Engineering folding dynamics from two-state to downhill: application to λ-repressor.

Area of Science:

• Computational Biology
• Protein Folding Dynamics
• Biophysics

Background:

• Achieving downhill protein folding is crucial for understanding protein dynamics.
• Experimental methods for accelerating protein folding through mutation are demanding.
• Computational tools can assist in identifying key residues for protein engineering.

Purpose of the Study:

• To present a computational method for screening protein engineering hot spots.
• To identify mutations that reduce folding cooperativity and facilitate downhill folding.
• To analyze folding kinetics of selected mutants using a Markov-state model.

Main Methods:

• Sampling the energy landscape of a pseudo-wild-type protein.
• Investigating the impact of point mutations on the energy landscape.
• Utilizing a novel cooperativity metric to identify critical residues.
• Characterizing folding dynamics and analyzing kinetics within a Markov-state model framework.

Main Results:

• The computational method successfully identified residues leading to decreased folding cooperativity.
• Simulations on a λ-repressor model demonstrated a significant reduction in the folding barrier.
• The folding barrier was reduced from ~4 k(B)T to ~1 k(B)T, facilitating downhill folding.

Conclusions:

• The developed method effectively screens for protein engineering hot spots.
• This approach aids in achieving the downhill folding regime for protein fragments.
• The findings provide insights into modulating protein folding kinetics through targeted mutations.