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Key roles in copper efflux and protein homeostasis of the intrinsically disordered region of a bacterial outer membrane channel
Ligand Desorption and Surface Oxidation Drive Nanoparticle Coalescence in Diffusion-Limited Aggregation
Self-cleavage of the GAIN domain of adhesion G protein-coupled receptors requires multiple domain-extrinsic factors
Abstract The autoproteolysis-inducing (GAIN) domain of class B2/adhesion G protein-coupled receptors (aGPCRs) is structurally conserved, and its self-cleavage is central to receptor mechanotransduction and signaling. Yet, the influence of factors beyond the protein fold on GAIN domain autoproteolysis remains unclear. Using ADGRE2/EMR2, a self-cleaved aGPCR, we investigated contributions of the seven-transmembrane (7TM) region to GAIN domain autoproteolysis during receptor maturation and trafficking. Retention Upon Selective Hook (RUSH) assays showed that self-cleavage acts as a checkpoint before endoplasmic reticulum (ER) exit, but not during plasma membrane transport. Stepwise truncations of the 7TM domain revealed that cleavage can occur before or at synthesis of the first transmembrane helix, and is enhanced with formation of the full 7TM domain. Analyses of six additional cleavage-competent aGPCRs demonstrated that ER membrane tethering facilitates GAIN domain processing, supported by proteomic evidence linking cleavage to proximity with the N-glycosylation pathway. These results highlight the interplay between GAIN and 7TM domains, offering mechanistic insights and guiding pharmacological strategies to modulate aGPCR activation and signaling.
Biochemical profiling of plant defense mechanisms against Spodoptera frugiperda (J.E. Smith) infestation in selected host species
Soluble Notch agonists drive T cell production
The cytoplasmic domain of the pseudoprotease iRhom2 mediates distinct signaling mechanisms to control activation of the cell surface protease ADAM17
Nickel-Catalyzed Enantioselective Direct Addition of Styrenes to Imines Enabled by Chiral Spiro Phosphine Ligands
Artificial Intelligence (AI)-assisted readout method for the evaluation of skin prick automated test results
Effects of plyometrics training on lower limb strength, power, agility, and body composition in athletically trained adults: systematic review and meta-analysis
Epigenetic inhibitor silences KRAS-driven oncogenes
Structural bias in vitamin A metabolism: Why α-retinoids miss the eye
Reprocessing Thermoset Polyurethane Foams Using Their Residual Polymerization Catalysts
Unifying machine learning and interpolation theory via interpolating neural networks
Ascertaining the morpho-molecular diversity in buckwheat germplasm and identification of high yielding, stable genotypes with superior biochemical quality
Gut microbiota–derived metabolite drives atherosclerosis
Withdrawal: A regulatory role for p38δ MAPK in keratinocyte differentiation: Evidence for p38δ-ERK1/2 complex formation
Dual-Vacancy-Induced Selective Oxidative Cleavage of C<sub>α</sub>–C<sub>β</sub> Bonds for the Electrocatalytic Depolymerization of Lignin
Machine learning of charges and long-range interactions from energies and forces
Abstract Accurate modeling of long-range forces is critical in atomistic simulations, as they play a central role in determining the properties of material and chemical systems. However, standard machine learning interatomic potentials (MLIPs) often rely on short-range approximations, limiting their applicability to systems with significant electrostatics and dispersion forces. We recently introduced the Latent Ewald Summation (LES) method, which captures long-range electrostatics without explicitly learning atomic charges or charge equilibration. We benchmark LES on diverse and challenging systems, including charged molecules, ionic liquids, electrolyte solutions, polar dipeptides, surface adsorption, electrolyte/solid interfaces, and solid-solid interfaces. Here we show that LES can reproduce the exact atomic charges for classical systems with fixed charges and can infer dipole and quadrupole moments, as well as the dipole derivative with respect to atomic positions, for quantum mechanical systems. Moreover, LES can achieve better accuracy in energy and force predictions compared to methods that explicitly learn from charges.