GENERALIST: A latent space based generative model for protein sequence families.

Autor: Hoda Akl, Brooke Emison, Xiaochuan Zhao, Arup Mondal, Alberto Perez, Purushottam D Dixit
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: PLoS Computational Biology, Vol 19, Iss 11, p e1011655 (2023)
Druh dokumentu: article
ISSN: 1553-734X
1553-7358
DOI: 10.1371/journal.pcbi.1011655
Popis: 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.
Databáze: Directory of Open Access Journals
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