Protocol for iterative optimization of modified peptides bound to protein targets

Area of Science:

• Computational biology
• Biochemistry
• Drug discovery

Background:

• Therapeutic peptides face challenges with degradation and instability.
• Non-natural amino acids offer a strategy to improve peptide stability and target affinity.

Purpose of the Study:

• To present a computational pipeline (mPARCE) for optimizing peptide therapeutics.
• To enhance peptide binding affinity and stability using non-natural amino acids.

Main Methods:

• An iterative computational evolution algorithm inspired by the PARCE protocol.
• Utilizing Rosetta framework modules for single-point mutations and incorporating non-natural amino acids.
• Estimating protein-peptide binding affinity through conformational sampling and consensus scoring.

Main Results:

• The mPARCE pipeline successfully optimizes peptide sequences.
• Benchmarking demonstrated the effectiveness of the sampling/scoring scheme against experimental data.
• A practical application using a known protein-peptide complex was provided.

Conclusions:

• The mPARCE protocol offers a structure- and dynamic-based approach for optimizing therapeutic peptides.
• This computational pipeline enables personalized optimization of peptides with enhanced properties.
• The mPARCE protocol is publicly available for broader research applications.