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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Nature communications Ročník 14; číslo 1; s. 2222 - 15
Hlavní autoři:	Ziegler, Cheyenne, Martin, Jonathan, Sinner, Claude, Morcos, Faruck
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 19.04.2023 Nature Publishing Group Nature Portfolio
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2041-1723 2041-1723
DOI:	10.1038/s41467-023-37958-z