MULTI-evolve: Rapid directed evolution guided by protein language models and epistatic interactions. скачать в хорошем качестве

MULTI-evolve: Rapid directed evolution guided by protein language models and epistatic interactions.

Vincent Tran of the Patrick Hsu Lab discusses his team's publication in the journal Science on machine learning-guided engineering of hyperactive multi-mutant proteins. The authors developed MULTI-evolve, an end-to-end framework that trains neural networks on small datasets of ~200 strategic variants to predict which combinations of beneficial mutations will work synergistically. Applied to three proteins, the framework achieved up to 256-fold improvement in activity while compressing what traditionally takes 5-10 iterative experimental rounds into weeks. Citation: Tran, V.Q., Nemeth, M., Bartie, L.J., Chandrasekaran, S.S., Fanton, A., Moon, H.C., Hie, B.L., Konermann, S., & Hsu, P.D. (2026). Rapid directed evolution guided by protein language models and epistatic interactions. Science. https://doi.org/10.1126/science.aea1820 GitHub Link: https://github.com/ArcInstitute/MULTI... 0:00 Introduction 0:11 Enhancing protein function 0:40 Traditional directed evolution 1:08 Machine learning-guided directed evolution (MLDE) 2:05 Key components for rapid protein evolution 2:27 Efficient discovery of function-enhancing mutations 3:23 MULTI-evolve models enable data-efficient extrapolation 4:14 Reliable construction of multi-mutant genes 4:36 MULTI-evolve framework 5:24 Developing a rapid protein evolution approach 6:09 MULTI-evolve applied to three distinct proteins 6:38 Acknowledgements