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FCC Clustering

Structure prediction methods generate a large number of models of which only a fraction matches the biologically relevant structure. To identify this (near-)native model, we often employ clustering algorithms, based on the assumption that, in the energy landscape of every biomolecule, its native state lies in a wide basin neighboring other structurally similar states. We developed a novel clustering strategy that is based on a very efficient similarity measure - the fraction of common contacts.

You can read more and download the necessary scripts to perform FCC clustering here.

Advantages of FCC clustering vs. RMSD-based clustering:

  • 100-times faster on average.
  • Handles symmetry by consider complexes as entities instead of collections of chains.
  • Does not require atom equivalence (clusters mutants, missing loops, etc).
  • Handles any molecule type (protein, DNA, RNA, carbohydrates, lipids, ligands, etc).
  • Allows multiple levels of "resolution": chain-chain contacts, residue-residue contacts, residue-atom contacts, etc.

Requirements & Usage

  • Python2.6 or greater
  • C/C++ compiler (for the contact scripts)
  1. Create a contact list for each protein using make_contacts.py

    ./make_contacts.py a.pdb b.pdb ...

    You can also provide a text file containing one structure per line:

    ./make_contacts.py -f pdb_list.txt

  2. Generate the similarity matrix using calc_fcc_matrix.py. For symmetrical complexes, use the -i option.

    ./calc_fcc_matrix.py [-i] a.contacts b.contacts -o fcc_matrix.out

    You can also provide a text file containing one contact file per line:

    ./calc_fcc_matrix.py -f contact_list.txt -o fcc_matrix.out

  3. Calculate the clusters with cluster_fcc.py.

    ./cluster_fcc.py fcc_matrix.out 0.75 -o clusters.txt

Help on the usage and options of any of the above-mentioned scripts can be obtained by running them with the -h option.

You can email suggestions to Alexandre M.J.J. Bonvin.


Rodrigues JPGLM et al. (2012) Clustering biomolecular complexes by residue contacts similarity. Proteins 80:1810–1817

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And the following article describing the WeNMR portals should be cited:
Wassenaar et al. (2012). WeNMR: Structural Biology on the Grid.J. Grid. Comp., 10:743-767.


The WeNMR Virtual Research Community has been the first to be officially recognized by the EGI.

European Union

WeNMR is an e-Infrastructure project funded under the 7th framework of the EU. Contract no. 261572

WestLife, the follow up project of WeNMR is a Virtual Research Environment e-Infrastructure project funded under Horizon 2020. Contract no. 675858