Megamolecule Self-Assembly Networks: A Combined Computational and Experimental Design Strategy

Area of Science:

• Biomolecular engineering
• Computational chemistry
• Materials science

Background:

• Designing large biomolecular materials for self-assembly is complex.
• Traditional methods are time-consuming and labor-intensive.
• Metal-mediated self-assembly offers a route to ordered networks.

Purpose of the Study:

• To develop a simulation-guided strategy for designing megamolecule building blocks.
• To optimize peptide linkers within fusion proteins for enhanced self-assembly.
• To streamline the design process for large biomolecular materials.

Main Methods:

• Utilized all-atom molecular dynamics and enhanced sampling simulations.
• Employed large-scale coarse-grained molecular dynamics simulations.
• Designed and evaluated 11 candidate megamolecules.

Main Results:

• Identified (EAAAK)2 as the most promising peptide linker.
• Determined optimal experimental conditions for self-assembly.
• Validated simulation findings through experimental results.

Conclusions:

• The study provides valuable insights into megamolecule network self-assembly.
• A novel and general strategy for biomolecular material design was established.
• Systematic bottom-up coarse-grained simulations are effective for large biomolecular designs.