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Updated: Aug 8, 2026
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Directed Evolution Method in Saccharomyces cerevisiae: Mutant Library Creation and Screening
Published on: April 1, 2016
Improved mutants from directed evolution are biased to orthologous substitutions
Jennifer R Cochran1, Yong-Sung Kim, Shaun M Lippow
1Division of Biological Engineering, Massachusetts Institute of Technology Cambridge, MA 02139, USA.
Protein Engineering, Design & Selection : PEDS
|June 3, 2006
Summary
Researchers engineered human epidermal growth factor (EGF) for higher affinity to the EGF receptor (EGFR). This was achieved by leveraging natural variations found in related species, leading to a 30-fold increase in binding.
Area of Science:
- Protein Engineering
- Molecular Biology
- Biotechnology
Background:
- Directed evolution is a powerful tool for protein engineering.
- Understanding mutation patterns in natural protein variants can guide engineering efforts.
- Epidermal growth factor (EGF) and its receptor (EGFR) are crucial in cell signaling.
Purpose of the Study:
- To engineer human EGF with enhanced binding affinity to EGFR.
- To investigate the role of phylogenetic variation in EGF functional enhancement.
- To develop a novel mutagenesis strategy for protein engineering.
Main Methods:
- Yeast surface display was used for directed evolution of human EGF.
- Statistical analysis of mutant libraries identified mutation biases.
- Shotgun ortholog scanning mutagenesis (SOSM) was developed and applied.
- High-affinity mutants were screened for binding to soluble EGFR ectodomain.
Main Results:
- Mutations in high-affinity EGF variants were biased towards residues found in orthologous EGF species.
- SOSM successfully introduced orthologous mutations into EGF libraries.
- Engineered EGF mutants showed a 30-fold increase in binding affinity to EGFR.
- These engineered EGF variants represent some of the highest affinity proteins developed to date.
Conclusions:
- Phylogenetic variability provides a valuable resource for generating genetic diversity in directed evolution.
- The SOSM method effectively harnesses orthologous information for protein engineering.
- This approach offers a general strategy for improving protein function by leveraging evolutionary insights.
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