NanoNet: Rapid and accurate end-to-end nanobody modeling by deep learning

Tomer Cohen*, Matan Halfon, Dina Schneidman-Duhovny*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Antibodies are a rapidly growing class of therapeutics. Recently, single domain camelid VHH antibodies, and their recognition nanobody domain (Nb) appeared as a cost-effective highly stable alternative to full-length antibodies. There is a growing need for high-throughput epitope mapping based on accurate structural modeling of the variable domains that share a common fold and differ in the Complementarity Determining Regions (CDRs). We develop a deep learning end-to-end model, NanoNet, that given a sequence directly produces the 3D coordinates of the backbone and Cβ atoms of the entire VH domain. For the Nb test set, NanoNet achieves 3.16Å average RMSD for the most variable CDR3 loops and 2.65Å, 1.73Å for the CDR1, CDR2 loops, respectively. The accuracy for antibody VH domains is even higher: 2.38Å RMSD for CDR3 and 0.89Å, 0.96Å for the CDR1, CDR2 loops, respectively. NanoNet run times allow generation of ∼1M nanobody structures in less than 4 hours on a standard CPU computer enabling high-throughput structure modeling. NanoNet is available at GitHub: https://github.com/dina-lab3D/NanoNet

Original languageAmerican English
Article number958584
JournalFrontiers in Immunology
Volume13
DOIs
StatePublished - 12 Aug 2022

Bibliographical note

Publisher Copyright:
Copyright © 2022 Cohen, Halfon and Schneidman-Duhovny.

Keywords

  • antibody
  • deep learning- artificial neural network
  • machine-learning (ML)
  • nanobody (Nb)
  • protein modeling

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