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The EVcouplings Python framework for coevolutionary sequence analysis

Journal Article · · Bioinformatics
 [1];  [2];  [3];  [2];  [4];  [2];  [2];  [2];  [2];  [5];  [2];  [6];  [7];  [8];  [3];  [2]
  1. Harvard Medical School, Boston, MA (United States); DOE/OSTI
  2. Harvard Medical School, Boston, MA (United States)
  3. Harvard Medical School, Boston, MA (United States); Dana-Farber Cancer Institute, Boston, MA (United States)
  4. Harvard Medical School, Boston, MA (United States); Univ. of Tubingen (Germany)
  5. Harvard Medical School, Boston, MA (United States); Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  6. Memorial Sloan Kettering Cancer Center, New York, NY (United States)
  7. Univ. of Virginia, Charlottesville, VA (United States)
  8. Harvard Medical School, Boston, MA (United States); Technical Univ. of Munich (Germany)
+ Show Author Affiliations
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.
Research Organization:
Krell Institute, Ames, IA (United States)
Sponsoring Organization:
National Science Foundation (NSF); Pathway Commons; USDOE; USDOE Office of Science (SC)
Grant/Contract Number:
FG02-97ER25308
OSTI ID:
1610349
Alternate ID(s):
OSTI ID: 1483009
Journal Information:
Bioinformatics, Journal Name: Bioinformatics Journal Issue: 9 Vol. 35; ISSN 1367-4803
Publisher:
International Society for Computational Biology - Oxford University PressCopyright Statement
Country of Publication:
United States
Language:
English

References (21)

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CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations journal July 2014
MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins journal November 2014
ConKit: a python interface to contact predictions journal March 2017
Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 journal October 2015
Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models journal January 2013
Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns journal November 2014
Identification of direct residue contacts in protein-protein interaction by message passing text January 2009
Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information journal May 2014
Large-scale determination of previously unsolved protein structures using evolutionary information journal September 2015
Learning generative models for protein fold families
  • Balakrishnan, Sivaraman; Kamisetty, Hetunandan; Carbonell, Jaime G.
  • Proteins: Structure, Function, and Bioinformatics, Vol. 79, Issue 4 https://doi.org/10.1002/prot.22934
journal January 2011
3D RNA and Functional Interactions from Evolutionary Couplings journal May 2016
Mutation effects predicted from sequence co-variation journal January 2017
Identification of direct residue contacts in protein-protein interaction by message passing journal December 2008
Direct-coupling analysis of residue coevolution captures native contacts across many protein families journal November 2011
Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models journal January 2013
FreeContact: fast and free software for protein contact prediction from residue co-evolution journal January 2014
Protein 3D Structure Computed from Evolutionary Sequence Variation journal December 2011
Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information journal May 2014
Sequence co-evolution gives 3D contacts and structures of protein complexes journal September 2014
Large-scale determination of previously unsolved protein structures using evolutionary information journal September 2015

Cited By (13)

Information-Driven Modeling of Biomolecular Complexes preprint January 2021
Additional file 10 of The evolution of the huntingtin-associated protein 40 (HAP40) in conjunction with huntingtin dataset January 2020
Additional file 11 of The evolution of the huntingtin-associated protein 40 (HAP40) in conjunction with huntingtin dataset January 2020
Additional file 12 of The evolution of the huntingtin-associated protein 40 (HAP40) in conjunction with huntingtin dataset January 2020
Additional file 1 of The evolution of the huntingtin-associated protein 40 (HAP40) in conjunction with huntingtin dataset January 2020
Additional file 2 of The evolution of the huntingtin-associated protein 40 (HAP40) in conjunction with huntingtin dataset January 2020
Additional file 3 of The evolution of the huntingtin-associated protein 40 (HAP40) in conjunction with huntingtin dataset January 2020
Additional file 4 of The evolution of the huntingtin-associated protein 40 (HAP40) in conjunction with huntingtin dataset January 2020
Additional file 5 of The evolution of the huntingtin-associated protein 40 (HAP40) in conjunction with huntingtin dataset January 2020
Additional file 6 of The evolution of the huntingtin-associated protein 40 (HAP40) in conjunction with huntingtin dataset January 2020
Additional file 7 of The evolution of the huntingtin-associated protein 40 (HAP40) in conjunction with huntingtin dataset January 2020
Additional file 8 of The evolution of the huntingtin-associated protein 40 (HAP40) in conjunction with huntingtin dataset January 2020
Additional file 9 of The evolution of the huntingtin-associated protein 40 (HAP40) in conjunction with huntingtin dataset January 2020

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