pisnrs 0.0.6

A computational server for potential inhibitor and scaffold prediction of nuclear receptors

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