Energy Landscape of the Designed Protein Top7

Area of Science:

• Biophysics
• Computational Biology
• Protein Science

Background:

• Proteins fold via funnelled energy landscapes, but topological traps can impede folding.
• De novo designed proteins may exhibit increased topological trapping due to lack of evolutionary optimization.
• Structure-based models (SBMs) isolate topological effects on protein folding landscapes.

Purpose of the Study:

• To compare the topological trapping and potential energy landscapes (PELs) of a designed protein (Top7) and a natural protein (S6).
• To quantify the impact of protein topology on folding pathways and identify kinetic traps.
• To investigate how non-native interactions and coarse-graining affect frustration in protein folding.

Main Methods:

• Utilized structure-based models (SBMs) to simulate protein folding and analyze energy landscapes.
• Compared the designed protein Top7 with the natural ribosomal protein S6.
• Quantified topological trapping and frustration using a frustration density parameter and probability contact maps.

Main Results:

• The designed protein Top7 exhibited a more frustrated potential energy landscape (PEL) than the natural protein S6, even within an SBM.
• Adding coarse-grained side-chains increased frustration, while adding non-native hydrophobic interactions narrowed the folding funnel.
• Identified potential kinetic traps and quantified energetic barriers for both proteins.

Conclusions:

• Protein topology significantly influences the folding energy landscape and can lead to kinetic traps, particularly in designed proteins.
• Quantitative analysis of frustration density and folding routes is crucial for understanding and optimizing protein design.
• This work provides insights for designing proteins with optimized, structure-seeking landscapes, mimicking evolutionary processes.