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.  and Balakrishnan and Kamisetty . 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 https://github.com/soedinglab/CCMpred.git
With the sourcecode ready, simply run cmake with the default settings and libraries should be auto-detected:
cmake . make
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.
scripts/ subdirectory contains some python scripts you might find useful - please make sure both NumPy and BioPython are installed to use them!
convert_alignment.py- Use BioPython's
Bio.SeqIOto convert a variety of alignment formats (FASTA, etc.) into the CCMpred alignment input format
top_couplings.py- 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.
 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  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