Latent generative landscapes as maps of functional diversity in protein sequence space

Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and gener...

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Veröffentlicht in:Nature communications Jg. 14; H. 1; S. 2222 - 15
Hauptverfasser: Ziegler, Cheyenne, Martin, Jonathan, Sinner, Claude, Morcos, Faruck
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 19.04.2023
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Schlagworte:
631/114/1305
631/114/2404
631/114/469
631/57/2266
Amino Acid Sequence
beta-Lactamases - genetics
Clustering
Fitness
Globins
Hamiltonian functions
Humanities and Social Sciences
Ion channels
Manifolds
multidisciplinary
Phylogenetics
Phylogeny
Protein composition
Protein engineering
Protein families
Proteins
Reproductive fitness
Science
Science (multidisciplinary)
Transcription Factors
Unsupervised learning
β Lactamase
ISSN:2041-1723, 2041-1723
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Abstract Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β -lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design. In this work, the authors study protein families’ VAE latent manifolds and coevolutionary Hamiltonians. These Latent Generative Landscapes predict phylogenetic groupings, fitness & functional properties for several systems with clear protein engineering/design potential.
AbstractList Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β -lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design.
Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design.Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design.
Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β -lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design. In this work, the authors study protein families’ VAE latent manifolds and coevolutionary Hamiltonians. These Latent Generative Landscapes predict phylogenetic groupings, fitness & functional properties for several systems with clear protein engineering/design potential.
Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design.
Abstract Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design.
Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design. In this work, the authors study protein families’ VAE latent manifolds and coevolutionary Hamiltonians. These Latent Generative Landscapes predict phylogenetic groupings, fitness & functional properties for several systems with clear protein engineering/design potential.
ArticleNumber 2222
Author Martin, Jonathan
Morcos, Faruck
Ziegler, Cheyenne
Sinner, Claude
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  surname: Ziegler
  fullname: Ziegler, Cheyenne
  organization: Department of Biological Sciences, University of Texas at Dallas
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  surname: Martin
  fullname: Martin, Jonathan
  organization: Department of Biological Sciences, University of Texas at Dallas
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  givenname: Claude
  surname: Sinner
  fullname: Sinner, Claude
  organization: Department of Biological Sciences, University of Texas at Dallas
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  givenname: Faruck
  orcidid: 0000-0001-6208-1561
  surname: Morcos
  fullname: Morcos, Faruck
  email: faruckm@utdallas.edu
  organization: Department of Biological Sciences, University of Texas at Dallas, Department of Bioengineering, University of Texas at Dallas, Center for Systems Biology, University of Texas at Dallas
BackLink https://www.ncbi.nlm.nih.gov/pubmed/37076519$$D View this record in MEDLINE/PubMed
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Snippet Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and...
Abstract Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by...
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SubjectTerms 631/114/1305
631/114/2404
631/114/469
631/57/2266
Amino Acid Sequence
beta-Lactamases - genetics
Clustering
Fitness
Globins
Hamiltonian functions
Humanities and Social Sciences
Ion channels
Manifolds
multidisciplinary
Phylogenetics
Phylogeny
Protein composition
Protein engineering
Protein families
Proteins
Reproductive fitness
Science
Science (multidisciplinary)
Transcription Factors
Unsupervised learning
β Lactamase
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Title Latent generative landscapes as maps of functional diversity in protein sequence space
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