RNAPosers

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

Prerequisite

Anaconda

Quick Start

Install

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

Setup environment

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

Using RNAPosers

./src/run.sh -h

Arguments

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)

Example

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

Output

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
...

PyMOL plugin

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.

Additional Notes

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

Description	Type
Carbon sp3	C.3
Carbon sp2	C.2
Carbon sp	C.1
Carbon aromatic	C.ar
Carbocation (guanadinium)	C.cat
Nitrogen sp3	N.3
Nitrogen sp2	N.2
Nitrogen sp	N.1
Nitrogen aromatic	N.ar
Nitrogen amide	N.am
Nitrogen trigonal planar	N.pl3
Nitrogen sp3 positively charged	N.4
Oxygen sp3	O.3
Oxygen sp2	O.2
Oxygen in carboxylates and phosphates	O.co2
Sulphur sp3	S.3
Sulphur sp2	S.2
Sulphoxide sulphur	S.o
Sulphone sulphur	S.o2
Phosphorus sp3	P.3