lociPARSE: A Python repository from Bhattacharya Lab

lociPARSE: a locality-aware invariant point attention model for scoring RNA 3D structures

by Sumit Tarafder and Debswapna Bhattacharya

Codebase for our locality-aware invariant Point Attention-based RNA ScorEr (lociPARSE).

Installation

pip install lociPARSE

git clone https://github.com/Bhattacharya-Lab/lociPARSE.git
cd lociPARSE
pip install .

Typical installation time should take less than a minute in a 64-bit Linux system.

Usage

Instructions for running lociPARSE:

from lociPARSE import lociparse
lp = lociparse()
score = lp.score("R1108.pdb")

Additional functionality

score.pMoL.show() #Returns pMoL value
score.pNuL.show() #Returns a list of pNuL values
score.pNuL.show(1) #Returns the pNuL value of 1st nucleotide
score.save("score.txt") #Saves the scores

Given an RNA pdb "R1108.pdb" as input, lociPARSE predicts both molecular-level lDDT (pMoL) and nucleotide-wise lDDT (pNuL) score.
Use show() function to print the pMoL or pNuL values.
Save the output in a provided filename of your choice("score.txt"). First line shows pMoL score. Each of the subsequent lines sepcify 2 columns: column-1: nucleotide index in PDB and column-2: predicted nucleotide-wise lDDT (pNuL) score.

Inference time for a typical RNA structure (~70 nucleotides) should take a few seconds.

Datasets

The lists of IDs used in our training set, test sets and validation set used in ablation study are available here.
Training set and test set of 30 independent RNAs were taken from trRosettaRNA.
CASP15 experimental strctures and all submiited predictions were downloaded from CASP15.
The set of 60 non-redundant RNA targets TS60 for hyperparameter optimization was in-house curated. See (https://doi.org/10.1093/biomethods/bpae047) for more details.

Training and evaluation materials

If you want to train or evaluate lociPARSE, please follow these initial steps:

Download the necessary materials from here and place it in the root directory(/lociPARSE)
```
wget https://zenodo.org/records/12729167/files/Materials.tar.gz
```

Extract the Material.tar.gz folder

tar -xvzf Materials.tar.gz --strip-components=1

Make sure if you have installed appropriate torch version compatible with the CUDA version installed in your machine for GPU training. See here for more (https://pytorch.org/get-started/locally/).

Training lociPARSE

If you wish to train lociPARSE from scratch on our training set, please follow these steps:

Download our training dataset Train.tar.gz from here and place it inside Input/Dataset folder.
Extract the training dataset
```
tar -xzvf Train.tar.gz
```
Run the following command to train our architecture
```
chmod a+x lociPARSE_train.sh && ./lociPARSE_train.sh > log.txt
```
It will take approximately 16 hours to finish feature generation and 50 epochs of training on a single A100 gpu.
The best model saved on validation loss will be placed inside the Model folder as "QAmodel_retrained.pt".

Evaluation of lociPARSE

If you want to generate our reported results in the paper from the provided predictions, follow these steps:

To generate Tables 1-6, please run the following commands one by one.

cd Evaluate
python3 QA_eval.py Test30_CASP15 0
python3 QA_eval.py ARES_benchmark2 0

You will find the corresponding results inside Evaluate/Results folder.
To generate Supplementary Figures S1-S2, please run the following commands.
```
 cd Evaluate
 python3 draw.py
```
Generated figures will be inside Evaluate/Figures folder.

If you want to predict the scores by lociPARSE from scratch and re-evaluate, follow these steps:

Download our test datasets Test.tar.gz and Ares_set.tar.gz from here and place it inside Input/Dataset folder.

Extract the folders

tar -xzvf Test.tar.gz

tar -xzvf Ares_set.tar.gz

To predict and evaluate results on our two test sets Test30 and CASP15 (Tables 1-5), please run the following command.
```
chmod a+x evaluate.sh && ./evaluate.sh Test30_CASP15 Model/QAmodel_lociPARSE.pt
```
To predict and evaluate results on ARES benchmark set-2 (Table 6), please run the following command. [This will be slow due to ~76k models in this test set]
```
chmod a+x evaluate.sh && ./evaluate.sh ARES_benchmark2 Model/QAmodel_Ares_set.pt
```
You will find the corresponding results inside Evaluate/Results folder.