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Related Experiment Video

Updated: Jun 20, 2026

Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli

Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli

Published on: March 16, 2011

Rapid directed evolution guided by protein language models and epistatic interactions.

Vincent Q Tran^1,2, Matthew Nemeth¹, Liam J Bartie¹

¹Arc Institute, Palo Alto, CA, USA.

Science (New York, N.Y.)

|February 19, 2026

Summary

We developed MULTI-evolve, a rapid protein engineering framework to efficiently discover synergistic mutations. This machine learning-guided approach accelerates the discovery of improved protein functions.

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Area of Science:

Biochemistry and Molecular Biology
Computational Biology
Protein Engineering

Background:

Protein engineering faces challenges in navigating vast sequence spaces for synergistic mutations.
Current methods like stepwise mutation stacking and machine learning are often inefficient or resource-intensive.

Purpose of the Study:

To introduce MULTI-evolve, a novel framework for systematic engineering of protein multimutants.
To overcome limitations in gene synthesis and accelerate the discovery of beneficial mutations.

Main Methods:

Utilizing protein language models or functional data combined with epistatic modeling to predict synergistic mutation combinations.
Employing MULTI-assembly, a high-efficiency mutagenesis technique for assembling multikilobase sequences.

Main Results:

Achieved up to 10-fold protein improvements in a single round of directed evolution across three different proteins.
Demonstrated the framework's ability to streamline end-to-end multimutant engineering.

Conclusions:

MULTI-evolve offers a rapid and efficient solution for designing proteins with enhanced functions.
The framework is applicable to a broad range of protein types and functionalities.