Author: Cohen, Tomer; Halfon, Matan; Schneidman-Duhovny, Dina
Title: NanoNet: Rapid end-to-end nanobody modeling by deep learning at sub angstrom resolution Cord-id: vidzhgnx Document date: 2021_8_4
ID: vidzhgnx
Snippet: 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 giv
Document: 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 CÉ‘ atoms of the entire VH domain. For the Nb test set, NanoNet achieves 1.7Ã… overall average RMSD and 3.0Ã… average RMSD for the most variable CDR3 loops. The accuracy for antibody VH domains is even higher: overall average RMSD < 1Ã… and 2.2Ã… RMSD for CDR3. NanoNet runtimes allow generation of ~1M nanobody structures in less than an hour on a standard CPU computer enabling high-throughput structure modeling.
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