Study quantifies monomeric yield for 39,000+ protein variants, showing expression is learnable
KevinKaichuang · x · 2026-11-13
Kevin Kaichuang highlights a bioRxiv preprint from a University of Tokyo team (Selena Setsu, Kotaro Tsuboyama et al.) performing massively parallel quantification of monomeric protein yield in E. coli across more than 39,000 natural variants, de novo proteins, and nanobodies. Using barcoded peptide isolation with MS quantification, the study shows that the sequence determinants of expression are learnable, enabling yield prediction from sequence.
Related event: Tokyo Team Quantifies Expression of 39,000 Protein Variants(2 posts)→
More from Research
- New research tackles the agent experience problem: knowledge trapped in weights — khademinori · 2026-11-13
- Computational chemistry platform Rowan launches gamified Intro to Optimization games — CatAstro_Piyush · 2026-11-13
- One Chart Explains the Linear Algebra Behind Modern AI, from Vectors to SVD — HankYeomans · 2026-11-13
- Codex agent shrinks a 27M-param transformer to 11.46M with equal loss via weight sharing — yoshiyama_akira · 2026-11-13
- Google open-sources Harness²: Agent = Model + Harness, recursive agent harnessing framework — 2C_ornot2C · 2026-11-13
- Question's Gambit: first-step reranking lifts agentic search accuracy to 90.5% — CShorten30 · 2026-11-13