Antibody complementarity determining region design using high-capacity machine learning.

ABSTRACT:

Motivation

The precise targeting of antibodies and other protein therapeutics is required for their proper function and the elimination of deleterious off-target effects. Often the molecular structure of a therapeutic target is unknown and randomized methods are used to design antibodies without a model that relates antibody sequence to desired properties.

Results

Here, we present Ens-Grad, a machine learning method that can design complementarity determining regions of human Immunoglobulin G antibodies with target affinities that are superior to candidates derived from phage display panning experiments. We also demonstrate that machine learning can improve target specificity by the modular composition of models from different experimental campaigns, enabling a new integrati

SUBMITTER: Liu G 

PROVIDER: S-EPMC7141872 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature