Jing Huang

The paper introduces REAL-SWIT, a generative framework for ultra-large virtual screening that efficiently explores chemical space by generating molecules with favorable docking scores.

The paper shows Ufd1's UT3 domain unfolds ubiquitin in an ATP-independent manner, enabling Cdc48/p97 to process polyubiquitinated substrates.

This study introduces individual docking, a method that improves virtual screening by generating receptor conformations specifically for each ligand, showing better enrichment factors than standard docking.

This paper introduces AFEX, a geometry-constrained framework for predicting active kinase domains, achieving high accuracy across the human kinome.

This paper introduces AFLF, a method to generate diverse protein conformations from sequence using AlphaFold's latent space, showing it captures structural fluctuations and binding sites.

This study shows extending the spacer in SuperFi-Cas9 restores cleavage activity while maintaining specificity, offering a strategy to improve CRISPR efficiency and stability.

This paper introduces ErePOC, a contrastive learning model for representing protein binding pockets, which improves DEL target selection by identifying shared characteristics and functional similarities.

This study shows gangliosides modulate EV secretion and misfolded protein cargo, with GM1 enhancing EV release and reducing mutant huntingtin burden in HD models.

This study uses molecular dynamics simulations to explore the Na+/H+ antiport mechanism of NhaA, revealing conformational transitions and the role of Lys300 in transport.

The paper reports cryo-EM structures of Arabidopsis thaliana AUX1 in IAA-bound and IAA-free states, revealing the molecular mechanism of auxin binding and transport.

This study benchmarks three MLFF models on their ability to capture hydrogen-bond cooperativity in N-methylacetamide polymers, finding MACE-OFF most accurate.

This study quantifies the cooperativity of protein backbone hydrogen bonds using quantum chemistry calculations and evaluates force fields' ability to reproduce cooperative effects.

AFEXplorer improves protein structure prediction by incorporating user-defined constraints, enhancing the generation of functional conformations.

The paper benchmarks protein structure prediction methods on alternative conformations of membrane proteins, finding biases in deep learning models and showing AlphaFold's energy model is state-neutral.

The paper identifies topological links in AlphaFold-Multimer-predicted protein complexes and proposes a method to detect them, finding about 0.7% of structures contain such links.

This paper introduces a method that integrates target structural information into molecular generative models to improve drug discovery by increasing hit generation and generating molecules similar to known ligands.