CavityPoser

CavityPoser: Mining For Bound-Like Conformations of RNA Using a Binding Cavity Screening Approach.

Prerequisite

Python and Python packages: numpy, pandas and scikit-learn v0.21.2
openbabel
rdock
PyMOL. For first time PyMOL users: you will need a PyMOL license file, as PyMOL is a commercial software.

Quick Start

Install Dependencies

git clone git@github.com:karoka/CavityPoser.git
cd cavity_mining/
git clone --depth=1 git@github.com:atfrank/RNAPosers.git
cd RNAPosers/
make clean
make
cd ..
cp RNAPosers/bin/featurize bin/
rm -rf RNAPosers

Using CavityPoser

Main script is src/cavityPoser.sh. It requires a pdb file containing RNA 3D structure as input. Example:

cd test/
./../src/cavityPoser.sh receptor.pdb

The predicted cavities and their corresponding scores are written in file receptor/predicted_pockets.txt.

Output

pdb	tag	cavityID	x	y	z	pred_MLP	pred_XGB	pred_RF
receptor.pdb	decoy	1	2.835	0.475	-0.482	1.000	0.886	0.705

Optional Arguments

./../src/cavityPoser.sh

It takes the following arguments:

path-to-pdb-file: input pdb file to perform cavity mining on.
scalar: 1 or 0, corresponds to different version of models (1: scalar; 0: vector). Currently the scalar version is faster and has better performance. Default: 1
rdock-param-file: rdock parameter file. 3 parameter files are provided under params/ folder. Default: blind_docking_de_novo.prm.
working-dir: working directory. Default: a sub-directory in current working directory. The sub-directory is named as the input pdb-file name without extension. e.g. If the input filename is receptor.pdb, then a folder named receptor/ will be created if not existed in current working directory and all operations will be performed there.
tag: an arbitrary identifier of the structure. Default: "decoy".
output-file: output file path and name.

Validate thesis results (Table 4.2)

cd training/

Required python environment: pandas, scikit-learn, xgboost
Use pre-trained models: python train.py 1
Re-train models: python train.py 0
Get ranking table: python get_rank 1 and python get_rank 1
Output: results/rank_testset1.csv and results/rank_testset1.csv

Publications

RNAPosers(In revision): Chhabra, Sahil, Jingru Xie, and Aaron T. Frank. "RNAPosers: Machine Learning Classifiers For RNA-Ligand Poses." bioRxiv (2019): 702449.