FrInRecon

Drugs binding mode reconstruction based on fragments interactions fingerprints.

Whereas the chemical libraries contain millions of drug compounds, the vast majority of them do not have structures to crystallized targets and is therefore impossible to characterize their binding to targets from a structural view. However, the concept of building blocks offers a novel perspective on the structural problem. A drug compound is considered a complex of small chemical blocks or fragments, which confer the relevant properties to the drug and have a high proportion of functional groups involved in protein binding. Based on this, we propose a novel approach to expand the scope of structure-based repositioning approaches by transferring the structural knowledge from a fragment to a compound level.

The FrInRecon code works with the idea to take a compound for which no structural data is available and based on the binding mode of its molecular fragments, reconstruct the full compound's binding mode in terms of non-vovalent interactions fingerprints. The reconstruction is target-based, meaning that the binding mode of fragments is defined in reference to a given target, therefore, the reconstructed binding mode of the full compound will be for the same target.

Usage

Dependencies

• Rdkit (version >= 2018.09.1)
• OpenBabel (version >= 3.0.0)
• NumPy (version >= 1.19.1)
• Pickle (version >= 4.0)
• Pandas (version >= 1.1.5)

OpenBabel: Some users encounter trouble setting up OpenBabel with Python bindings correctly. You can find some installation help for OpenBabel below.

Clone the repository

Open a terminal and clone this repository using

git clone https://github.com/madasme/FrInRecon.git

Setting the PYTHONPATH

In your terminal, add the FrInRecon repository to your PYTHONPATH variable. You can also do it manually in your ./bashrc file.

$ export PYTHONPATH=~/FrInRecon:${PYTHONPATH}

Import the module in your code

from FPrecon import *

Input files

1. Fragments fingerprints
2. Binding mode conservation scores for fragments

Required settings

1. The canonical smiles of the compound to reconstruct
2. The Uniprot/Unirep ID of the target
3. The reconstruction type to be used (e.g. union, frequency, or binary frequency)
4. Threshold of fragments binding mode conservation (i.e. how corserved has to be the binding mode in terms of mTIS)
5. Threshold of ligand proportion (i.e. proprtion of ligand fragments to be found in dataset)

Test case

As a proof of concept we have tested the FrinRecon code by conducting a fragmentation of PDB compounds and a simple conservation analysis of the PDB fragments' binding mode.

Fragmentation analysis

The fragmentation pipeline considered the following steps:

1. A fragmentation process supported by PDBParser service from PharmAI GmbH, the RdKit implementation of RECAP algorithm, and the RdKit implementation of BRICS algorithm.
2. The non-covalent interactions detection by PLIP tool in plipxml format.
3. The PLIP fingerprinting and isomorphism method provided by PharmAI GmbH services.
   For more details about the process you can check here.

Please download the fragmentation file (frin-report_v9.pickle) to be used in the test case.

Conservation analysis

Taking the fragmentation results, we conducted a simple conservation analysis checking how conserved is the binding mode of each fragment when binding to the given target. For such procedure, we considered unique combination of fragment inchikeys and target Uniprot IDs (in the format inchikey~Uniprot), with enough structural data, considering at least 5 different compounds. The conservation score was measured in mTIS (mean Tanimoto Similarity) of the fingerprint set's pairwise comparison. For more details please check here.

Please download the conservation file (frag_target_conser_stats.csv) to be used in the test case.

Test case code example

from FPrecon import *

# You will need the input files to execute the test case. You can dowload them here:
# Fragmentation file -> https://sharing.crt-dresden.de/index.php/s/weEr9nAnScvJJMM/download
# Conservation file -> https://sharing.crt-dresden.de/index.php/s/lG6v03nVYqR9lxL/download

####### INPUTS #######
report = read_pickle("frin-report_v9.pickle") #Fragmentation file (specify the right path to file)
confrag_file = "frag_target_conser_stats.csv" #Conservation file (specify the right path to file)

####### SETTINGS #######
smile = "O=c1[nH]c(=O)n(C2CC(O)C(CO)O2)cc1C=CBr" #Smiles of the ligand to reconstruct
unirep = "Q9XZT6" #Uniprot representative of the target to reconstruct
recontype = "union" #Reconstruction type. It can be union, freq or freqbin
confrag_thres = 0.2 # Threshold for the fragments conservation
proportion = 0.2 #threshold for the ligand proportion

####### EXECUTION ########
confrag = conserved_fragments(confrag_file, confrag_thres)
recon = Reconstructor(smile, unirep, report, recontype, confrag, proportion)

####### SHOWING RESULTS ########
print('Ligand smiles:',recon.smile)
print('Ligand inchikey:',recon.inchi)
print('Fragments smiles:',recon.fragsmiles)
print('Fragments inchikeys:', recon.fraginchis)
print('Ligand reconstructed fingerprint with {0}:\n{1}'.format(recontype, recon.rec_fp))

Running the test case

$ python3 testcase.py 

The FrInRecon code can be used with any other fragmentation approach and any other fingerprint descriptor that can be transferred from a fragment to a compound level. Nonetheless, keep in mind that the input files format should be maintained.

Installing Open Babel

As many users encounter problems with installing the required OpenBabel tools, we want to provide some help here. However, we cannot offer technical support. Comprehensive information about the installation of OpenBabel for Windows, Linux, and macOS can be found in the OpenBabel wiki and the OpenBabel Docs. Information about the installation of OpenBabel Python bindings can also be found there.

Using Conda, HomeBrew or the binary Windows Installer

Install OpenBabel using the binary from GitHub or with

# For Conda users
$ conda install openbabel -c conda-forge
# On macOS
$ brew install open-babel

Install the Python bindings with:

$ pip install openbabel

• Note: If you have trouble, make sure the OpenBabel version matches the one for the python bindings!

Using your Package Manager (Example for Ubuntu 20.04)

$ apt-get update && apt-get install -y \
    libopenbabel-dev \
    libopenbabel6 \
    python3-openbabel \
    openbabel

From Source (Example for Ubuntu 18.04)

Clone the OpenBabel repository into the /src directory

$ git clone -b openbabel-3-0-0 \
https://github.com/openbabel/openbabel.git

Within /src/openbabel create end enter a directory /build and configure the build using

$ cmake .. \
-DPYTHON_EXECUTABLE=/usr/bin/python3.6 \
-DPYTHON_BINDINGS=ON \
-DCMAKE_INSTALL_PREFIX=/usr/local \
-DRUN_SWIG=ON

From within the same directory (/src/openbabel/build) compile and install using

$ make -j$(nproc --all) install