The EVcouplings Python framework for coevolutionary sequence analysis

By Thomas A. Hopf, Anna G. Green, Benjamin Schubert, Sophia Mersmann, Charlotta P I Schärfe, John Ingraham, Agnes Toth-Petroczy, Kelly P. Brock, Adam J. Riesselman, Perry Palmedo and 6 more. 2018

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Summary Coevolutionary sequence analysis has become a commonly used technique for de novo prediction of the structure and function of proteins, RNA, and protein complexes. This approach requires extensive computational pipelines that integrate multiple tools, databases, and data processing steps. We present the EVcouplings framework, a fully integrated open-source application and Python package for coevolutionary analysis. The framework enables generation of sequence alignments, calculation and evaluation of evolutionary couplings (ECs), and de novo prediction of structure and mutation effects. The application has an easy to use command line interface to run workflows with user control over all analysis parameters, while the underlying modular Python package allows interactive data analysis and rapid development of new workflows. Through this multi-layered approach, the EVcouplings framework makes the full power of coevolutionary analyses available to entry-level and advanced users. Availability https://github.com/debbiemarkslab/evcouplings Contact sander.research@gmail.com, debbie@hms.harvard.edu

Subfields: Molecular Biology

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In Molecular Biology

Citations in the corpus

PDBe-KB: collaboratively defining the biological context of structural data (2021, methodology): “Macias EM validation annotations from 3DBionotes ESP EVcouplings (30) (new) D.”