Carscadden, J. K. | bioRxiv Science

bioRxiv2026

Antizyme regulates polyamine uptake via ATP13A3

Intracellular polyamine levels are tightly regulated and frequently elevated in cancer. While the regulation of polyamine synthesis is well characterized, the regulation of polyamine uptake is poorly understood. Here we identify ATP13A3 as a plasma membrane polyamine transporter. An increase in intracellular polyamine levels, due to polyamine supplementation or induction of polyamine synthesis, causes rapid internalization of the transporter and inhibition of polyamine uptake. Mechanistically, increased polyamine concentrations lead to expression of antizyme (AZ) which binds ATP13A3 and triggers its internalization. Mutations in the AZ binding site of ATP13A3 prevent AZ binding and lead to polyamine toxicity through uncontrolled polyamine influx. These findings establish ATP13A3 as a key polyamine transporter and provide a framework for targeting polyamine metabolism in cancer.

cell biology↗

bioRxiv2024

Rapid protein evolution by few-shot learning with a protein language model

Directed evolution of proteins is critical for applications in basic biological research, therapeutics, diagnostics, and sustainability. However, directed evolution methods are labor intensive, cannot efficiently optimize over multiple protein properties, and are often trapped by local maxima. In silico-directed evolution methods incorporating protein language models (PLMs) have the potential to accelerate this engineering process, but current approaches fail to generalize across diverse protein families. We introduce EVOLVEpro, a few-shot active learning framework to rapidly improve protein activity using a combination of PLMs and protein activity predictors, achieving improved activity with as few as four rounds of evolution. EVOLVEpro substantially enhances the efficiency and effectiveness of in silico protein evolution, surpassing current state-of-the-art methods and yielding proteins with up to 100-fold improvement of desired properties. We showcase EVOLVEpro for five proteins across three applications: T7 RNA polymerase for RNA production, a miniature CRISPR nuclease, a prime editor, and an integrase for genome editing, and a monoclonal antibody for epitope binding. These results demonstrate the advantages of few-shot active learning with small amounts of experimental data over zero-shot predictions. EVOLVEpro paves the way for broader applications of AI-guided protein engineering in biology and medicine.

bioengineering↗