Estimates backbone quality based on C beta (or C alpha, optionally) distance maps.
usage: BackboneEstimator.py [-h] [--pdb] [--ca] [--verbose] [--gpu GPU] infolder ...
Backbone Quality Estimator
positional arguments:
infolder input folder name full of pdbs or path to a single pdb
outfolder output folder name. If a pdb path is passed this needs to be a .npz file. Can also be empty. Default
is current folder or pdbname.npz
optional arguments:
-h, --help show this help message and exit
--pdb, -pdb Running on a single pdb file instead of a folder (Default: False)
--ca, -ca Predicting based on a calpha distance map (Default: False)
--verbose, -v verbose flag (Default: False)
--gpu GPU, -g GPU gpu device to use (default gpu0)
v0.0.1
Type the following commands to activate tensorflow environment with pyrosetta3.
source activate tensorflow
source /software/pyrosetta3/setup.sh
Running on a folder of pdbs with Cbeta coordinates (foldername: samples)
python BackboneEstimator.py -v samples out
Running on a folder of pdbs with Calpha coordinates (foldername: samples)
python BackboneEstimator.py -v -ca samples out
Running on a single pdb file (inputname: input.pdb). Output name is optional and defaults to input.npz
python BackboneEstimator.py -v --pdb input.pdb [output.npz]
Output of the network is written to [input_file_name].npz.
You can extract the predictions as follows.
import numpy as np
x = np.load("testoutput.npz")
lddt = x["lddt"] # per residue lddt
estogram = x["estogram"] # per pairwise distance e-stogram
mask = x["mask"] # mask predicting native < 15
Perhaps lddt is the easiest place to start as it is per-residue quality score. You can simply take an average if you want a global score per protein structure.
If you want to do something more involved, especially for protein complex design, see example.ipynb for getting more specialized metrics. If you want to play with pair-wise error predictions, samples.ipynb is a good place to start.
- Python3.5>
- Pyrosetta
- Tensorflow 1.14 (not Tensorflow 2.0)