The RESP AI model accelerates the identification of tight-binding antibodies.

ABSTRACT: High-affinity antibodies are often identified through directed evolution, which may require many iterations of mutagenesis and selection to find an optimal candidate. Deep learning techniques hold the potential to accelerate this process but the existing methods cannot provide the confidence interval or uncertainty needed to assess the reliability of the predictions. Here we present a pipeline called RESP for efficient identification of high affinity antibodies. We develop a learned representation trained on over 3 million human B-cell receptor sequences to encode antibody sequences. We then develop a variational Bayesian neural network to perform ordinal regression on a set of the directed evolution sequences binned by off-rate and quantify their likelihood to be tight binders against an

SUBMITTER: Parkinson J 

PROVIDER: S-EPMC9884274 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature