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GREMLIN is a method to learn a statistical model of a protein family that captures both conservation and co-evolution patterns in the family. The strength of measured co-evolution is strongly predictive of residue-residue contacts in the 3D structure of the protein.
On this website, you can:
  • Submit contact prediction jobs for both monomers and complexes.
  • Upload PDB models to see agreement to co-evolution data.
  • Generate restraints to be used in Rosetta for modeling.
  • Generate sequence restraints for mutagenesis and design experiments.
  • View data and precomputed databases from our publications.
This website is the result of members and computing power of Prof. David Baker's Lab. We would also like to thank the developers of HHsuite, HMMER and folks that maintain the Pfam, PDB and UniProt databases.
  • Robust and accurate prediction of residue-residue interactions across protein interfaces using evolutionary information.
    Sergey Ovchinnikov, Hetunandan Kamisetty, and David Baker.
    Elife (2014).
  • Assessing the utility of coevolution-based residue–residue contact predictions in a sequence-and structure-rich era.
    Hetunandan Kamisetty, Sergey Ovchinnikov, and David Baker.
    Proceedings of the National Academy of Sciences 110, no. 39 (2013): 15674-15679.
  • Learning generative models for protein fold families.
    Sivaraman Balakrishnan, Hetunandan Kamisetty, Jaime G. Carbonell, Su-In Lee, and Christopher James Langmead.
    Proteins: Structure, Function, and Bioinformatics 79, no. 4 (2011): 1061-1078.