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The EVcouplings Python framework for coevolutionary sequence analysis.

Thomas A Hopf1,2, Anna G Green1, Benjamin Schubert1,2,3

  • 1Department of Systems Biology, Harvard Medical School, Boston, MA, USA.

Bioinformatics (Oxford, England)
|October 11, 2018
PubMed
Summary
This summary is machine-generated.

EVcouplings is an open-source framework for coevolutionary sequence analysis. It aids in predicting protein and RNA structures, functions, and mutation effects using evolutionary couplings (ECs).

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Bioinformatics

Background:

  • Coevolutionary sequence analysis is vital for predicting molecular structures and functions.
  • Existing methods require significant expertise and integration efforts.

Purpose of the Study:

  • To introduce EVcouplings, an integrated open-source framework for coevolutionary analysis.
  • To provide a user-friendly tool for de novo prediction of protein and RNA structures and functions.

Main Methods:

  • Development of a comprehensive application and Python package.
  • Integration of sequence alignment generation, evolutionary couplings (ECs) calculation, and structure prediction.
  • Implementation of a flexible command-line interface and modular Python package.

Main Results:

  • EVcouplings enables de novo prediction of protein, RNA, and complex structures.
  • The framework facilitates the calculation and evaluation of evolutionary couplings (ECs).
  • It allows for the prediction of mutation effects on molecular structures and functions.

Conclusions:

  • EVcouplings democratizes coevolutionary analysis for both novice and expert users.
  • The framework streamlines the prediction of molecular structures and functions.
  • It offers a powerful, integrated solution for bioinformatics research.