The EVcouplings Python framework for coevolutionary sequence analysis

Abstract

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.

Fig. 1.

The EVcouplings Python framework. (a) The protein monomer EVcouplings pipeline entails multiple sequence alignment generation (align stage), EC inference (couplings stage), de novo folding (fold stage), mutation effect prediction (mutate stage) and comparison to experimental structure (compare stage). (b) The protein complex pipeline extends the monomer pipeline to protein interactions by pairing putatively interacting homologs (concatenate stage) and providing restraints for molecular docking (dock stage)