RNAPosers: Machine-Learning Pose Classifiers for RNA Containing Complexes.

This repo contains source code for RNAPosers' pose fingerprint and prediction (classification) modules. Pose fingerprint module is written in C++, and will generate executable bin/featurize once compiled. Prediction (classification) is done using Python script src/rna_poser.py and pre-trained classifiers with various parameter setting in classifier/ . All the classifiers were trained with Python 3.5.3 and sklearn v0.19.2, but should be compatible with both Python2 and Python 3 and any later version sklearn. The combined fingerprinting and classification process can be done by running src/run.sh.

RNAPosers-plugin: We also offer a PyMOL plugin version of RNAPosers (rnaposerplugin.zip) with graphical user interface as a supplement to the source code version. See Using RNAPosers PyMOL plugin for installation instructions.

Manuscript: Chhabra, Sahil, Jingru Xie, and Aaron T. Frank. "RNAPosers: Machine Learning Classifiers For RNA-Ligand Poses." J. Phys. Chem. B 2020, 124, 22, 4436–4445; Publication Date:May 19, 2020; https://doi.org/10.1021/acs.jpcb.0c02322

Training and Testing Data (Poses): https://doi.org/10.5281/zenodo.3711071

• Anaconda

git clone --depth=1 https://github.com/atfrank/RNAPosers.git
cd RNAPosers/
make clean
make
echo "export RNAPOSERS_PATH=$(pwd)" >> ~/.bashrc
source ~/.bashrc

conda env create -f env/rnaposers.yml
conda activate rnaposers

./src/run.sh -h

• receptor mol2: .mol2 file of receptor structure. Default: tests/input/1AM0/receptor.mol2
• ligand poses sd: .sd file containing all ligand posees. Default: tests/input/1AM0/poses.sd
• output file: where to save scores. Default: tests/output/1AM0.txt
• rmsd: 1, 1.5, 2, 2.5. Default: 2
• eta: 2, 24, or 248 (2A, 2A and 4A, 2A 4A and 8A). Default: 248
• stop frame: only score the first several poses. Default: -1 (using all frames)

cd $RNAPOSERS_PATH
# Test 1: score the first 10 poses of 1AM0
./src/run.sh tests/input/1AM0/receptor.mol2 tests/input/1AM0/poses.sd tests/output/1AM0.txt 10 2.5 248
# Test 2: score the full set of poses of 2B57
./src/run.sh tests/input/2B57/receptor.mol2 tests/input/2B57/poses.sd tests/output/2B57.txt

cat tests/output/1AM0.txt
# columns: prediction probability(0) probability(1)
1.000000 0.027000 0.973000
1.000000 0.023000 0.977000
1.000000 0.009000 0.991000
1.000000 0.062000 0.938000
1.000000 0.008000 0.992000
1.000000 0.050000 0.950000
1.000000 0.031000 0.969000
1.000000 0.010000 0.990000
1.000000 0.284000 0.716000
0.000000 0.687000 0.313000

cat tests/output/2B57.txt
1.000000 0.025000 0.975000
1.000000 0.015000 0.985000
1.000000 0.025000 0.975000
1.000000 0.005000 0.995000
1.000000 0.004000 0.996000
1.000000 0.004000 0.996000
1.000000 0.004000 0.996000
1.000000 0.005000 0.995000
1.000000 0.005000 0.995000
1.000000 0.004000 0.996000
...

The RNAPosers PyMOL plugin is compressed it as rnaposerplugin.zip. See PyMOL website: https://pymolwiki.org/index.php/Plugin_Manager for an instruction on installing PyMOL plugin from local file. Note that you still have to install this repo and setup environment to use the plugin.

• Ligand Atom types should to be one of the following SYBYL atom types:

If you are interested in commercial licensing of these applications (clinical, operational, etc.) please contact the University of Michigan Office of Technology Transfer for a quote and licensing options.

Drew Bennett - https://techtransfer.umich.edu/team/drew-bennett/

or

[email protected]