Evolutionary Pareto-optimization of stably folding peptides

Area of Science:

• Biochemistry
• Computational Biology
• Structural Biology

Background:

• Peptides are typically flexible, unlike stable proteins.
• Few stably folding peptides are known, but more may exist.
• Stable peptides have potential applications in research and medicine.

Purpose of the Study:

• To develop a computational method for discovering novel, conformationally stable peptides.
• To explore the potential for peptides with enhanced stability and unique folds.

Main Methods:

• Developed an evolutionary algorithm for optimizing peptide sequences based on stability and accessibility.
• Applied the algorithm to a known stable peptide (Villin Headpiece) with sequence perturbations.
• Synthesized and characterized a predicted stable mutant using nuclear magnetic resonance (NMR) spectroscopy and circular dichroism (CD).

Main Results:

• The algorithm generated two clusters of peptide sequences with predicted higher stability than the wild-type.
• One cluster exhibited a fold distinct from the native Villin Headpiece.
• Experimental validation confirmed the predicted structure and stability of a synthesized mutant.
• Eight additional stable peptide sequences and structures were predicted, including five with non-native folds.

Conclusions:

• The findings suggest a larger pool of conformationally stable peptides than currently known.
• Small peptide fold classes may contain distinct sub-folds.
• The developed algorithm is effective for identifying novel, stable peptide structures.