NanoNet
Rapid and accurate end-to-end nanobody modeling by deep learning
Rapid and accurate end-to-end nanobody modeling by deep learning
NanoNet is a deep learning-based end-to-end tool designed for rapid and accurate modeling of nanobodies (Nbs), monoclonal antibody (mAb) heavy chains, and T-cell receptor (TCR) variable beta (VB) domains. Given an amino acid sequence, NanoNet predicts the 3D coordinates of the backbone and Cβ atoms for the entire variable domain.
NanoNet can be run via our web server, colab notebook, or installed locally. Please take a look at the help section for more details.
Typical run time for 1,000 structures on a single standard CPU:
Backbone + Cβ - less than 5 seconds (For better performance, use GPU)
Backbone + SCWRL - ~20 minutes
Backbone + Modeller - ~80 minutes