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Protein Complex Assembly02:41

Protein Complex Assembly

Proteins can form homomeric complexes with another unit of the same protein or heteromeric complexes with different types.  Most protein complexes self-assemble spontaneously via ordered pathways, while some proteins need assembly factors that guide their proper assembly. Despite the crowded intracellular environment, proteins usually interact with their correct partners and form functional complexes.
Many viruses self-assemble into a fully functional unit using the infected host cell to...

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Liquid-cell Transmission Electron Microscopy for Tracking Self-assembly of Nanoparticles
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Nanosystem self-assembly pathways discovered via all-atom multiscale analysis.

Stephen D Pankavich1, Peter J Ortoleva

  • 1Department of Mathematics, United States Naval Academy, Annapolis, Maryland 21402, United States. pankavic@usna.edu

The Journal of Physical Chemistry. B
|March 1, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a multiscale framework to predict self-assembly rates for nanocomposite structures. The method accurately models various assembly pathways, crucial for designing advanced nanomaterials and therapeutic delivery systems.

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Area of Science:

  • Nanoscience and Materials Science
  • Statistical Mechanics
  • Computational Chemistry

Background:

  • Predicting self-assembly pathways for complex nanocomposites is challenging due to numerous potential intermediates.
  • Existing phenomenological approaches require frequent recalibration for new applications.
  • Understanding the feedback across scales is fundamental to nanosystem self-assembly.

Purpose of the Study:

  • To develop a rigorous multiscale theoretical framework for predicting self-assembly rates and pathways of nanocomposite structures.
  • To derive stochastic equations for aggregate population dynamics from a reduced statistical framework.
  • To provide a predictive model applicable to diverse nanosystems without recalibration.

Main Methods:

  • Multiscale analysis of the classical Liouville equation.
  • Derivation of reduced statistical framework and stochastic equations for population levels.
  • Integration of multicomponent association with atomic-level configurations.

Main Results:

  • Predictable rates for the assembly of each possible intermediate in nanocomposite formation.
  • A self-consistency criterion for defining assembly types, balancing precision and population dynamics.
  • Demonstration of accounting for feedback across spatial and temporal scales.

Conclusions:

  • The developed theory provides accurate predictions for nanocomposite self-assembly rates and pathways.
  • The method offers a universal approach applicable to bionanostructures, engineered composites, and nanomedicine.
  • This framework advances the design and understanding of self-assembling nanosystems.