Evolutionary Coupling Analysis

Evolutionary Coupling Analysis is a computational method in structural bioinformatics that identifies co-evolving amino acid residues in protein families by analyzing patterns of sequence variation across homologs, thereby inferring spatial proximities and functional dependencies for secondary structure prediction.

66

Publications

5.5K

Citations

258

Authors

134

Institutions

Publications per year

2017–2026

37

Variants

Eca

Authors

258

  1. TA
    Thomas A. Hopf

    Center for Systems Biology, United States

    7 publications · 1.9K citations

    7

    h-index

  2. DS
    Debora S. Marks

    Harvard University, United States

    7 publications · 1.9K citations

    7

    h-index

  3. CS
    Chris Sander

    Memorial Sloan Kettering Cancer Center, United States

    7 publications · 1.8K citations

    7

    h-index

  4. LM
    Leon Moonen

    Simula Research Laboratory, Norway

    5 publications · 103 citations

    5

    h-index

  5. TR
    Thomas Rolfsnes

    Simula Research Laboratory, Norway

    5 publications · 103 citations

    5

    h-index

1–5 of 258

Venues

  1. Proteins Structure Function and Bioinformatics

    4 publications · 287 citations

    4

    h-index

  2. Bioinformatics

    4 publications · 521 citations

    4

    h-index

  3. PLoS Computational Biology

    3 publications · 1K citations

    3

    h-index

  4. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 3 publications · 105 citations

    3

    h-index

  5. Proceedings of the National Academy of Sciences

    PNAS · 3 publications · 143 citations

    3

    h-index

1–5