T Liu

De novo protein design in an expanded chemical space

This paper introduces CheMoDesign, a framework for de novo protein design with user-specified chemistry, demonstrating its ability to generate proteins with chemistry-dependent recognition and reactivity.

Promoter architecture decodes response regulator phosphorylation into distinct transcriptional logics in Pseudomonas aeruginosa

The paper shows how promoter architecture in Pseudomonas aeruginosa determines whether phosphorylation of the ParR response regulator activates, represses, or reverses gene expression.

Therapeutic targeting of MYC- and MYCN-driven medulloblastoma with a novel MYC degrader molecule

This study identifies UNSW-SC-22 as a novel MYC/MYCN-targeting molecule that crosses the blood-brain barrier and shows potent preclinical efficacy in medulloblastoma.

A cross-frequency architecture links biological and behavioral periodicities

The paper shows that biological and behavioral periodicities form a cross-frequency architecture with specific irrational scaling, suggesting an emergent temporal reference frame.

Super-Resolution Visual ProteomEx for Hard Tissues and Clinical Samples

The paper introduces microProteomEx, a platform combining tissue expansion, imaging, and proteomics to achieve high-resolution spatial proteomics in hard tissues and clinical samples.

Learning Perturbation Effects Through Contrastive Alignment of Multimodal Biological Embeddings

This paper introduces PertOmni, a multimodal framework that aligns transcriptomic data with text and image embeddings to improve perturbation effect prediction.

Ancestral Musaceae karyotype reconstruction provides insights into chromosome evolution and bract coloration

This paper reconstructs the ancestral Musaceae karyotype and links chromosome evolution to bract coloration diversity through genomic and transcriptomic analyses.

To Predict is to Design: Unlocking Generative Capabilities in All-Atom Structure Predictors via Geometric Score Distillation

This paper introduces DREAM, a framework that enables protein design by inverting all-atom diffusion predictors, allowing gradient-based optimization of complex modalities like cyclic peptides.