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Biology subjects

Gomes, G.-N.

Publications and source records attributed to Gomes, G.-N..

2 recordsLinked to original sources

Inhibition of ApoE4 Endocytosis with LDLR-LA Peptides

The Apolipoprotein E4 (ApoE4) genotype is the most significant genetic risk factor for late-onset Alzheimers disease (AD). A key driver of ApoE4 cellular toxicity is the endo-lysosomal burden resulting from the excessive receptor-mediated uptake of ApoE4 lipoparticles. The high-affinity interaction between lipidated ApoE4 and the Low-Density Lipoprotein Receptor (LDLR) saturates the cellular degradation machinery, correlating with lysosomal alkalinization, lipid accumulation, and cell death. To target this critical interaction interface, which consists of 7 tandem ligand-binding type-A (LA) modules in the human LDLR, we present the design and evaluation of recombinant LDLR minireceptors comprising combinations of these LA modules to competitively antagonize ApoE4 endocytosis. We observe a distinct isoform-dependent uptake dynamic across multiple central nervous system (CNS) cell models, with ApoE4 showing significantly greater total intracellular accumulation than ApoE2. Furthermore, engineered LA peptides selectively bind ApoE4 over human serum LDL and differentially inhibit its uptake, revealing a distinct structural efficacy hierarchy of LA3456 [~] LA345 > LA456 [~] LA45 >> LA34. We establish the resilience of the LA45 minireceptor under physiological serum conditions and identify LA345 as the most stable truncated construct in vitro. Notably, molecular tagging orientation is critical for therapeutic engineering; C-terminal tagging completely preserves the inhibitory function of the minireceptors, whereas N-terminal tagging drastically reduces it. These findings provide a framework for scalable, deliverable inhibition of the ApoE4-LDLR interaction as a potential therapeutic target to mitigate endo-lysosomal accumulation in AD.

biochemistry↗

Integrative conformational ensembles of Sic1 using different initial pools and optimization methods

Intrinsically disordered proteins play key roles in regulatory protein interactions, but their detailed structural characterization remains challenging. Here we calculate and compare conformational ensembles for the disordered protein Sic1 from yeast, starting from initial ensembles that were generated either by statistical sampling of the conformational landscape, or by molecular dynamics simulations. Two popular, yet contrasting optimization methods were used, ENSEMBLE and Bayesian Maximum Entropy, to achieve agreement with experimental data from nuclear magnetic resonance, small-angle X-ray scattering and single-molecule Forster resonance energy transfer. The comparative analysis of the optimized ensembles, including secondary structure propensity, inter-residue contact maps, and the distributions of hydrogen bond and pi interactions, revealed the importance of the physics-based generation of initial ensembles. The analysis also provides insights into designing new experiments that can maximally discriminate among the optimized ensembles. Overall, differences between ensembles optimized from different priors were greater than when using the same prior with different optimization methods. Generating increasingly accurate, reliable and experimentally validated ensembles for disordered proteins is an important step towards a mechanistic understanding of their biological function and involvement in various diseases.

biophysics↗