NanoNet - a rapid nanobody modeling tool. for citation, please cite our paper: https://www.biorxiv.org/content/10.1101/2021.08.03.454917v1 (preprint)
How to run NanoNet from google Colaboratory (currently doesn't support side chains reconstruction):
1. Just make a copy of the Colab notebook (NanoNet.ipynb) in your drive.
2. select protein type (Nb/mAb heavy chain or TCR VB).
3. select input type (sequence or path to a fasta file)
4. Provide Nb sequence/fasta.
5. Press the 'Run all' option.
How to run NanoNet locally:
1. Clone the git repository : git clone "https://github.com/dina-lab3D/NanoNet"
2. Make sure you have the following libraries installed in your environment:
- numpy
- tensorflow (2.4.0 or higher)
- Bio
- modeller (requires license - https://salilab.org/modeller/)
3. If you want to construct the side chains using Scwrl4, make sure you have it installed on your computer (requires license - http://dunbrack.fccc.edu/SCWRL3.php/).
4. Run the following command (with python 3):
python NanoNet.py <fasta file path>
this will produce a backbone + cb pdb named '<record name>_nanonet_backbone_cb.pdb' for each record in the fasta file.
options:
-s : write all the models into a single PDB file, separated with MODEL and ENDMDL (reduces running time when predicting many structures), default is False.
-o <output directory> : path to a directory to put the generated models in, default is './NanoNetResults'
-m : run side chains reconstruction using modeller, default is False. Output it to a pdb file named '<record name>_nanonet_full_relaxed.pdb'
-c <path to Scwrl4 executable>: run side chains reconstruction using scwrl, default is False. Output it to a pdb file named '<record name>_nanonet_full.pdb'
-t : use this parameter for TCR V-beta modeling, default is False
Running times for 1,000 structures on a standard CPU:
only backbone + Cb - less than 15 seconds (For better preformance use GPU and cuda). backbone + SCWRL - about 20 minutes. backbone + Modeller - about 80 minutes.