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Updated: Jun 28, 2026
15:00
Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli
Published on: August 18, 2023
Biophysical fitness landscape design traps viral evolution
Vaibhav Mohanty1,2,3, Eugene I Shakhnovich1
1Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138.
Summary
We introduce Fitness Landscape Design (FLD) to precisely control protein evolution by shaping fitness landscapes. This method enables designing antibodies and proactive vaccines to guide evolutionary trajectories and prevent viral escape variants.
Area of Science:
- Biophysics
- Protein Engineering
- Evolutionary Biology
Background:
- Protein evolution is often modeled as movement on a fitness landscape.
- Current methods allow protein design but lack control over the evolutionary landscape itself.
- A need exists for methods to quantitatively design and reshape fitness landscapes for predictable evolutionary outcomes.
Purpose of the Study:
- To introduce foundational principles of Fitness Landscape Design (FLD) for precise control over protein evolution.
- To develop algorithms for quantitatively customizing biophysical fitness landscapes.
- To demonstrate FLD's application in antibody design and preemptive vaccine development against SARS-CoV-2.
Main Methods:
- Development of Fitness Landscape Design (FLD) algorithms.
- Utilizing stochastic optimization of a chemically derived biophysical fitness model.
- Application of FLD to design antibody ensembles and analyze SARS-CoV-2 evolution.
Main Results:
- FLD enables quantitative customization of fitness landscapes, controlling evolutionary trajectories.
- Optimal antibody ensembles were discovered, guiding target proteins toward specified fitness landscapes.
- FLD was applied to suppress fitness of SARS-CoV-2 neutral networks and design preemptive vaccines.
Conclusions:
- Fitness Landscape Design provides a powerful framework for engineering predictable evolutionary pathways.
- FLD offers robust control over long-term evolutionary outcomes in protein engineering.
- This approach has significant implications for developing targeted therapeutics, including proactive vaccines.
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