Seeking Precise Protein-like Functions from Random Heteropolymer Ensemble and through Dimensionality Reduction

Area of Science:

• Biomimetic chemistry
• Polymer science
• Synthetic biology

Background:

• Proteins perform diverse functions via sequence-structure relationships, but can also operate as statistical ensembles.
• Random heteropolymers (RHPs) are explored as synthetic, scalable, and versatile protein mimetics.
• RHPs can exhibit emergent behaviors like folding, binding, and catalysis.

Purpose of the Study:

• To highlight recent advances in the discovery and mechanistic understanding of functional RHPs.
• To emphasize the utility of RHPs in sustainability, human health, and pharmaceuticals.
• To propose a new framework for creating functional polymers inspired by biological systems.

Main Methods:

• Review of recent literature on functional RHPs.
• Emphasis on emergent behaviors and mechanistic understanding.
• Discussion of accelerating design and discovery through autonomous experimentation, machine learning, and multiscale modeling.

Main Results:

• RHPs function as polymer ensembles with controllable monomer sequences.
• Emergent behaviors of RHPs are observed across various applications.
• Advances in computational and experimental methods accelerate RHP design.

Conclusions:

• RHPs offer a scalable and versatile platform for biomimetic functions.
• Statistical design principles are key to creating functional polymers mirroring biological systems.
• The convergence of AI and modeling promises rapid progress in RHP discovery and application.