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. https://github.com/debbiemarkslab/evcouplings.
Direct-coupling analysis of residue coevolution captures native contacts across many protein families
Robust and accurate prediction of residue-residue interactions across protein interfaces using evolutionary information
MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins
PLD2-PI(4,5)P2 interactions in fluid phase membranes: Structural modeling and molecular dynamics simulations
Multiplexed measurement of variant abundance and activity reveals VKOR topology, active site and human variant impact.
Small design from big alignment: engineering proteins with multiple sequence alignment as the starting point
Study on gene expression patterns and functional pathways of peripheral blood monocytes reveals potential molecular mechanism of surgical treatment for periodontitis
αβDCA method identifies unspecific binding but specific disruption of the group I intron by the StpA chaperone.
Characterization of aromatic acid/proton symporters in Pseudomonas putida KT2440 toward efficient microbial conversion of lignin-related aromatics.
Improving integrative 3D modeling into low- to medium-resolution electron microscopy structures with evolutionary couplings.
Evolution and insights into the structure and function of the DedA superfamily containing TMEM41B and VMP1.
DNCON2_Inter: predicting interchain contacts for homodimeric and homomultimeric protein complexes using multiple sequence alignments of monomers and deep learning.
Researcher Network:CZI Neurodegeneration Challenge
The Neurodegeneration Challenge Network aims to provide funding for and to bring together researchers studying neurodegenerative diseases. Find the latest research from the NDCN grantees here.