- EMDB-40557: Cryo-EM structure of designed Influenza HA binder, HA_20, bound t... -
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Entry
Database: EMDB / ID: EMD-40557
Title
Cryo-EM structure of designed Influenza HA binder, HA_20, bound to Influenza HA (Strain: Iowa43)
Map data
unsharpened
Sample
Complex: Influenza HA (Iowa43) bound to RFdiffusion designed minibinder, HA_20
Protein or peptide: Hemagglutinin HA1 chain
Protein or peptide: Hemagglutinin
Protein or peptide: HA_20 minibinder (RFdiffusion-designed)
Ligand: 2-acetamido-2-deoxy-beta-D-glucopyranose
Keywords
flu / influenza / hemagglutinin / HA / Iowa43 / HA_20 / DE NOVO PROTEIN / minibinder / binder / designed protein / fusion protein / glycoprotein / DE NOVO PROTEIN-Viral Protein complex
Function / homology
Function and homology information
viral budding from plasma membrane / clathrin-dependent endocytosis of virus by host cell / host cell surface receptor binding / fusion of virus membrane with host plasma membrane / fusion of virus membrane with host endosome membrane / viral envelope / virion attachment to host cell / host cell plasma membrane / virion membrane / membrane Similarity search - Function
Journal: Nature / Year: 2023 Title: De novo design of protein structure and function with RFdiffusion. Authors: Joseph L Watson / David Juergens / Nathaniel R Bennett / Brian L Trippe / Jason Yim / Helen E Eisenach / Woody Ahern / Andrew J Borst / Robert J Ragotte / Lukas F Milles / Basile I M Wicky / ...Authors: Joseph L Watson / David Juergens / Nathaniel R Bennett / Brian L Trippe / Jason Yim / Helen E Eisenach / Woody Ahern / Andrew J Borst / Robert J Ragotte / Lukas F Milles / Basile I M Wicky / Nikita Hanikel / Samuel J Pellock / Alexis Courbet / William Sheffler / Jue Wang / Preetham Venkatesh / Isaac Sappington / Susana Vázquez Torres / Anna Lauko / Valentin De Bortoli / Emile Mathieu / Sergey Ovchinnikov / Regina Barzilay / Tommi S Jaakkola / Frank DiMaio / Minkyung Baek / David Baker / Abstract: There has been considerable recent progress in designing new proteins using deep-learning methods. Despite this progress, a general deep-learning framework for protein design that enables solution of ...There has been considerable recent progress in designing new proteins using deep-learning methods. Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.
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