by Sumit Tarafder and Debswapna Bhattacharya
Codebase for our locality-aware invariant Point Attention-based RNA ScorEr (lociPARSE).
- Use conda virtual environment to install dependencies for lociPARSE. The following command will create a virtual environment named 'lociPARSE'.
conda env create -f lociPARSE_environment.yml
- Activate the virtual environment
conda activate lociPARSE
Typical installation time on a "normal" desktop computer should take a few minutes in a 64-bit Linux system.
Instructions for running lociPARSE:
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Put the desired pdb(s) inside the 'Input' folder.
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Put the PDB ID or list of IDs in the text file named 'input.txt' inside 'Input' folder. See the example in the 'Input' folder.
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Run
chmod a+x lociPARSE.sh && ./lociPARSE.sh -
The script will generate features for every ID listed in Input/input.txt and store in individual folder inside 'Feature' folder. Then it will run inference and store predicted molecular-level lDDT (pMoL) and predicted nucleotide-wise lDDT (pNuL) in "score.txt" in individual folder inside 'Prediction' folder.
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First line in the output "score.txt" shows pMoL score. Each of the subsequent lines sepcify 2 columns: column-1: nucleotide index in PDB and column-2: pNuL score.
Inference time for a typical RNA structure (~70 nucleotides) should take a few seconds.
- The lists of IDs used in our training set, test sets and validation set used in ablation study are available under Datasets.
- Training set and test set of 30 independent RNAs were taken from trRosettaRNA.
- CASP15 experimental strctures and all submiited predictions were downloaded from CASP15.
- 60 non-redundant targets for TS60 validation set were curated from PDB.
