using DCAF15 recruiting PROTACs, only degradation of BRD4 was observed. Deconvolution of the mechanism of action showed that this degradation was not mediated by DCAF15, thereby highlighting both the challenges faced when trying to expand the toolbox of validated E3 ligase ligands for use in PROTAC degraders and the pitfalls of using BRD4 as a model substrate.

Flexible Fitting of PROTAC Concentration-Response Curves with Changepoint Gaussian Processes.

A proteolysis-targeting chimera (PROTAC) is a new technology that marks proteins for degradation in a highly specific manner. During screening, PROTAC compounds are tested in concentration-response (CR) assays to determine their potency, and parameters such as the half-maximal degradation concentration (DC50) are estimated from the fitted CR curves. These parameters are used to rank compounds, with lower DC50 values indicating greater potency. However, PROTAC data often exhibit biphasic and polyphasic relationships, making standard sigmoidal CR models inappropriate. A common solution includes manual omitting of points (the so-called masking step), allowing standard models to be used on the reduced data sets. Due to its manual and subjective nature, masking becomes a costly and nonreproducible procedure. We therefore used a Bayesian changepoint Gaussian processes model that can flexibly fit both nonsigmoidal and sigmoidal CR curves without user input. Parameters such as the DC50, maximum effect Dmax, and point of departure (PoD) are estimated from the fitted curves. We then rank compounds based on one or more parameters and propagate the parameter uncertainty into the rankings, enabling us to confidently state if one compound is better than another. Hence, we used a flexible and automated procedure for PROTAC screening experiments. By minimizing subjective decisions, our approach reduces time and cost and ensures reproducibility of the compound-ranking procedure. The code and data are provided on GitHub (https://github.com/elizavetasemenova/gp_concentration_response).

Synergistic activation of phospholipase C-beta3 by Galpha(q) and Gbetagamma describes a simple two-state coincidence detector.

BACKGROUND: Receptors that couple to G(i) and G(q) often interact synergistically in cells to elicit cytosolic Ca(2+) transients that are several-fold higher than the sum of those driven by each receptor alone. Such synergism is commonly assumed to be complex, requiring regulatory interaction between components, multiple pathways, or multiple states of the target protein. RESULTS: We show that cellular G(i)-G(q) synergism derives from direct supra-additive stimulation of phospholipase C-beta3 (PLC-beta3) by G protein subunits Gbetagamma and Galpha(q), the relevant components of the G(i) and G(q) signaling pathways. No additional pathway or proteins are required. Synergism is quantitatively explained by the classical and simple two-state (inactive<-->active) allosteric mechanism. We show generally that synergistic activation of a two-state enzyme reflects enhanced conversion to the active state when both ligands are bound, not merely the enhancement of ligand affinity predicted by positive cooperativity. The two-state mechanism also explains why synergism is unique to PLC-beta3 among the four PLC-beta isoforms and, in general, why one enzyme may respond synergistically to two activators while another does not. Expression of synergism demands that an enzyme display low basal activity in the absence of ligand and becomes significant only when basal activity is