A simplified implementation of DSSP algorithm for PyTorch and NumPy
Project description
PyDSSP
A simplified implementation of DSSP algorithm for PyTorch and NumPy
What's this?
DSSP (Dictionary of Secondary Structure of Protein) is a popular algorithm for assigning secondary structure of protein backbone structure. [ Wolfgang Kabsch, and Christian Sander (1983)] This repository is a python implementation of DSSP algorithm that simplifies some parts of the algorithm.
General Info
- It's NOT a complete implementation of the original DSSP, as some parts have been simplified (some more details here). However, an average of over 97% of secondary structure determinations agree with the original.
- The algorithm used to identify hydrogen bonded residue pairs is exactly the same as the original DSSP algorithm, but is extended to output the hydrogen-bond-pair-matrix as continuous values in the range [0,1].
- With the continuous variable extension above, the hydrogen-bond-pair-matrix is differentiable with torch.Tensor as input.
Install
install through PyPi
pip install pydssp
install by git clone
git clone https://github.com/ShintaroMinami/PyDSSP.git
cd PyDSSSP
python setup.py install
How to use
To use pydssp script
If you have already installed pydssp, you should be able to use pydssp command.
pydssp input_01.pdb input_02.pdb ... input_N.pdb -o output.result
The output.result will be a text format, looking like follows,
-EEEEE-E--EEEEEE---EEEE-HHHH--EEEE--------- input_01.pdb
-HHHHHHHHHHHHHH----HHHHHHHHHHHHHHHHHHH--- input_02.pdb
-EEEE-----EEEE----EEEE--E---EEE-----EEE-EEE-- input_03.pdb
...
To use as python module
Import & test coordinates
# Import
import torch
import pydssp
# Sample coordinates
batch, length, atoms, xyz = 10, 100, 4, 3
## atoms should be 4 (N, CA, C, O) or 5 (N, CA, C, O, H)
coord = torch.randn([batch, length, atom, xyz]) # batch-dim is optional
To get hydrogen-bond matrix: pydssp.get_hbond_map()
hbond_matrix = pydssp.get_hbond_map(coord)
print(hbond_matrix.shape) # should be (batch, length, length)
- For hbond_matrix[b, i, j], index 'i' is for donner (N-H) and 'j' is for acceptor (C=O), respectively
- The output matrix consists of constant values in the range [0,1], which is defined as follows.
$HbondMat(i,j) = (1+\sin((-0.5-E(i,j)-margin)/margin*\pi/2))/2$
Here $E$ is the electrostatic energy defined by (Kabsch and Sander 1983) and $margin(=1.0)$ is introduced to control smoothness.
To get secondary structure assignment: pydssp.assign()
dssp = pydssp.assign(coord, out_type='c3')
## output is batched np.ndarray of C3 annotation, like ['-', 'H', 'H', ..., 'E', '-']
# To get secondary str. as index
dssp = pydssp.assign(coord, out_type='index')
## 0: loop, 1: alpha-helix, 2: beta-strand
# To get secondary str. as onehot representation
dssp = pydssp.assign(coord, out_type='onehot')
## dim-0: loop, dim-1: alpha-helix, dim-2: beta-strand
Differences from the original DSSP
This implementation was simplified from the original DSSP algorithm. The differences from the original DSSP are as follows
- The implementation omitted β-bulge annotation, so β-bulge is determined as a loop instead of β-strand.
- Parameters for adding hydrogen atoms are slightly different from the original DSSP, which may cause small differences in hydrogen bond annotation.
- Only support C3 ('-', 'H', and 'E') type assignment instead of C8 type (B, E, G, H, I, S, T, and ' ').
Although the above simplifications, the C3 type annotation still matches with the original DSSP for more than 97% of residues on average.
Reference
@article{kabsch1983dictionary,
title={Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features},
author={Kabsch, Wolfgang and Sander, Christian},
journal={Biopolymers: Original Research on Biomolecules},
volume={22},
number={12},
pages={2577--2637},
year={1983},
publisher={Wiley Online Library}
}
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