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This study uses deep learning to optimize macromolecular self-assembly, finding efficient strategies to avoid kinetic traps and maximize product yield. Diverse subunit designs and external control protocols prove most effective for high-yield assembly.

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Area of Science:

  • Biophysics
  • Systems Biology
  • Computational Chemistry

Background:

  • Macromolecular self-assembly, crucial for biological structures like ribosomes and viral capsids, often suffers from reduced yields due to kinetic traps.
  • Designing synthetic assembly systems requires understanding biological strategies to avoid these traps and achieve efficient assembly.

Conclusions:

  • Multiple strategies exist to eliminate kinetic trapping and maximize assembly yield in complex systems.
  • Subunit diversity significantly enhances assembly efficiency, particularly with external control protocols.
  • The study identifies universal scaling laws for the cost of kinetic trapping.