Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences

TL;DR

This paper introduces CloneBO, a Bayesian optimization method guided by a generative model trained on antibody clonal families, significantly improving the efficiency and quality of antibody design both in silico and in vitro.

Contribution

It presents CloneBO, a novel Bayesian optimization approach that leverages a large language model of antibody evolution to enhance antibody design efficiency and effectiveness.

Findings

CloneBO outperforms previous methods in in silico experiments.

CloneBO designs stronger, more stable antibodies in wet lab tests.

The approach efficiently explores the antibody sequence space.

Abstract

To build effective therapeutics, biologists iteratively mutate antibody sequences to improve binding and stability. Proposed mutations can be informed by previous measurements or by learning from large antibody databases to predict only typical antibodies. Unfortunately, the space of typical antibodies is enormous to search, and experiments often fail to find suitable antibodies on a budget. We introduce Clone-informed Bayesian Optimization (CloneBO), a Bayesian optimization procedure that efficiently optimizes antibodies in the lab by teaching a generative model how our immune system optimizes antibodies. Our immune system makes antibodies by iteratively evolving specific portions of their sequences to bind their target strongly and stably, resulting in a set of related, evolving sequences known as a clonal family. We train a large language model, CloneLM, on hundreds of thousands of clonal families and use it to design sequences with mutations that are most likely to optimize an antibody within the human immune system. We propose to guide our designs to fit previous measurements with a twisted sequential Monte Carlo procedure. We show that CloneBO optimizes antibodies substantially more efficiently than previous methods in realistic in silico experiments and designs stronger and more stable binders in in vitro wet lab experiments.