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, which constructed on top of RDKit, scikit-learn under the MIT license.
Installation
Currently, PisNRs requires the following dependencies:
- Python (>= 3.6)
- rdkit (>= 2018.03.2.0)
- scikit-learn (>= 0.19.1)
- pandas (>= 0.23.1)
Ancaconda is recommended for package management and environment configures.
1. Anaconda introduction
The user can download and install Anaconda at Anaconda Python distribution. Also, Miniconda is acceptable in here. The conda source code repository is avaliable at github and project website.
2. Install RDKit with Anaconda
Creating a new conda environment with the RDKit installation with the following command:
$ conda create -c rdkit rdkit
3. Install scikit-learn
User can install scikit-learn by using pip :
pip install -U scikit-learn
or conda :
conda install scikit-learn
4. Install PisNRs
After installation of RDKit and scikit-learn, PisNRs can be installed by using pip :
pip install --upgrade 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 query 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/2.2_MolFile.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/2.4_SDFFile.sdf'
protein = 'NR1C1'
des = model.calPCMDecriptorFromMolText(sdffile, protein, moltype='sdf')
print(model.preProba(des))
3. Derive molecule scaffold of query ligand
smiles = 'CC1OC(C2=CC=CC=C2)=NC=1CN(CC1=CC(=C(C(=C1)C)OC(C(O)=O)(C)C)C)CC1OC=CC=1'
scaffold = model.calScaffoldFromSmiles(smiles)
print(scaffold)
4. Create molucule image of query ligand
smiles = '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_smiles(smiles, name='4_OutputImage.png', dir='example/') # output image of smiles to 'example/' folder
5. Batch mode uploading
smiles_list = ['CC1OC(C2=CC=CC=C2)=NC=1CN(CC1=CC(=C(C(=C1)C)OC(C(O)=O)(C)C)C)CC1OC=CC=1', 'C1=CC=CC=C1']
protein_list = ['NR1C1', 'NR1C2']
print(model.preBatch(smiles_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
### load smiles list from file
smiles_file = 'example/5_SmilesList.smiles'
smiles_list = [i.strip() for i in open(smiles_file, 'r').readlines()]
print(model.preBatch(smiles_list))
Related 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
The latest sources can be checked by using the following command:
git clone https://github.com/ddhmed/pisnrs.git
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