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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
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Learning dynamical information from static protein and sequencing data.

Philip Pearce1, Francis G Woodhouse2, Aden Forrow1,2

  • 1Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139-4307, USA.

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|November 28, 2019
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Summary
This summary is machine-generated.

Researchers developed a new computational framework to infer state transition dynamics from static data. This method accurately reconstructs complex biological networks, including protein folding and HIV evolution, from equilibrium distributions.

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Area of Science:

  • Computational Biology
  • Biophysics
  • Systems Biology

Background:

  • Complex biological processes, such as protein folding and neuronal dynamics, involve navigating high-dimensional energy landscapes.
  • While cluster detection in high-dimensional spaces is established, inferring state transition dynamics from such data remains challenging.

Purpose of the Study:

  • To introduce a robust numerical framework for inferring Markovian transition networks from time-independent data.
  • To enable the analysis of dynamic processes using stationary equilibrium distributions.

Main Methods:

  • Developed a flexible and robust numerical framework for network inference.
  • Applied the scheme to time-independent data sampled from stationary equilibrium distributions.
  • Validated the inferred network dynamics against established methods.

Main Results:

  • Successfully reconstructed network dynamics for protein-folding transitions, gene-regulatory network motifs, and HIV evolution pathways.
  • Inferred network topologies and transition timescales showed strong agreement with direct estimates from molecular dynamics, stochastic simulations, and phylogenetic trees.
  • Demonstrated the practical potential and accuracy of the inference scheme.

Conclusions:

  • The introduced framework provides a reliable method for inferring state transition dynamics from time-independent data.
  • The generic structure of the framework allows for broad applicability to various biological systems and datasets, including high-throughput sequencing and cryo-electron microscopy (cryo-EM) data.