Hyunjun Yang

The paper develops a spatial multi-omic method to study lipid dysregulation in Alzheimer's disease across brain regions in mice, identifying region-specific glial metabolic changes.

This paper introduces SweetFold, a diffusion model for predicting glycan and glycoprotein structures, showing improved performance over existing models.

This study shows that random sequences repeated in tandem are more likely to fold into stable structures than fully random sequences, with some structures being novel and experimentally confirmed.

The paper develops a high-throughput method to identify fluoroprobes that distinguish between tau fibril polymorphs using paDSF and chemical screening.

The paper uses cryo-EM to show that tau filaments in Down syndrome share the same structure as those in Alzheimer's disease.

This paper designs drug-binding proteins with low nanomolar affinity and predictable specificity using computational methods, validated by experimental measurements and structural analysis.

This paper reports the X-ray crystal structure of a teixobactin analogue, revealing its supramolecular assembly and anion binding interactions.

This paper introduces a computational method to design peptides that bind specific conformers of α-synuclein fibrils, resulting in a peptide that slows amyloid formation and shows high specificity.

The paper introduces EMBER, a spectral imaging method using machine learning to identify amyloid strains in neurodegenerative diseases.