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Efficient, Few-shot Directed Evolution With Energy Rank Alignment.

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Efficient, Few-shot Directed Evolution With Energy Rank Alignment.

Related Experiment Video

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

Efficient, Few-shot Directed Evolution with Energy Rank Alignment.

Sebastian Ibarraran¹, Shriram Chennakesavalu¹, Frank Hu¹

¹Department of Chemistry, Stanford University, Stanford CA 94305.

Biorxiv : the Preprint Server for Biology

|February 12, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a novel method for protein engineering using adapted protein language models. This approach efficiently identifies high-fitness protein sequences with less experimental data, improving directed evolution strategies.

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Area of Science:

Biochemistry
Computational Biology
Protein Engineering

Background:

Directed evolution is crucial for protein engineering but is limited by the high cost of experimental data.
Current machine learning methods for sequence selection are constrained by sparse data, restricting model complexity.

Purpose of the Study:

To develop a more efficient method for protein engineering by adapting large-scale protein language models.
To leverage natural protein sequence distributions for navigating complex fitness landscapes.

Main Methods:

Adapted large-scale pre-trained protein language models using experimental data.
Employed a post-training algorithm grounded in statistical physics.
Utilized quantitative experimental rankings to generate diverse, high-fitness sequences.

Main Results:

Achieved remarkable efficiency improvements in protein engineering.
Required fewer experimental data points compared to competing methods.
The adapted models provided insights into the biophysical characteristics of high-fitness sequences.

Conclusions:

Adapting protein language models offers a powerful alternative for efficient protein engineering.
This method effectively navigates high-dimensional fitness landscapes.
The adapted models are interpretable and enhance understanding of protein properties.