5N7W

Computationally designed functional antibody

5N7W の概要

• エントリーDOI: 10.2210/pdb5n7w/pdb
• 分子名称: Antibody Fragment Heavy Chain, Antibody Fragment Light Chain, Interleukin-17A, ... (4 entities in total)
• 機能のキーワード: computationally designed antibody il17, immune system
• 由来する生物種: Homo sapiens (Human); 詳細
• タンパク質・核酸の鎖数: 6
• 化学式量合計: 129625.71

構造登録者

Hargreaves, D.,Breed, J. (登録日: 2017-02-21, 公開日: 2018-11-14, 最終更新日: 2024-11-06)

主引用文献

Nimrod, G.,Fischman, S.,Austin, M.,Herman, A.,Keyes, F.,Leiderman, O.,Hargreaves, D.,Strajbl, M.,Breed, J.,Klompus, S.,Minton, K.,Spooner, J.,Buchanan, A.,Vaughan, T.J.,Ofran, Y.
Computational Design of Epitope-Specific Functional Antibodies.
Cell Rep, 25:2121-2131.e5, 2018

PubMed Abstract: The ultimate goal of protein design is to introduce new biological activity. We propose a computational approach for designing functional antibodies by focusing on functional epitopes, integrating large-scale statistical analysis with multiple structural models. Machine learning is used to analyze these models and predict specific residue-residue contacts. We use this approach to design a functional antibody to counter the proinflammatory effect of the cytokine interleukin-17A (IL-17A). X-ray crystallography confirms that the designed antibody binds the targeted epitope and the interaction is mediated by the designed contacts. Cell-based assays confirm that the antibody is functional. Importantly, this approach does not rely on a high-quality 3D model of the designed complex or even a solved structure of the target. As demonstrated here, this approach can be used to design biologically active antibodies, removing some of the main hurdles in antibody design and in drug discovery.

PubMed: 30463010
DOI: 10.1016/j.celrep.2018.10.081

実験手法

X-RAY DIFFRACTION (1.96 Å)