An in silico method to assess antibody fragment polyreactivity

Antibodies are essential biological research tools and important therapeutic agents, but some exhibit non-specific binding to off-target proteins and other biomolecules. Such polyreactive antibodies compromise screening pipelines, lead to incorrect and irreproducible experimental results, and are generally intractable for clinical development. Here, we design a set of experiments using a diverse naive synthetic camelid antibody fragment (nanobody) library to enable machine learning models to accurately assess polyreactivity from protein sequence (AUC > 0.8). Moreover, our models provide quantitative scoring metrics that predict the effect of amino acid substitutions on polyreactivity. We experimentally test our models' performance on three independent nanobody scaffolds, where over 90% of predicted substitutions successfully reduced polyreactivity. Importantly, the models allow us to diminish the polyreactivity of an angiotensin II type I receptor antagonist nanobody, without compromising its functional properties. We provide a companion web-server that offers a straightforward means of predicting polyreactivity and polyreactivity-reducing mutations for any given nanobody sequence.

Automated summary

In this study, a diverse synthetic camelid antibody fragment library was used to design a set of experiments that allowed machine learning models to accurately assess polyreactivity from protein sequence. The models provided quantitative scoring metrics and predicted the effect of amino acid substitutions on polyreactivity. Experimental tests showed that over 90% of predicted substitutions successfully reduced polyreactivity without compromising the functional properties. A web server was also provided for predicting polyreactivity and polyreactivity-reducing mutations for any given nanobody sequence.