Fast and easy contact prediction.
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README.md

PconsC4:

Fast, accurate, and hassle-free contact prediction.

Installation instructions:

Download the tarball from the releases tab

pip3 install numpy Cython pythran &&
pip3 install pconsc4-0.2.tar.gz

NB: the trained model is a bit over Github's limit, so they cannot be checked in the repo, hence the need for the tarball.

You will also need a deep learning backend compatible with Keras. We recommend Tensorflow:

pip3 install -U tensorflow

Usage instructions

Inside Python:

import pconsc4

model = pconsc4.get_pconsc4()

pred_1 = pconsc4.predict(model, 'path/to/alignment1')
pred_2 = pconsc4.predict(model, 'path/to/alignment2')

# Show pred_1 on the screen:

import matplotlib.pyplot as plt 
plt.imshow(pred_1['cmap'])
plt.show()

The program accepts alignments in .fasta, .a3m, or .aln, without line wrapping.

We also provide a function to format the output in CASP format:

# Save in CASP format:
from pconsc4.utils import format_contacts_casp
print(format_contacts_casp(pred_2['cmap'], seq_2, min_sep=5))

Troubleshooting:

If pyGaussDCA fails to install with template errors, upgrade your compiler. GCC 5 and higher is known to work.