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https://github.com/soedinglab/CCMpred

Protein Residue-Residue Contacts from Correlated Mutations predicted quickly and accurately.
https://github.com/soedinglab/CCMpred

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

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# CCMpred



[![Travis](https://img.shields.io/travis/soedinglab/CCMpred.svg)](https://travis-ci.org/soedinglab/CCMpred)

[![Codeship](https://img.shields.io/codeship/c2512a40-d488-0132-75f3-623d5159f317.svg)](https://codeship.com/projects/77807)



Protein Residue-Residue **C**ontacts from **C**orrelated **M**utations **pred**icted 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.



## Requirements



To compile from source, you will need:



  * a recent C compiler (we suggest GCC 4.4 or later)

  * [CMake](http://cmake.org/) 2.8 or later

  * Optional: [NVIDIA CUDA SDK](https://developer.nvidia.com/cuda-downloads) 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](https://developer.nvidia.com/cuda-gpus) 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



## Installation

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



### Downloading

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



### Compilation

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.



## 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!



  * `convert_alignment.py` - Use BioPython's `Bio.SeqIO` to 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



## License

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



## References



	[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