Lisa N. Kinch

This study identifies metabolic reprogramming and immune signaling modulation by Vibrio parahaemolyticus during host cell invasion and infection.

This study investigates how ANGPTL3 and ANGPTL8 regulate LDL cholesterol levels through their cleavage products and interactions with lipases, showing that both proteins are required for maximal lipolysis inhibition.

This study identifies geranyl- and farnesyl-pyrophosphate as FicD inhibitors that differentially affect disease-causing variants, providing insights for therapeutic targeting of UPR.

This study identifies genetic factors associated with invasiveness in Escherichia coli strains from IBD patients using whole genome sequencing and a modified invasion assay.

ABHD5 binds PNPLA3 more strongly than ATGL regardless of the I148M variant; steatosis from PNPLA3(148M) needs ATGL and is reversed by ABHD5 overexpression.

This study investigates the role of FicD in regulating BiP AMPylation and UPR signaling in mouse embryonic fibroblasts under different stress conditions, revealing its impact on stress recovery and protein secretion.

The paper classifies proteins from Salmonella enterica pangenome, identifies potentially pathogenic proteins, and reveals their clustering in the genome.

The paper classifies domains in 48 proteomes from the AlphaFold Database using DPAM, finding differences in domain composition between eukaryotes and bacteria.

This study identifies ZNF692 as a nucleolar scaffold that enhances 40S ribosome maturation and translation efficiency in rapidly proliferating cells, particularly in cancer cells.

The paper introduces a 3-track network for accurate protein structure and interaction prediction, achieving performance close to DeepMind's CASP14 results and enabling rapid modeling of protein complexes from sequence data.

The paper identifies a SNP in the cut gene responsible for yellow coloration in bumble bees, showing it affects protein structure without major external phenotypes.

The paper develops DeepSAV, a deep-learning model to predict the pathogenicity of single-amino acid variations, and uses it to assess mutation severity across genes, identifying intolerant and tolerant genes for different disease categories.