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Discovering optimal kinetic pathways for self-assembly using automatic differentiation.

Adip Jhaveri1, Spencer Loggia1, Yian Qian1

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Subunit diversity enhances macromolecular complex self-assembly by expanding parameters, mimicking deep learning. Optimal kinetic protocols, designed or controlled externally, efficiently guide assembly and avoid kinetic traps for high yields.

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Area of Science:

  • Biophysics
  • Systems Biology
  • Computational Biology

Background:

  • Macromolecular complexes assemble from diverse subunits.
  • Self-assembly is a nonequilibrium process requiring efficient pathways to avoid kinetic traps.

Purpose of the Study:

  • To investigate how subunit diversity and kinetic protocols influence self-assembly efficiency.
  • To identify design principles for high-yield, kinetically controlled self-assembly.

Main Methods:

  • Utilized automatic differentiation algorithms from deep learning to search kinetic parameter spaces.
  • Developed mass-action kinetic models for self-assembly processes.
  • Derived theoretical expressions for kinetic trap timescales.

Main Results:

  • Subunit diversity expands the parameter space, improving self-assembly 'expressivity'.
  • Internal design of binding rates or external control (subunit titration) can steer assembly to avoid kinetic traps.
  • External control offers versatility without molecular engineering, while internal design provides robustness.

Conclusions:

  • Optimal kinetic protocols are crucial for efficient, high-yield self-assembly in synthetic systems.
  • Both internal design and external control strategies can overcome kinetic limitations.
  • The methods are applicable for both designing and inferring binding rates in self-assembling systems.