hotspots 1.0.3

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Hotspots API

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The Hotspots API is the Python package for the Fragment Hotspot Maps project, a knowledge-based method for determining small molecule binding "hotspots".

For more information on this method:

Radoux, C.J. et. al., Identifying the Interactions that Determine Fragment Binding at Protein Hotspots J. Med. Chem. 2016, 59 (9), 4314-4325

Getting Started

Although the Hotspots API is publicly available, it is dependant on the CSD Python API - a commercial package.

If you are an academic user, it's likely your institution will have a license. If you are unsure if you have a license or would like to enquire about purchasing one, please contact [e-mail].

Please note, this is an academic project and we would therefore welcome feedback, contributions and collaborations. If you have any queries regarding this package please contact us ([e-mail])!

Installation

1 Install CSDS 2019

The CSDS is available from here.

You will need a valid site number and confirmation code, this will have been emailed to you when you bought your CSDS 2019 license.

2 Install GHECOM

Ghecom is available from here.

"The source code of the GHECOM is written in C, and developed and executed on the linux environment (actually on the Fedora Core). For the installation, you need the gcc compiler. If you do not want to use it, please change the "Makefile" in the "src" directory."

Download the file ghecom-src-[date].tar.gz file.

tar zxvf ghecom-src-[date].tar.gz
cd src
make

NB: The executable will be located at the parent directory.

3 Create conda environment (recommended)

conda create -n hotspots python=2.7

4 Create Install RDKit and CSD Python API

Install RDKit:

conda install -n hotspots -c rdkit rdkit

The latest standalone CSD-Python-API installer from is available here.

Install the Python CSD API:

 unzip csd-python-api-2.1.0-linux-64-py2.7-conda
 conda install -n hotspots -c <path to ccdc_conda_channel> csd-python-api

5 Install Hotspots

Install Hotspots v1.x.x:

a) Latest stable release (recommended for most users):

conda activate hotspots

pip install hotspots
or 
pip install https://github.com/prcurran/hotspots/archive/v1.x.x.zip

b) Very latest code

mkdir ./hotspots_code
git clone [e-mail]:prcurran/hotspots.git
conda activate hotspots
cd hotspots_code
pip install hotspots_code

NB: dependencies should install automatically. If they do not, please see setup.py for the package requirements!

Hotspots API Usage

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Start activating your Anaconda environment and setting some variables.

conda activate hotspots
export GHECOM_EXE=<path_to_GHECOM_executable>
export CSDHOME=<path_to_CSDS_installation>/CSD_2019

Running a Calculation

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Protein Preparation

The first step is to make sure your protein is correctly prepared for the calculation. The structures should be protonated with small molecules and waters removed. Any waters or small molecules left in the structure will be included in the calculation.

One way to do this is to use the CSD Python API:

from ccdc.protein import Protein

prot = Protein.from_file('protein.pdb')
prot.remove_all_waters()
prot.add_hydrogens()
for l in prot.ligands:
    prot.remove_ligand(l.identifier)

For best results, manually check proteins before submitting them for calculation.

Calculating Fragment Hotspot Maps

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Once the protein is prepared, the hotspots.calculation.Runner object can be used to perform the calculation:

from hotspots.calculation import Runner

runner = Runner()
# Only SuperStar jobs are parallelised (one job per processor). By default there are 3 jobs, when calculating charged interactions there are 5.
results = runner.from_protein(prot, nprocesses=3)

Alternatively, for a quick calculation, you can supply a PDB code and we will prepare the protein as described above:

runner = Runner()
results = runner.from_pdb("1hcl", nprocesses=3)

Reading and Writing Hotspots

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Writing

The hotspots.hs_io module handles the reading and writing of both hotspots.calculation.results and hotspots.best_volume.Extractor objects. The output .grd files can become quite large, but are highly compressible, therefore the results are written to a .zip archive by default, along with a PyMOL run script to visualise the output.

from hotspots.hs_io import HotspotWriter

out_dir = "results/pdb1"

# Creates "results/pdb1/out.zip"
with HotspotWriter(out_dir) as writer:
    writer.write(results)

Reading

If you want to revisit the results of a previous calculation, you can load the out.zip archive directly into a hotspots.calculation.results instance:

from hotspots.hs_io import HotspotReader

results = HotspotReader('results/pdb1/out.zip').read()

Using the Output

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While Fragment Hotspot Maps provide a useful visual guide, the grid-based data can be used in other SBDD analysis.

Scoring

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One example is scoring atoms of either proteins or small molecules.

This can be done as follows:

from ccdc.protein import Protein
from ccdc.io import MoleculeReader, MoleculeWriter
from hotspots.calculation import Runner

r = Runner()
prot = Protein.from_file("1hcl.pdb")    # prepared protein
hs = r.from_protein(prot)

# score molecule
mol = MoleculeReader("mol.mol2")
scored_mol = hs.score(mol)
with MoleculeWriter("score_mol.mol2") as w:
    w.write(scored_mol)

# score protein
scored_prot = hs.score(hs.prot)
with MoleculeWriter("scored_prot.mol2") as w:
    w.write(scored_prot)

To learn about other ways you can use the Hotspots API please see the examples directory and read our API documentation.