- PDB-7xyq: Crystal strucutre of PD-L1 and the computationally designed DBL1_... -
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基本情報
登録情報
データベース: PDB / ID: 7xyq
タイトル
Crystal strucutre of PD-L1 and the computationally designed DBL1_03 protein binder
要素
CD274 molecule
DBL1_03
キーワード
IMMUNE SYSTEM / PD-L1
機能・相同性
機能・相同性情報
: / positive regulation of tolerance induction to tumor cell / negative regulation of tumor necrosis factor superfamily cytokine production / positive regulation of activated CD8-positive, alpha-beta T cell apoptotic process / negative regulation of CD8-positive, alpha-beta T cell activation / negative regulation of CD4-positive, alpha-beta T cell proliferation / negative regulation of interleukin-10 production / negative regulation of activated T cell proliferation / positive regulation of interleukin-10 production / negative regulation of type II interferon production ...: / positive regulation of tolerance induction to tumor cell / negative regulation of tumor necrosis factor superfamily cytokine production / positive regulation of activated CD8-positive, alpha-beta T cell apoptotic process / negative regulation of CD8-positive, alpha-beta T cell activation / negative regulation of CD4-positive, alpha-beta T cell proliferation / negative regulation of interleukin-10 production / negative regulation of activated T cell proliferation / positive regulation of interleukin-10 production / negative regulation of type II interferon production / negative regulation of T cell proliferation / positive regulation of T cell proliferation / T cell costimulation / response to cytokine / recycling endosome membrane / actin cytoskeleton / early endosome membrane / cellular response to lipopolysaccharide / cell surface receptor signaling pathway / immune response / external side of plasma membrane / extracellular exosome / nucleoplasm 類似検索 - 分子機能
National Natural Science Foundation of China (NSFC)
92169208
中国
引用
ジャーナル: Nature / 年: 2023 タイトル: De novo design of protein interactions with learned surface fingerprints. 著者: Pablo Gainza / Sarah Wehrle / Alexandra Van Hall-Beauvais / Anthony Marchand / Andreas Scheck / Zander Harteveld / Stephen Buckley / Dongchun Ni / Shuguang Tan / Freyr Sverrisson / Casper ...著者: Pablo Gainza / Sarah Wehrle / Alexandra Van Hall-Beauvais / Anthony Marchand / Andreas Scheck / Zander Harteveld / Stephen Buckley / Dongchun Ni / Shuguang Tan / Freyr Sverrisson / Casper Goverde / Priscilla Turelli / Charlène Raclot / Alexandra Teslenko / Martin Pacesa / Stéphane Rosset / Sandrine Georgeon / Jane Marsden / Aaron Petruzzella / Kefang Liu / Zepeng Xu / Yan Chai / Pu Han / George F Gao / Elisa Oricchio / Beat Fierz / Didier Trono / Henning Stahlberg / Michael Bronstein / Bruno E Correia / 要旨: 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 ...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.