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Using Markov state models to study self-assembly.

Matthew R Perkett1, Michael F Hagan1

  • 1Martin Fisher School of Physics, Brandeis University, Waltham, Massachusetts 02474, USA.

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|June 9, 2014
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Summary
This summary is machine-generated.

This study introduces a novel method for constructing Markov state models (MSMs) to simulate multi-molecular assembly, such as virus self-assembly. The approach accurately predicts dynamics and significantly reduces computational time.

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Area of Science:

  • Computational biology
  • Biophysics
  • Statistical mechanics

Background:

  • Markov state models (MSMs) are effective for studying intramolecular processes like protein folding.
  • Modeling multi-molecular assembly presents unique challenges due to complex interactions and spatial variations.

Purpose of the Study:

  • To develop a new approach for constructing MSMs applicable to multi-molecular assembly reactions.
  • To validate the method using coarse-grained models of virus self-assembly.

Main Methods:

  • Utilizing undirected graphs based on subunit interactions to define distinct assembly states.
  • Incorporating a Gaussian-based signature to account for spatial inhomogeneities of free subunits.
  • Investigating simplifications for state identification.

Main Results:

  • The new MSM approach successfully models virus self-assembly dynamics.
  • Predicted dynamics show good agreement with long, unbiased simulations.
  • MSMs reduce overall simulation time by orders of magnitude.

Conclusions:

  • The developed MSM construction method is effective for multi-molecular assembly.
  • This approach offers significant computational efficiency for studying complex biological systems.