A somatic multiple myeloma mutation unravels a mechanism of oligomerization-mediated product inhibition in GGPPS.

Protein prenylation plays a critical role in regulating the cellular localization of small GTPases and is essential for multiple myeloma (MM) pathology. Geranylgeranyl diphosphate synthase (GGPPS), producing a key prenylation moiety, exists in a dimeric or hexameric form, depending on the species. However, the functional significance of this oligomerization remains unclear.

Identification of a magnesium-binding site at the primary allosteric calcium sensor of the sodium-calcium exchanger: Implications for physiological regulation.

Sodium-calcium exchanger (NCX) proteins are ubiquitously expressed and play a pivotal role in cellular calcium homeostasis by mediating uphill calcium efflux across the cell membrane. Intracellular calcium allosterically regulates the exchange activity by binding to two cytoplasmic calcium-binding domains, CBD1 and CBD2. However, the calcium-binding affinities of these domains are seemingly inadequate to sense physiological calcium oscillations.

Structural dynamics of Na and Ca interactions with full-size mammalian NCX.

Cytosolic Ca and Na allosterically regulate Na/Ca exchanger (NCX) proteins to vary the NCX-mediated Ca entry/exit rates in diverse cell types. To resolve the structure-based dynamic mechanisms underlying the ion-dependent allosteric regulation in mammalian NCXs, we analyze the apo, Ca, and Na-bound species of the brain NCX1.4 variant using hydrogen-deuterium exchange mass spectrometry (HDX-MS) and molecular dynamics (MD) simulations.

Correction: Erlichman et al. Tumor Cell-Autonomous Pro-Metastatic Activities of PD-L1 in Human Breast Cancer Are Mediated by PD-L1-S283 and Chemokine Axes. 2022, , 1042.

The authors apologize for several typos/technical inaccuracies in the original article [...

Tumor Cell-Autonomous Pro-Metastatic Activities of PD-L1 in Human Breast Cancer Are Mediated by PD-L1-S283 and Chemokine Axes.

Therapies targeting the PD-L1/PD-1 axis have recently been introduced to triple-negative breast cancer (TNBC) with limited efficacy, suggesting that this axis promotes tumor progression through mechanisms other than immune suppression. Here, we over-expressed WT-PD-L1 in human TNBC cells (express endogenous PD-L1) and in luminal-A breast cancer cells (no endogenous PD-L1 expression) and demonstrated that cell-autonomous PD-L1 activities lead to increased tumor cell growth, invasion and release of pro-metastatic factors (CXCL8, sICAM-1, GM-CSF). These activities were promoted by PD-1 and were inhibited by mutating S283 in PD-L1.

Alleviation of a polyglucosan storage disorder by enhancement of autophagic glycogen catabolism.

This work employs adult polyglucosan body disease (APBD) models to explore the efficacy and mechanism of action of the polyglucosan-reducing compound 144DG11. APBD is a glycogen storage disorder (GSD) caused by glycogen branching enzyme (GBE) deficiency causing accumulation of poorly branched glycogen inclusions called polyglucosans. 144DG11 improved survival and motor parameters in a GBE knockin (Gbe ) APBD mouse model.

A novel specific PERK activator reduces toxicity and extends survival in Huntington's disease models.

One of the pathways of the unfolded protein response, initiated by PKR-like endoplasmic reticulum kinase (PERK), is key to neuronal homeostasis in neurodegenerative diseases. PERK pathway activation is usually accomplished by inhibiting eIF2α-P dephosphorylation, after its phosphorylation by PERK. Less tried is an approach involving direct PERK activation without compromising long-term recovery of eIF2α function by dephosphorylation.

Computational design of substrate selective inhibition.

Most enzymes act on more than a single substrate. There is frequently a need to block the production of a single pathogenic outcome of enzymatic activity on a substrate but to avoid blocking others of its catalytic actions. Full blocking might cause severe side effects because some products of that catalysis may be vital.

Discovering highly selective and diverse PPAR-delta agonists by ligand based machine learning and structural modeling.

PPAR-δ agonists are known to enhance fatty acid metabolism, preserving glucose and physical endurance and are suggested as candidates for treating metabolic diseases. None have reached the clinic yet. Our Machine Learning algorithm called "Iterative Stochastic Elimination" (ISE) was applied to construct a ligand-based multi-filter ranking model to distinguish between confirmed PPAR-δ agonists and random molecules.