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PconsC3

Faster, more accurate and entirely open source method for predicting contacts in proteins

If you use PconsC3 please cite:

  • Carlo Baldassi, Marco Zamparo, Christoph Feinauer, Andrea Procaccini, Riccardo Zecchina, Martin Weigt and Andrea Pagnani, (2014) PLoS ONE 9(3): e92721. doi:10.1371/journal.pone.0092721
  • Christoph Feinauer, Marcin J. Skwark, Andrea Pagnani, and Erik Aurell. (2014) PLoS Comp Bio: e1003847. doi:10.1371/journal.pcbi.1003847

Prerequisites

  • h5py and cython if you want to use the parallel version with hdf5 support (highly recommended even for non-parallel usage, see Parallel version below.)
  • Julia interpreter (ver. 0.3 and up is supported). Present in most Linux repositories (Ubuntu , otherwise download it from Julia website.
  • Python interpreter (2.7+)
  • CD-HIT. Available in most Linux distributions, otherwise downloadable from GitHub
  • A way to generate multiple sequence alignments (or a FASTA formatted MSA).
  • A way to generate PSIPRED-like secondary structure predictions
  • A way to generate NetSurfP-like solvent accessibility predictions
  • An external source of contact information (e.g. PhyCMAP, CMAPpro...), capable of producing contact estimates in CASP RR format

If Julia, Python and CD-HIT are in your search path, you are set to go. Otherwise, you need to either add them to the path, or modify the necessary scripts.

For Julia: rungdca.py and runplm.py For CD-HIT: alignmentstats.py

Installation

  1. Check out PconsC3 from GitHub 
    git checkout https://github.com/mskwark/PconsC3.git
  2. Install Julia packages. Start Julia and install: 
    julia> Pkg.add("NLopt")

    julia> Pkg.clone("https://github.com/carlobaldassi/GaussDCA.jl")
julia> Pkg.clone("https://github.com/carlobaldassi/ArgParse.jl")

    julia> Pkg.clone("https://github.com/mskwark/PlmDCA")
  3. Make sure the prerequisites are installed.

Running the software

Before the first run, download and unpack the trained Random Forests in the same directory as the PconsC3 code. You should have six subdirectories named tforest0, tforest1,...tforest5. You can get them from Google Drive or a local mirror (378MiB). If you just want to give PconsC3 a try or for some other reason need a smaller archive, feel free to download the mini-version either from Google Drive or local mirror (38MB), being advised that this version may not perform as well as the fully-fledged one (but will be roughly 10x faster!).

> tar -xJf pconsc3-forests.tar.xz

You may want to put them on a fast filesystem (on a relatively recent Linux machine /dev/shm/ is a good choice and by default PconsC3 will look for them there (i.e. it will check if /dev/shm/tforest0 etc. exist and are sane). As a fallback it will look in the same directory ./predict.py is located. If you want to change it, you need to modify forestlocation variable in the head of ./predict.py.

To run PconsC3, you need to have at hand:

  • Your input alignment in FASTA format, retaining only these columns that you want to run the prediction for (most often: all the match states/amino acids in your target sequence). For A3M alignments this can be attained by filtering all the lowercase letters (inserts) from the sequences.
  • Predicted secondary structure in a PSIPRED ss2 format. Download or run PSIPRED here
  • Predicted relative solvent accessibility (RSA) in NetSurfP format. Download or run NetSurfP here
  • A source of external estimates of contact propensities in a CASP RR format to act as a contact prior. We have tested the method with CMAPpro and PhyCMAP, but other methods should work as well.

You can name these files any way you want, but assuming your alignment is named myprotein.fas, your contact priors are named external.RR, secondary structure prediction file is named psipred.ss2 and RSA is named netsurf.rsa, to run the prediction do the following.

  1. Infer evolutionary couplings with GaussDCA:

    ./rungdca.py myprotein.fas

    It will produce a file named myprotein.gdca

  2. Infer evolutionary couplings with plmDCA.jl:

    ./runplm.py myprotein.fas

    It will produce a file named myprotein.0.02.plm20

  3. Compute alignment statistics:

    ./alignmentstats.py myprotein.fas

    It will produce a file named myprotein.stats

  4. Run PconsC3:

    ./predict.py myprotein.gdca myprotein.0.02.plm20 external.RR netsurf.rsa psipred.ss2 myprotein.stats myprotein.fas outputfile

    This will run for a while, but will provide you with estimates of running time. It will result in a number of intermediate files being generated: outputfile.l0, outputfile.l1...outputfile.l5 and an outputfile.RR containing final predictions in RR format (by default only non-local prediction are output).

Parallel version and HDF5 support (recommended, even for non-parallel usage)

The parallel version with HDF5 support drastically reduces IO and computation time, while not changing the output in any way. To set it up make sure h5py and Cython are in your PYTHONPATH. You can install the packages via pip:

pip install h5py
pip install Cython

Then you need to convert the forest data in your PconsC3 root directory into HDF5-files:

cd <PconsC3 root directory>
python convert_to_hdf5.py .

After successful conversion you can safely remove the folders containing the forest data:

find tlayer* ! -name '*.hdf5' -type d -exec rm -r {} +

And finally compile the Cython script:

python setup.py build_ext -i

After that you can run the fast version of PconsC3:

./predict-parallel-hdf5.py myprotein.gdca myprotein.0.02.plm20 external.RR netsurf.rsa psipred.ss2 myprotein.stats myprotein.fas outputfile [NumberThreads]

Making PconsC3 run faster

There are a few parameters in ./predict.py that can be tweaked, notably:

  • maxtime -- the maximum time in seconds spent on a single prediction layer (total prediction time will be at most 6x maxtime + time spent on i/o). For the larger proteins setting maxtime too low may result in sub-par performance
  • treefraction -- the fraction of trees to be used. While we recommend leaving treefraction set to 1., benchmarks have demonstrated satisfactory performance at values as low as 0.3 and for proteins with a lot of sequence information, as low as 0.1.

Help and Support

If you run into any problems with the software or observe it performing poorer than expected, we would appreciate an email to Marcin J. Skwark (firstname@lastname.pl or firstname.middleinitial.lastname@vanderbilt.edu).