Automated design evolution of stereochemically randomized protein foldamers

Area of Science:

• Biochemistry
• Materials Science
• Computational Biology

Background:

• Protein engineering aims to create novel materials with tailored functions.
• Diversifying protein stereochemistry offers a vast, unexplored design space.
• Current methods lack the ability to create diverse, protein-like structures.

Purpose of the Study:

• To explore the de novo design of protein-like foldamers with diversified stereochemistry.
• To investigate the use of L- and D-α-amino acids in foldamer design.
• To develop a computational framework for designing and optimizing novel foldamers.

Main Methods:

• Sequence design algorithm incorporating chirality and amino acid sequence.
• Molecular dynamics simulations to fold stereochemically diverse homopolypeptides.
• Automated Repetitive Simulated Annealing Molecular Dynamics (AR-SAMD) for structure evaluation.
• Dead End Elimination cum Monte Carlo optimization for sequence refinement.

Main Results:

• Successfully designed and simulated stereochemically diverse homopolypeptides.
• Developed a fitness function to identify optimal foldamer structures.
• Identified highly scored poly-leucine foldamers of specific lengths.
• Optimized sequences for enhanced foldamer properties.

Conclusions:

• Demonstrated a novel computational approach for de novo foldamer design.
• Showcased the potential of incorporating L- and D-amino acids for expanded protein-like structures.
• Paved the way for designing advanced smart materials using engineered foldamers.