7XAD

Crystal strucutre of PD-L1 and DBL2_02 designed protein binder

7XAD の概要

• エントリーDOI: 10.2210/pdb7xad/pdb
• 分子名称: Programmed cell death 1 ligand 1, DBL2_02 binder (2 entities in total)
• 機能のキーワード: pd-l1, immune system
• 由来する生物種: Homo sapiens (human); 詳細
• タンパク質・核酸の鎖数: 8
• 化学式量合計: 157567.24

構造登録者

Liu, K.F.,Xu, Z.P.,Han, P.,Pacesa, M.,Gao, G.F.,Chai, Y.,Tan, S.G. (登録日: 2022-03-17, 公開日: 2023-04-12, 最終更新日: 2024-11-06)

主引用文献

Gainza, P.,Wehrle, S.,Van Hall-Beauvais, A.,Marchand, A.,Scheck, A.,Harteveld, Z.,Buckley, S.,Ni, D.,Tan, S.,Sverrisson, F.,Goverde, C.,Turelli, P.,Raclot, C.,Teslenko, A.,Pacesa, M.,Rosset, S.,Georgeon, S.,Marsden, J.,Petruzzella, A.,Liu, K.,Xu, Z.,Chai, Y.,Han, P.,Gao, G.F.,Oricchio, E.,Fierz, B.,Trono, D.,Stahlberg, H.,Bronstein, M.,Correia, B.E.
De novo design of protein interactions with learned surface fingerprints.
Nature, 617:176-184, 2023

PubMed Abstract: Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein-protein interactions. We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

PubMed: 37100904
DOI: 10.1038/s41586-023-05993-x

実験手法

X-RAY DIFFRACTION (3 Å)