A computational server for potential inhibitor and scaffold prediction of nuclear receptors
Project description
PisNRs
PisNRs is a Python module for potential inhibitor and scaffold prediction of nuclear receptors built on top of RDKit, scikit-learn and distributed under the MIT license.
Installation
Dependencies
pisnrs requires:
- Python (>= 2.6)
- rdkit (>= 2018.03.2.0)
- scikit-learn (>= 0.19.1)
- pandas (>= 0.23.1)
You can configure the environment of the pisnrs through Ancaconda.
1. Introduction to anaconda
Conda is an open-source, cross-platform, software package manager. It supports the packaging and distribution of software components, and manages their installation inside isolated execution environments. It has several analogies with pip and virtualenv, but it is designed to be more "python-agnostic" and more suitable for the distribution of binary packages and their dependencies.
2. How to get conda
The easiest way to get Conda is having it installed as part of the Anaconda Python distribution. A possible (but a bit more complex to use) alternative is provided with the smaller and more self-contained Miniconda. The conda source code repository is available on github and additional documentation is provided by the project website.
3. How to install RDKit with Conda
Creating a new conda environment with the RDKit installed requires one single command similar to the following:
$ conda create -c rdkit rdkit
4. Install scikit-learn
Install scikit-learn using pip :
pip install -U scikit-learn
or conda :
conda install scikit-learn
5. Install pisnrs
If you already have a working installation of rdkit and scikit-learn, the easiest way to install pisnrs is using pip :
pip install pisnrs
Example
1. import pisnrs and load model
import pisnrs
model = pisnrs.pisnrs()
print(model.getNRs()) #print NR proteins in model
print(model.getLigandDescriptors()) # print Ligand descriptors in model
2. predict the activity and scaffold of one ligand
### moltype includes : smiles, mol, block, sdf
### You can find the example .mol and .sdf file in the 'example/' folder
### Example: https://github.com/ddhmed/pisnrs/tree/master/example
# 1. smiles input
smiles = 'CC1OC(C2=CC=CC=C2)=NC=1CN(CC1=CC(=C(C(=C1)C)OC(C(O)=O)(C)C)C)CC1OC=CC=1'
protein = 'NR1C1'
des = model.calPCMDecriptorFromMolText(smiles, protein, moltype='smiles') # create descriptors
print(model.preProba(des)) # predict
# 2. .mol file input
molfile = 'example/example.mol'
protein = 'NR1C1'
des = model.calPCMDecriptorFromMolText(molfile, protein, moltype='mol')
print(model.preProba(des))
# 3. mol block input
block = '''
RDKit 2D
36 39 0 0 0 0 0 0 0 0999 V2000
4.9515 -5.4554 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
4.4879 -6.8820 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
5.3696 -8.0956 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
4.4879 -9.3091 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
4.9515 -10.7357 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
6.4187 -11.0475 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
6.8822 -12.4741 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
5.8785 -13.5888 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
4.4113 -13.2770 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
3.9478 -11.8504 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
3.0613 -8.8456 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0
3.0613 -7.3456 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
1.8478 -6.4639 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
0.4775 -7.0740 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0
0.3207 -8.5658 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-1.0496 -9.1759 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-2.2631 -8.2942 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-3.6335 -8.9043 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-3.7902 -10.3961 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-2.5767 -11.2778 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-1.2064 -10.6676 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-2.7335 -12.7695 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-5.1606 -11.0062 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
-5.3174 -12.4980 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-5.4741 -13.9897 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-6.8445 -14.5999 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
-4.2606 -14.8714 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
-6.8091 -12.3412 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-3.8256 -12.6548 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-4.8470 -8.0226 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-0.7360 -6.1923 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-0.5792 -4.7005 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-1.6939 -3.6968 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
-1.0838 -2.3265 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
0.4079 -2.4833 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
0.7198 -3.9505 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
1 2 1 0
2 3 1 0
3 4 1 0
4 5 1 0
5 6 2 0
6 7 1 0
7 8 2 0
8 9 1 0
9 10 2 0
4 11 2 0
11 12 1 0
12 13 1 0
13 14 1 0
14 15 1 0
15 16 1 0
16 17 2 0
17 18 1 0
18 19 2 0
19 20 1 0
20 21 2 0
20 22 1 0
19 23 1 0
23 24 1 0
24 25 1 0
25 26 1 0
25 27 2 0
24 28 1 0
24 29 1 0
18 30 1 0
14 31 1 0
31 32 1 0
32 33 1 0
33 34 1 0
34 35 2 0
35 36 1 0
12 2 2 0
21 16 1 0
36 32 2 0
10 5 1 0
M END
'''
des = model.calPCMDecriptorFromMolText(block, protein, moltype='block')
print(model.preProba(des))
# 4. .sdf file input
sdffile = 'example/example.sdf'
protein = 'NR1C1'
des = model.calPCMDecriptorFromMolText(sdffile, protein, moltype='sdf')
print(model.preProba(des))
3. Batch mode
smiles_list = ['CCCC', 'CCC']
protein_list = ['NR1C1', 'NR1C2']
print(model.preBatch(smiles_list, protein_list=protein_list)） # predict activity of every ligands and proteins in list
print(model.preBatch(smiles_list)） # predict activity of every ligands in list and all proteins in model
4. Create molecule images
smile = 'CC1OC(C2=CC=CC=C2)=NC=1CN(CC1=CC(=C(C(=C1)C)OC(C(O)=O)(C)C)C)CC1OC=CC=1'
model.image_from_smile(smile, name='example.png', dir='example/') # output image of smiles to 'examole/' folder
Important links
- Official source code repo: https://github.com/ddhmed/pisnrs
- Download releases: https://pypi.org/project/pisnrs/
- web server : http://pisnrs.com/predict
Source code
You can check the latest sources with the command:
git clone https://github.com/ddhmed/pisnrs.git
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