Nanobodies Analysis using deep learning models

The following library contains notebooks for a step by step analysis of nanobodies datasets using deep neural networks and a complete algorithm to explore and analyze the trained models. The algorithm for analysis is still in optimization stage and adaptation for different models.
They were all developed by Lirane Bitton under the supervision of Dina Schneidman at the Hebrew University of Jerusalem.

Requirements

• python >= 3.7.3 (a requirements.txt is provided)
• cd-hit installed on your system
• mafft installed on your system

Inputs

In data folder you can find two input dataset of nanobodies binding gst and has proteins.

Workflow

Start with the dataset_explore notebook then continue with the analysis one. For more statistics about cdr amino acids distribution go through the cdr_posistion_stats notebook.

Contacts

Lirane Bitton: lirane.bitton@mail.huji.ac.il Dina Schneidman: dina.schneidman@mail.huji.ac.il