Biophysical fitness landscape design traps viral evolution

Area of Science:

• Biophysics
• Protein Engineering
• Computational Biology

Background:

• Protein fitness is crucial for viral evolution and drug resistance.
• Controlling protein evolution is key to developing effective therapeutics.
• Current methods lack precise control over protein fitness landscapes.

Purpose of the Study:

• To establish principles for designing customizable protein fitness landscapes.
• To engineer antibody ensembles that create evolutionary traps for viruses.
• To demonstrate control over viral evolution and pathogen fitness.

Main Methods:

• Deriving a relationship between protein fitness, binding affinities, and antibody interactions.
• Utilizing stochastic optimization to design antibody ensembles for target fitness landscapes.
• Simulating viral evolution using in silico serial dilution experiments with chemical reaction dynamics.
• Applying the design protocol to SARS-CoV-2 genotype networks.

Main Results:

• Protein fitness is designable and can be tuned by antibody selection.
• Successfully simulated and validated fitness landscape design using in silico experiments.
• Demonstrated control over SARS-CoV-2 genotype network fitness, reducing absolute fitness.
• Developed an iterative protocol for designing vaccination targets that restrict escape variants.

Conclusions:

• Biophysical fitness landscape design offers a novel approach for prescient vaccine and antibody development.
• This strategy enables proactive control over pathogen evolution, anticipating escape mechanisms.
• Opens new avenues for designing peptides and other protein-based therapeutics.