An in silico method to assess antibody fragment polyreactivity

Author:

Harvey Edward P.ORCID,Shin Jung-EunORCID,Skiba Meredith A.ORCID,Nemeth Genevieve R.ORCID,Hurley Joseph D.ORCID,Wellner AlonORCID,Shaw Ada Y.ORCID,Miranda Victor G.ORCID,Min Joseph K.ORCID,Liu Chang C.ORCID,Marks Debora S.ORCID,Kruse Andrew C.ORCID

Abstract

ABSTRACTAntibodies 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. We designed a set of experiments using a diverse naïve 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 tested our model’s performance on three independent nanobody scaffolds, where over 90% of predicted substitutions successfully reduced polyreactivity. Importantly, the model allowed us to diminish the polyreactivity of an angiotensin II type I receptor antagonist nanobody, without compromising its pharmacological properties. We provide a companion web-server that offers a straightforward means of predicting polyreactivity and polyreactivity-reducing mutations for any given nanobody sequence.

Publisher

Cold Spring Harbor Laboratory

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