Scaffold Matcher: A CMA-ES based algorithm for identifying hotspot aligned peptidomimetic scaffolds.

Area of Science:

• Computational biology
• Drug discovery
• Protein-protein interactions

Background:

• Aberrant protein interactions drive disease, necessitating targeted inhibitors.
• Peptidomimetic scaffolds are a promising strategy for designing these inhibitors.
• A key challenge is aligning scaffolds to natural protein interfaces.

Purpose of the Study:

• To present the Scaffold Matcher algorithm for aligning molecular scaffolds to protein interaction hotspots.
• To implement and evaluate the covariance matrix adaptation evolution strategy (CMA-ES) for scaffold optimization within Rosetta.
• To demonstrate the algorithm's utility in designing potential protein interaction inhibitors.

Main Methods:

• Developed the Scaffold Matcher algorithm to align molecular scaffolds onto hotspot residues.
• Integrated CMA-ES, a derivative-free optimization algorithm, into Rosetta for scaffold degree-of-freedom optimization.
• Evaluated CMA-ES against other Rosetta algorithms using the FlexPepDock Benchmark (26 peptides).

Main Results:

• CMA-ES outperformed Rosetta's default minimizer, Monte Carlo, and Genetic algorithms, finding the lowest energy conformation for all 26 benchmark peptides.
• Successfully applied Scaffold Matcher with CMA-ES to a peptidomimetic scaffold targeting SARS-CoV-2 main protease.
• Demonstrated CMA-ES as an effective optimization method for macromolecular modeling with complex energy landscapes.

Conclusions:

• The Scaffold Matcher algorithm with CMA-ES provides an effective method for aligning peptidomimetic scaffolds to protein interfaces.
• This approach facilitates the identification of initial conformations for designing high-affinity protein interaction inhibitors.
• The integration of CMA-ES offers a novel optimization strategy for challenging molecular modeling problems.