The EVcouplings Python framework for coevolutionary sequence analysis

作者：Thomas A. Hopf, Anna G. Green, Benjamin Schubert, Sophia Mersmann, Charlotta Schärfe, John Ingraham, Ágnes Tóth-Petróczy, Kelly P. Brock, Adam J. Riesselman, Perry Palmedo, Chan Kang, Robert P. Sheridan, Eli J. Draizen, Christian Dallago, Chris Sander, Debora S. Marks · 发表于：Bioinformatics · 年份：2018 · DOI：10.1093/bioinformatics/bty862 · 被引用次数：338 · 研究领域：RNA and protein synthesis mechanisms、Protein Structure and Dynamics、Genomics and Phylogenetic Studies

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. 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 combination of an easy to use, flexible command line interface and an underlying modular Python package makes the full power of coevolutionary analyses available to entry-level and advanced users. AVAILABILITY AND IMPLEMENTATION: https://github.com/debbiemarkslab/evcouplings.