Biology subjects

Tam, K. Y.

Publications and source records attributed to Tam, K. Y..

3 recordsLinked to original sources

bioRxiv2026

EukaUTR: a foundation model for functional modelling and design of eukaryotic 3' UTRs

Eukaryotic mRNA 3' UTRs encode regulatory information that shapes post-transcriptional control, RNA fate and gene expression. However, a 3' UTR-specific foundation model that spans broad eukaryotic sequence diversity while supporting both functional prediction and sequence design is lacking. Here we present EukaUTR, a 3' UTR-specific foundation model trained on a large-scale eukaryotic 3' UTR sequence corpus spanning diverse evolutionary lineages. Across 13 prediction tasks spanning post-transcriptional regulation, RNA fate and expression output, EukaUTR models matched or exceeded the strongest external baselines on nearly all tasks, with relative improvements of up to 27.45%. EukaUTR also generated de novo 3' UTRs with natural-like sequence and regulatory properties. EukaUTR-Guide further derived stability-associated signals from small sequence sets to guide editing towards enhanced predicted stability. Together, EukaUTR provides a sequence-to-function-to-design framework for transferable 3' UTR modelling and design.

bioinformatics↗

bioRxiv2026

CodonMamba: a foundation model for programmable mRNA coding sequence design

Although mRNA codon language models provide a generalizable framework for biological sequence design, effective CDS design requires both a learned sequence design space that captures biological constraints and context-configurable design preferences. Here we present CodonMamba, a codon language model framework for mRNA prediction and programmable CDS design. Pretrained on large-scale coding-sequence corpora, CodonMamba achieved state-of-the-art performance across a comprehensive benchmark of 12 mRNA prediction tasks, ranking first on 10 of 12 tasks. Furthermore, CodonMamba establishes a programmable CDS design framework, transforming codon optimization from a process dependent on preferences embedded during model training into an inference-time steerable generation framework. By introducing user-specified codon usage priors during generation, CodonMamba enables inference-time steering toward host- or application-specific codon preferences without retraining. In design experiments, CodonMamba enabled coordinated optimization over multiple design-relevant sequence properties and demonstrated programmable cross-host CDS retargeting through inference-time prior switching, while preserving most model-derived codon choices. Together, these results establish CodonMamba as a programmable foundation model framework for CDS design, enabling context-specific mRNA sequence engineering and highlighting a promising direction for precise mRNA design using foundation models.

bioinformatics↗

bioRxiv2017

Association of Vancomycin with Lipid Vesicles

Antibiotics plays a pivotal role in modern medicine for the treatment of bacterial infection in patients. Membrane defines the boundary between single cell and its environment and is a main target for antibacterial agents. To better understand the mechanism of antibiotics action on microbes, we utilized liposome as membrane mimic model to study antibiotics interaction with bacterial membrane by variety of biophysical methods. Isothermal calorimetry and fluorescence photometry experiments were performed to examine interaction between antibiotics and liposome. We found that vancomycin, one of the most important antibiotics for the treatment of serious infections by gram-positive bacteria, binds to the liposome. The association between the drug and the liposome does not involve the tail part of the lipids. Moreover, the binding affinity increases along with the increment of liposome size. Of three major lipid components, phosphatidylglycerol is the preferential target for vancomycin binding. We also showed that vancomycin associates with vesicle derived from Staphylococcus aureus membrane in a similar manner as the binding to liposome. Our data suggested that vancomycin associates with bacterial membrane through direct interaction with lipid head groups with the extent of the association depending very much on specific type of lipids and curvature of local membrane structure.

biochemistry↗