Hannah Nguyen

The paper designs and validates protein readers that specifically detect disease-related modifications in Tau and Abeta in Alzheimer's disease models and human samples.

CaGE alternates guided structure prediction with sequence redesign, fading guidance each cycle, so sequences recover complex catalytic sites unaided; designed nonheme-iron and PLP-dependent enzymes were active.

This study designs de novo cysteine proteases with high activity and structural novelty, achieving 19% success rate and up to 3 x 10^7 rate enhancement over background reactions.

This paper designs de novo metalloproteases using RoseTTAFold Diffusion 2, achieving high activity and specificity for cleaving various substrates.

This paper develops a computational method to design glycoproteins with controlled N-linked glycan composition by encoding glycan location and structure into amino acid sequences.

This paper presents a modular strategy to design programmable, highly porous protein crystals with tunable architectures and composition, enabling applications in catalysis and biomedicine.

The paper designs miniproteins that inhibit Bacteroides fragilis toxin by blocking receptor binding, preventing gut pathology without antibiotics.

This study designs proteins to control the phase, form, and function of metal-carbonate materials, demonstrating their use as electrocatalysts for alkaline water splitting.

This paper designs de novo protein nanoparticles with integrated functional motifs, demonstrating their ability to form structured assemblies and elicit immune responses in mice.

The paper designs TrkA agonists that activate neuronal differentiation and neurite outgrowth without triggering pain-related gene expression.

The paper designs peptide traps that bind amyloidogenic segments in {beta}-strand and {beta}-hairpin conformations, effectively blocking fibril assembly for A{beta}42.

This paper designs 3D protein crystals with specified lattice architectures using computational methods, achieving atomic accuracy and demonstrating successful experimental validation.

The paper designs closed repeat proteins with central pockets using deep learning and validates them in E. coli, showing structural consistency and potential for small molecule binding.