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Biology subjects

White, C. L. A.

Publications and source records attributed to White, C. L. A..

2 recordsLinked to original sources

Large-Scale Mutagenesis Identifies Sequence Changes That Bias Tau Toward CTE-like Aggregation

Tau amyloid fibrils adopt disease-specific structures across tauopathies, yet the sequence determinants that favor one pathological fold over another remain poorly defined. Here we combine computational saturation mutagenesis and energetic profiling, in vitro aggregation, cryo-EM and cellular seeding assays to identify sequence changes that alter tau fold compatibility. Across six tauopathy fibril structures, approximately 12,000 substitutions revealed that mutational sensitivity is concentrated within interaction networks formed by amyloidogenic motifs rather than uniformly distributed across the fibril core. Comparison of Alzheimer's disease (AD) and chronic traumatic encephalopathy (CTE) folds identified residues within the 335-363 region that differentially influence fold compatibility. The S341L/S352I double mutant accelerated aggregation and remodeled packing within the PAM4-containing amyloidogenic region, producing an alternative amyloid architecture in a minimal tau fragment. S341L/S352I biosensors preferentially responded to CTE-derived over Alzheimer's disease-derived seeds, while aggregates generated from recombinant S341L/S352I tau exhibited CTE-like cellular templating behavior. Although these assemblies are not established to reproduce the atomic CTE fold, the results demonstrate that targeted sequence changes can bias tau aggregation and propagation toward a CTE-compatible state. This framework provides a strategy for defining sequence-structure relationships in polymorphic amyloids and developing conformation-selective models and biosensors.

bioengineering↗

Network of hotspot interactions cluster tau amyloid folds

Cryogenic electron microscopy has revealed unprecedented molecular insight into the conformation of {beta}-sheet-rich protein amyloids linked to neurodegenerative diseases. It remains unknown how a protein can adopt a diversity of folds and form multiple distinct fibrillar structures. Here we develop an in silico alanine scan method to estimate the relative energetic contribution of each amino acid in an amyloid assembly. We apply our method to twenty-seven ex vivo and in vitro fibril structural polymorphs of the microtubule-associated protein tau. We uncover networks of energetically important interactions involving amyloid-forming motifs that stabilize the different fibril folds. We test our predictions in cellular and in vitro aggregation assays. Using a machine learning approach, we classify the structures based on residue energetics to identify distinguishing and unifying features. Our energetic profiling suggests that minimal sequence elements that control the stability of tau fibrils, allowing future design of protein sequences that fold into unique structures.

neuroscience↗