DisruPPI: structure-based computational redesign algorithm for protein binding disruption.

Area of Science:

• Protein engineering
• Computational biology
• Biochemistry

Background:

• Disrupting protein-protein interactions has applications in antibody recognition, protein form, and signaling.
• Designing affinity-enhancing mutations is challenging, but computational methods can identify affinity-reducing mutations.
• Existing methods like FoldX and INT5 accurately predict mutational effects on binding.

Purpose of the Study:

• To develop a computational method, DisruPPI (DISRUpting Protein-Protein Interactions), for designing protein variants with disrupted interactions.
• To simultaneously optimize mutations for both interaction disruption and protein stability.
• To create a tool that leverages computational predictions to design functional protein variants.

Main Methods:

• DisruPPI integrates an INT5-based disruption score with an AMBER-based stability assessment.
• The method was applied to diverse protein targets to disrupt interactions.
• Retrospective evaluations used three case studies comparing DisruPPI variants to experimental data.

Main Results:

• DisruPPI identified diverse interaction-disrupting and stability-preserving variants more efficiently than previous approaches.
• In a prospective study, DisruPPI designed EGFP variants with significantly reduced nanobody binding.
• These EGFP variants maintained their function and thermostability.

Conclusions:

• DisruPPI effectively designs protein variants with disrupted interactions and preserved stability.
• The method demonstrates potential for removing epitopes from therapeutic proteins.
• DisruPPI is available in the EpiSweep package for academic use.