Improving antibody affinity using laboratory data with language model guided design

Abstract

AO_SCPLOWBSTRACTC_SCPLOWProtein design involves navigating vast sequence spaces to discover sequences with desired traits. Language models (LMs) pretrained on universal protein datasets have shown potential to make this search space tractable. However, LMs trained solely on natural sequences have limitations in creating proteins with novel functions. In this work, we used a combination of methods to finetune pretrained LMs on laboratory data collected in an anti-CD40L single domain antibody library campaign to develop an ensemble scoring function to model the fitness landscape and guide the design of new antibodies. Laboratory experiments confirmed improved CD40L affinity in the designed antibodies. Notably, the designs improved the affinities of four antibodies, originally ranging from 1 nanomolar to 100 picomolar, all to below 25 picomolar, approaching the limit of detection. This work is a promising step towards realizing the potential of LMs to leverage laboratory data to develop improved treatments for diseases.