Predicting antibody binders and generating synthetic antibodies using deep learning.

ABSTRACT: The antibody drug field has continually sought improvements to methods for candidate discovery and engineering. Historically, most such methods have been laboratory-based, but informatics methods have recently started to make an impact. Deep learning, a subfield of machine learning, is rapidly gaining prominence in the biomedical research. Recent advances in microfluidics technologies and next-generation sequencing have not only revolutionized therapeutic antibody discovery, but also contributed to a vast amount of antibody repertoire sequencing data, providing opportunities for deep learning-based applications. Previously, we used microfluidics, yeast display, and deep sequencing to generate a panel of binder and non-binder antibody sequences to the cancer immunotherapy targets PD-1 and C

SUBMITTER: Lim YW 

PROVIDER: S-EPMC9067455 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature