DeepCoil

Fast and accurate prediction of coiled coil domains in protein sequences

New in version 2.0

Retrained with the updated dataset based on SamCC-Turbo labels.
Faster inference time by applying SeqVec embeddings instead of psiblast profiles.
Heptad register prediction (a and d core positions).
No maximum sequence length limit.
Convenient interface for using DeepCoil within python scripts.
Automated peak detection for improved output readability.
Simplified installation with pip.

Older DeepCoil versions are available here.

Requirements and installation

DeepCoil requires python>=3.7,<3.9 and pip>=19.0.

The most convenient way to install DeepCoil is to use pip:

$ pip3 install deepcoil

Usage

Running DeepCoil standalone version:

deepcoil [-h] -i FILE [-out_path DIR] [-n_cpu NCPU] [--gpu] [--plot]
                [--dpi DPI]

Argument	Description
`-i`	Input file in FASTA format. Can contain multiple entries.
`-out_path`	Directory where the predictions are saved. For each entry in the input file one file will be saved. Defaults to the current directory if not specified.
`-n_cpu`	Number of CPUs to use in the prediction. By the default all cores will be used.
`--gpu`	Flag for turning on the GPU usage. Allows faster inference on large datasets. Overrides `-n_cpu` option.
`--plot`	Turns on the additional visual output of the predictions for each entry in the input. Plot files are saved in the `-out_path` directory.
`--dpi`	DPI of the saved plots, active only with `--plot` option.

In a rare case of deepcoil being not available in your PATH after installation please look in the $HOME/.local/bin/ or other system specific pip directory.

Description of columns in output file:

aa - amino acid in the input protein sequence
cc - sharpened coiled coil propensity
raw_cc - raw coiled coil propensity
prob_a - probability of a core position
prob_d - probability of d core position

Running DeepCoil within script:

from deepcoil import DeepCoil
from deepcoil.utils import plot_preds
from Bio import SeqIO

dc = DeepCoil(use_gpu=True)

inp = {str(entry.id): str(entry.seq) for entry in SeqIO.parse('example/example.fas', 'fasta')}

results = dc.predict(inp)

plot_preds(results['3WPA_1'], out_file='example/example.png')

results[entry] for an entry of sequence length N contains two keys:

['cc'] - per residue coiled coil propensity ([N, 1] shape)
['hept'] - per residue core positions ([N, 3] shape, order in the second axis is: no/other position, a position, d position)

Peak detection can be performed with the deepcoil.utils.sharpen_preds helper function.

labstructbioinf/DeepCoil