GENERALIST: A latent space based generative model for protein sequence families
Generative models of protein sequence families are an important tool in the repertoire of protein scientists and engineers alike. However, state-of-the-art generative approaches face inference, accuracy, and overfitting- related obstacles when modeling moderately sized to large proteins and/or prote...
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Published in: | PLoS computational biology Vol. 19; no. 11; p. e1011655 |
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Main Authors: | , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
United States
Public Library of Science
01-11-2023
Public Library of Science (PLoS) |
Subjects: | |
Online Access: | Get full text |
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Summary: | Generative models of protein sequence families are an important tool in the repertoire of protein scientists and engineers alike. However, state-of-the-art generative approaches face inference, accuracy, and overfitting- related obstacles when modeling moderately sized to large proteins and/or protein families with low sequence coverage. Here, we present a simple to learn, tunable, and accurate generative model, GENERALIST: GENERAtive nonLInear tenSor-factorizaTion for protein sequences. GENERALIST accurately captures several high order summary statistics of amino acid covariation. GENERALIST also predicts conservative local optimal sequences which are likely to fold in stable 3D structure. Importantly, unlike current methods, the density of sequences in GENERALIST-modeled sequence ensembles closely resembles the corresponding natural ensembles. Finally, GENERALIST embeds protein sequences in an informative latent space. GENERALIST will be an important tool to study protein sequence variability. |
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Bibliography: | new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1553-7358 1553-734X 1553-7358 |
DOI: | 10.1371/journal.pcbi.1011655 |