2023-07-25 アルゴンヌ国立研究所(ANL)
◆多反応性の抗体は誤った分子と結合することがあり、薬の開発にとって問題となる。ハーバード医学大学のAndrew Kruse教授率いる研究チームは、多反応性を避けるための結晶化研究を行い、効果的な抗体を特定する手段を見つけた。酵母ディスプレイ法を使用して抗体断片を分離し、機械学習モデルを使って多反応性に寄与する抗体の特徴を予測した。これにより、研究者は効果的な抗体を選択し、多反応性の抗体に時間を浪費しないことができる。さらなる研究により、抗体の多反応性についての理解が深まり、有益な抗体の特定につながる可能性がある。
<関連情報>
- https://www.anl.gov/article/researchers-use-argonne-xrays-to-find-the-best-antibodies
- https://www.nature.com/articles/s41467-022-35276-4
抗体フラグメントの多反応性を評価するインシリコ手法 An in silico method to assess antibody fragment polyreactivity
Edward P. Harvey,Jung-Eun Shin,Meredith A. Skiba,Genevieve R. Nemeth,Joseph D. Hurley,Alon Wellner,Ada Y. Shaw,Victor G. Miranda,Joseph K. Min,Chang C. Liu,Debora S. Marks & Andrew C. Kruse
Nature Communications Published:07 December 2022
DOI:https://doi.org/10.1038/s41467-022-35276-4
Abstract
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 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 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.
