Computational protein design suggests that human PCNA-partner interactions are not optimized for affinity.

Area of Science:

• Molecular Biology
• Biochemistry
• Structural Biology

Background:

• Increasing protein binding affinity is crucial for biological research and applications.
• Directed protein evolution via mutant library screening is the primary method for achieving this.
• Proliferating cell nuclear antigen (PCNA) is a key eukaryotic protein regulating DNA replication and repair through interactions with numerous partners.

Purpose of the Study:

• To utilize computational design to engineer human PCNA mutants with enhanced binding affinity for its partners.
• To explore novel mutation sites outside the primary binding interface for affinity enhancement.
• To validate the efficacy of computational design in creating high-affinity PCNA variants.

Main Methods:

• Employed computational design to identify specific double mutations in human PCNA.
• Focused mutations in regions outside the established PCNA partner binding site.
• Predicted enhanced binding through novel hydrophobic interactions with conserved partner residues.
• Experimentally validated the increased binding affinities of designed mutants with four PCNA partners.

Main Results:

• Successfully generated human PCNA mutants with significantly enhanced binding affinities for multiple partners.
• Identified double mutations outside the main binding site that increase affinity.
• Demonstrated that computational design can uncover non-intuitive sites for affinity enhancement.
• Experimental validation confirmed the predicted affinity increases for four distinct PCNA partners.

Conclusions:

• Computational design is an effective strategy for engineering increased protein-protein interaction affinities.
• The designed PCNA mutants serve as valuable tools for in vitro and in vivo studies of DNA replication and repair.
• The findings suggest that interaction affinity for PCNA partners may not be a fully evolutionarily optimized trait.