Kotaro Tsuboyama

This study introduces TISCA2 and IncepTIS to map and predict stress-responsive translation initiation sites, revealing tissue-specific patterns and enabling engineered mRNA 5' leaders.

The paper develops a scalable assay and a protein language model to predict monomeric yield of proteins in E. coli, showing it can be learned and designed.

The paper measures protein stability for 1.8 million domains using a new experimental method and develops models that accurately predict stability with high precision.

The paper measures conformational fluctuations of protein domains using hydrogen-deuterium exchange mass spectrometry and uses computational modeling to design mutations that stabilize low-stability segments.

This study presents a joint optimization approach for modeling protein conformational landscapes and sequence design, showing improved stability prediction and sequence generation.

The paper develops a method to engineer lipid-binding probes by screening protein-lipid interactions and identifies structural features that enhance binding to phosphatidylinositol phosphates.

The paper designs over ten thousand {square}{beta}{beta}{square} proteins, identifies stability determinants, and demonstrates how high-throughput methods can improve protein design success rates.

This study identifies Hero proteins as heat-resistant proteins that stabilize other proteins and prevent aggregation, extending lifespan in Drosophila.