Database of articles cited by EMDB/PDB/SASBDB data

Keywords
Structure methods	All X-ray diffraction NMR electron microscopy small angle scattering neutron diffraction fiber diffraction powder diffraction infrared spectroscopy EPR fluorescence transfer theoretical model Hybrid
Author
Journal
IF	>30 >20 >10 >5 all

Title	De novo design of protein interactions with learned surface fingerprints.
Journal, issue, pages	Nature, Vol. 617, Issue 7959, Page 176-184, Year 2023
Publish date	Apr 26, 2023
Authors	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 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 ...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.
External links	Nature / PubMed:37100904 / PubMed Central
Methods	EM (single particle) / X-ray diffraction
Resolution	2.63 - 3.29 Å
Structure data	14922 7zrv EMDB-14922, PDB-7zrv: cryo-EM structure of omicron spike in complex with de novo designed binder, full map Method: EM (single particle) / Resolution: 2.8 Å 14930 7zsd EMDB-14930, PDB-7zsd: cryo-EM structure of omicron spike in complex with de novo designed binder, local Method: EM (single particle) / Resolution: 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 Method: EM (single particle) / Resolution: 2.63 Å PDB-7xad: Crystal strucutre of PD-L1 and DBL2_02 designed protein binder Method: X-RAY DIFFRACTION / Resolution: 3 Å PDB-7xyq: Crystal strucutre of PD-L1 and the computationally designed DBL1_03 protein binder Method: X-RAY DIFFRACTION / Resolution: 2.85 Å
Chemicals	ChemComp-ARG: ARGININE ChemComp-HOH: WATER ChemComp-NAG: 2-acetamido-2-deoxy-beta-D-glucopyranose
Source	severe acute respiratory syndrome coronavirus 2 drosophila melanogaster (fruit fly) tequatrovirus t4 homo sapiens (human) chemical production metagenome (others) synthetic construct (others)
Keywords	IMMUNE SYSTEM / PD-L1 / ANTIVIRAL PROTEIN / SARS-COV2 / de novo / design binder / spike / RBD / receptor binding domain