Apolipoprotein E (ApoE) performs most of its pathologically relevant biology at lipid interfaces, yet design efforts target its receptor binding, interdomain geometry or abundance rather than the lipid affinity of its N-terminal bundle. We asked whether the reverse-QTY (rQTY) code can make that bundle membrane-compatible without disturbing its fold. Within mature residues 11–167 we converted every glutamine, threonine and tyrosine lying inside an α-helix (Q→L, T→V, Y→F) and left those in turns and loops unchanged: 22 substitutions over 14.0% of the segment, sparing the receptor recognition region, moving the grand average of hydropathicity from −0.757 to +0.123. Across five AlphaFold3 models per sequence the variant bundle superimposes on the native at 0.81 Å mean Cα root-mean-square deviation with indistinguishable helix content, while apolar solvent-accessible surface rises 59% at constant total surface and burial. Hydrophobic moment increases in five of six helical segments, so amphipathicity is preserved. In all-atom molecular dynamics the native segment holds its fold in water over 100 ns; the variant stays folded and essentially fully helical in a six-component neuronal bilayer (50 ns) and a highly mobile membrane mimetic (90 ns), thinning the bilayer locally by 10.9 Å. The first lipid shell is only mildly biased in composition, and the two membrane models disagree. Each system was run once with the protein embedded at the build stage, so quantifying affinity will require matched native trajectories and partitioning free-energy calculations. rQTY thus acts as a geometry-preserving, residue-resolved control on the surface chemistry of a soluble helical bundle.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.