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Protein Residue-Residue Contacts from Correlated Mutations predicted quickly and accurately.

CCMpred is a C implementation of a Markov Random Field pseudo-likelihood maximization for learning protein residue-residue contacts as made popular by Ekeberg et al. [1] and Balakrishnan and Kamisetty [2]. While predicting contacts with comparable accuracy to the referenced methods, however, CCMpred is written in C / CUDA C, performance-tuned and therefore much faster.


To compile from source, you will need:

  • a recent C compiler (we suggest GCC 4.4 or later)
  • CMake 2.8 or later
  • Optional: NVIDIA CUDA SDK 5.0 or later (if you want to compile for the GPU)

To run CUDA-accelerated computations, you will need an NVIDIA GPU with a Compute Capability of 2.0 or later and the proprietary NVIDIA drivers installed. See the NVIDIA CUDA GPU Overview for details on your graphics card.

Memory Requirement on the GPU

When doing computations on the GPU, the available memory limits the size of the model you will be able to compute. We recommend at least 2 GB of GPU RAM so you can calculate contacts for big multiple sequence alignments (e.g for N=5000):

GPU RAM		Lmax	Lmax(pad)
1 GB		353	291
2 GB		512	420
3 GB		635	519
5 GB		829	676
6 GB		911	743
8 GB		1057	861
12 GB		1302	1059

You can calculate the memory requirements in bytes for L columns and N rows using the following formula:

4*(4*(L*L*21*21 + L*20) + 23*N*L + N + L*L) + 2*N*L + 1024

For the padded version:

4*(4*(L*L*32*21 + L*20) + 23*N*L + N + L*L) + 2*N*L + 1024


We recommend compiling CCMpred on the machine that should run the computations so that it can be optimized for the appropriate CPU/GPU architecture.


If you want to compile the most recent version, use the follwing to clone both CCMpred and its submodules:

git clone --recursive


With the sourcecode ready, simply run cmake with the default settings and libraries should be auto-detected:

cmake .

You should find the compiled version of CCMpred at bin/ccmpred. To check if the CUDA libraries were detected, you can run ldd bin/ccmpred to see if CUDA was linked with the program, or simply run a prediction and check the program's output.

Useful scripts

The scripts/ subdirectory contains some python scripts you might find useful - please make sure both NumPy and BioPython are installed to use them!

  • - Use BioPython's Bio.SeqIO to convert a variety of alignment formats (FASTA, etc.) into the CCMpred alignment input format
  • - Extract the top couplings from an output contact maps in a list representation


CCMpred is released under the GNU Affero General Public License v3 or later. See LICENSE for more details.


[1] Ekeberg, M., Lövkvist, C., Lan, Y., Weigt, M., & Aurell, E. (2013).
    Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models.
    Physical Review E, 87(1), 012707. doi:10.1103/PhysRevE.87.012707

[2] Balakrishnan, S., Kamisetty, H., Carbonell, J. G., Lee, S.-I., & Langmead, C. J. (2011).
    Learning generative models for protein fold families.
    Proteins, 79(4), 1061–78. doi:10.1002/prot.22934


Protein Residue-Residue Contacts from Correlated Mutations predicted quickly and accurately.







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