New compstatin variants through two de novo protein design frameworks

Area of Science:

• Biochemistry
• Computational Biology
• Protein Design

Background:

• Compstatin is a peptide inhibitor of the complement system.
• The complement system plays a role in immune responses and various diseases.
• Developing novel compstatin variants with improved binding affinity is crucial for therapeutic applications.

Purpose of the Study:

• To apply two de novo protein design frameworks for discovering new compstatin variants.
• To predict and experimentally validate compstatin variants with enhanced binding affinities to C3c.
• To elucidate key sequence positions influencing compstatin binding affinity.

Main Methods:

• Utilized two de novo protein design frameworks: one based on sequence selection and fold specificity, the other on sequence selection and approximate binding affinity calculations.
• Applied these frameworks to the C3c-compstatin complex.
• Performed experimental validation of predicted compstatin variants.

Main Results:

• Successfully predicted and experimentally validated new compstatin variants with improved binding affinities to C3c.
• Identified key amino acid positions (1, 4, 9, 10, 11, 13) significantly affecting compstatin binding affinity.
• Determined specific amino acid preferences at these key positions (e.g., Trp at 4 and 13, Asn at 1, 9, 10, Gln at 11).

Conclusions:

• The applied de novo protein design frameworks are effective for discovering high-affinity compstatin variants.
• Computational predictions of binding affinity and experimental validation were successful.
• Structural insights into C3c-bound peptide analogs were gained, aiding future drug design.