EMDB/PDB/SASBDBから引用されている文献のデータベース

キーワード
構造解析手法	All X線回折 NMR 電子顕微鏡小角散乱中性子線回折線維回折粉末回折赤外分光 EPR FRET 理論モデルハイブリッド
著者・登録者
ジャーナル
IF	>30 >20 >10 >5 all

タイトル	De novo design of protein interactions with learned surface fingerprints.
ジャーナル・号・ページ	Nature, Vol. 617, Issue 7959, Page 176-184, Year 2023
掲載日	2023年4月26日
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 /
PubMed 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.
リンク	Nature / PubMed:37100904 / PubMed Central
手法	EM (単粒子) / X線回折
解像度	2.63 - 3.29 Å
構造データ	14922 7zrv EMDB-14922, PDB-7zrv: cryo-EM structure of omicron spike in complex with de novo designed binder, full map 手法: EM (単粒子) / 解像度: 2.8 Å 14930 7zsd EMDB-14930, PDB-7zsd: cryo-EM structure of omicron spike in complex with de novo designed binder, local 手法: EM (単粒子) / 解像度: 3.29 Å 14947 7zss EMDB-14947: cryo-EM structure of D614 spike in complex with de novo designed binder, full and local maps(addition) PDB-7zss: cryo-EM structure of D614 spike in complex with de novo designed binder 手法: EM (単粒子) / 解像度: 2.63 Å PDB-7xad: Crystal strucutre of PD-L1 and DBL2_02 designed protein binder 手法: X-RAY DIFFRACTION / 解像度: 3.0 Å PDB-7xyq: Crystal strucutre of PD-L1 and the computationally designed DBL1_03 protein binder 手法: X-RAY DIFFRACTION / 解像度: 2.85 Å
化合物	ChemComp-ARG: ARGININE / L-アルギニン-ω′-カチオン / アルギニン ChemComp-HOH: WATER / 水 ChemComp-NAG: 2-acetamido-2-deoxy-beta-D-glucopyranose / N-アセチル-β-D-グルコサミン / N-アセチルグルコサミン
由来	severe acute respiratory syndrome coronavirus 2 (SARSコロナウイルス2) drosophila melanogaster (キイロショウジョウバエ) tequatrovirus t4 (T4ファージ) homo sapiens (ヒト) chemical production metagenome (メタゲノム) synthetic construct (人工物)
キーワード	IMMUNE SYSTEM (免疫系) / PD-L1 (PD-L1) / ANTIVIRAL PROTEIN / SARS-COV2 (SARSコロナウイルス2) / de novo (De novo) / design binder / spike / RBD / receptor binding domain (受容体)