Jing Zhang

TriFlow, discrete flow-matching sequence designer with global three-track structure attention, raises in silico binder success over ProteinMPNN in RFdiffusion and BindCraft-style pipelines on 500 targets and flags functional sites.

The paper uses genomic analysis to distinguish two cryptic Atrytonopsis skipper species, revealing their genetic uniqueness, phylogenetic relationship, and geographic distribution.

This study identifies NUDT5 as a regulator of purine metabolism and thiopurine sensitivity by interacting with PPAT, independent of its known enzymatic activity.

This paper evaluates progress in predicting protein complex structures in CASP16, finding moderate improvements but ongoing challenges in model ranking, stoichiometry prediction, and handling difficult targets.

Across 42 CASP16 nucleic acid targets, accuracy stayed tied to template availability; human experts (Vfold first) led servers and AlphaFold 3, though servers now improve on templates on average.

The paper predicts 18,316 human protein-protein interactions with 90% precision using enhanced coevolutionary analysis and a new deep learning model.

This paper assesses CASP16 monomer structure prediction, showing near-solution for single-domain folds but highlighting challenges in truncated sequences and model ranking.

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

This study uses deep learning to predict protein-protein interactions in bacterial pathogens, identifying new complexes involving essential genes and virulence factors, with half of tested predictions validated.

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.

The paper uses AlphaFold to predict cancer protein-protein interactions and maps somatic mutations to these interfaces, revealing functional insights and potential drug targets.

The paper uses deep learning to predict mitochondrial protein complexes and interactions, identifying known and novel protein pairs with structural and functional insights.

This study investigates the genetic basis of hibernation in the meadow jumping mouse by analyzing its genome and transcriptome, identifying key genes and pathways involved in hibernation-related processes.

RADAR identifies variants affecting RNA-binding protein function by integrating conservation, RNA structure, and tissue-specific data to prioritize noncoding variants.

The paper sequences all butterfly species in North America north of Mexico to study diversification patterns and adaptation mechanisms across their evolutionary history.