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pyconsFold: a fast and easy tool for modeling and docking using distance predictions.

J Lamb1,2, A Elofsson1,2

  • 1Science for Life Laboratory, Stockholm University, Solna SE-171 21, Sweden.

Bioinformatics (Oxford, England)
|July 9, 2021
PubMed
Summary

pyconsFold utilizes predicted protein contact distances for faster and accurate protein structure prediction. This novel method also enables simultaneous folding and docking of protein dimers.

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Bioinformatics

Background:

  • Protein structure prediction is crucial for understanding biological function.
  • Contact predictions, especially using distance distributions, enhance accuracy over binary annotations.
  • Interprotein distance predictions show promise for protein-dimer docking.

Purpose of the Study:

  • Introduce pyconsFold, a novel computational tool for protein structure prediction and docking.
  • Evaluate pyconsFold's performance against existing methods.
  • Highlight pyconsFold's speed and versatility.

Main Methods:

  • Employs CNS (Crystal Napieralski-Snyder) as the underlying folding mechanism.
  • Integrates predicted contact distances for protein folding.
  • Utilizes predicted interprotein contacts/distances for fold-and-dock protocol.

Main Results:

  • pyconsFold outperforms standard contact prediction-based modeling for protein structure.
  • Achieves comparable results to state-of-the-art pyRosetta but is approximately 20 times faster.
  • Demonstrates capability as a fold-and-dock protocol for protein dimers.

Conclusions:

  • pyconsFold offers a fast and effective approach for protein structure prediction and docking.
  • Its implementation in Python 3 with minimal dependencies ensures longevity and accessibility.
  • The tool is publicly available on GitHub under the GPLv3 license.