De novo design of protein structure and function with RFdiffusion

There has been considerable recent progress in designing new proteins using deep-learning methods 1 – 9 . 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...

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Published inNature (London) Vol. 620; no. 7976; pp. 1089 - 1100
Main Authors Watson, Joseph L., Juergens, David, Bennett, Nathaniel R., Trippe, Brian L., Yim, Jason, Eisenach, Helen E., Ahern, Woody, Borst, Andrew J., Ragotte, Robert J., Milles, Lukas F., Wicky, Basile I. M., Hanikel, Nikita, Pellock, Samuel J., Courbet, Alexis, Sheffler, William, Wang, Jue, Venkatesh, Preetham, Sappington, Isaac, Torres, Susana Vázquez, Lauko, Anna, De Bortoli, Valentin, Mathieu, Emile, Ovchinnikov, Sergey, Barzilay, Regina, Jaakkola, Tommi S., DiMaio, Frank, Baek, Minkyung, Baker, David
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 31.08.2023
Nature Publishing Group
Subjects
101/28
631/114/1305
631/114/469
631/45/612
82
82/83
Amino acid sequence
Binders
Catalytic Domain
Complexity
Cryoelectron Microscopy
Deep Learning
Design
Design specifications
Diffusion models
Electron microscopy
Hemagglutinin Glycoproteins, Influenza Virus - chemistry
Hemagglutinin Glycoproteins, Influenza Virus - metabolism
Hemagglutinin Glycoproteins, Influenza Virus - ultrastructure
Hemagglutinins
Humanities and Social Sciences
Modelling
multidisciplinary
Protein Binding
Protein structure
Proteins
Proteins - chemistry
Proteins - metabolism
Proteins - ultrastructure
Scaffolding
Science
Science & Technology - Other Topics
Science (multidisciplinary)
Structure-function relationships
Success
Topology
Online AccessGet full text

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Abstract There has been considerable recent progress in designing new proteins using deep-learning methods 1 – 9 . 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 10 , 11 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. Fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks yields a generative model for protein design that achieves outstanding performance on a wide range of protein structure and function design challenges.
AbstractList There has been considerable recent progress in designing new proteins using deep-learning methods 1 – 9 . 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 10 , 11 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. Fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks yields a generative model for protein design that achieves outstanding performance on a wide range of protein structure and function design challenges.
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.
There has been considerable recent progress in designing new proteins using deep-learning methods 1–9 . 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 10,11 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.
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 ofdiverse functional proteins from simple molecular specifications.
Abstract There has been considerable recent progress in designing new proteins using deep-learning methods1–9. 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 models10,11have 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.
There has been considerable recent progress in designing new proteins using deep-learning methods1-9. 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 models10,11 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.There has been considerable recent progress in designing new proteins using deep-learning methods1-9. 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 models10,11 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.
Author Courbet, Alexis
Ragotte, Robert J.
Ovchinnikov, Sergey
Venkatesh, Preetham
Trippe, Brian L.
Watson, Joseph L.
Torres, Susana Vázquez
Sheffler, William
Sappington, Isaac
De Bortoli, Valentin
Baker, David
Wang, Jue
Borst, Andrew J.
DiMaio, Frank
Pellock, Samuel J.
Jaakkola, Tommi S.
Yim, Jason
Mathieu, Emile
Baek, Minkyung
Lauko, Anna
Eisenach, Helen E.
Bennett, Nathaniel R.
Milles, Lukas F.
Juergens, David
Wicky, Basile I. M.
Ahern, Woody
Barzilay, Regina
Hanikel, Nikita
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  surname: Watson
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  organization: Department of Biochemistry, University of Washington, Institute for Protein Design, University of Washington
– sequence: 2
  givenname: David
  surname: Juergens
  fullname: Juergens, David
  organization: Department of Biochemistry, University of Washington, Institute for Protein Design, University of Washington, Graduate Program in Molecular Engineering, University of Washington
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  givenname: Nathaniel R.
  orcidid: 0000-0001-8590-1454
  surname: Bennett
  fullname: Bennett, Nathaniel R.
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  organization: Institute for Protein Design, University of Washington, Columbia University, Department of Statistics, Irving Institute for Cancer Dynamics, Columbia University
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  givenname: Helen E.
  surname: Eisenach
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  givenname: Woody
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  organization: Department of Biochemistry, University of Washington, Institute for Protein Design, University of Washington, Paul G. Allen School of Computer Science and Engineering, University of Washington
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  givenname: Andrew J.
  orcidid: 0000-0003-4297-7824
  surname: Borst
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  givenname: Robert J.
  surname: Ragotte
  fullname: Ragotte, Robert J.
  organization: Department of Biochemistry, University of Washington, Institute for Protein Design, University of Washington
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  givenname: Lukas F.
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  surname: Wicky
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  organization: Department of Biochemistry, University of Washington, Institute for Protein Design, University of Washington
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  givenname: Samuel J.
  orcidid: 0000-0002-7557-7985
  surname: Pellock
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  surname: Wang
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  organization: Department of Biochemistry, University of Washington, Institute for Protein Design, University of Washington
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  givenname: Preetham
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  organization: Department of Biochemistry, University of Washington, Institute for Protein Design, University of Washington, Graduate Program in Biological Physics, Structure and Design, University of Washington
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  organization: Department of Biochemistry, University of Washington, Institute for Protein Design, University of Washington, Howard Hughes Medical Institute, University of Washington
BackLink https://www.ncbi.nlm.nih.gov/pubmed/37433327$$D View this record in MEDLINE/PubMed
https://www.osti.gov/biblio/2420884$$D View this record in Osti.gov
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Snippet There has been considerable recent progress in designing new proteins using deep-learning methods 1 – 9 . Despite this progress, a general deep-learning...
There has been considerable recent progress in designing new proteins using deep-learning methods 1–9 . Despite this progress, a general deep-learning...
There has been considerable recent progress in designing new proteins using deep-learning methods . Despite this progress, a general deep-learning framework...
There has been considerable recent progress in designing new proteins using deep-learning methods. Despite this progress, a general deep-learning framework for...
There has been considerable recent progress in designing new proteins using deep-learning methods1-9. Despite this progress, a general deep-learning framework...
Abstract There has been considerable recent progress in designing new proteins using deep-learning methods1–9. Despite this progress, a general deep-learning...
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SubjectTerms 101/28
631/114/1305
631/114/469
631/45/612
82
82/83
Amino acid sequence
Binders
Catalytic Domain
Complexity
Cryoelectron Microscopy
Deep Learning
Design
Design specifications
Diffusion models
Electron microscopy
Hemagglutinin Glycoproteins, Influenza Virus - chemistry
Hemagglutinin Glycoproteins, Influenza Virus - metabolism
Hemagglutinin Glycoproteins, Influenza Virus - ultrastructure
Hemagglutinins
Humanities and Social Sciences
Modelling
multidisciplinary
Protein Binding
Protein structure
Proteins
Proteins - chemistry
Proteins - metabolism
Proteins - ultrastructure
Scaffolding
Science
Science & Technology - Other Topics
Science (multidisciplinary)
Structure-function relationships
Success
Topology
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Title De novo design of protein structure and function with RFdiffusion
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