ArticlePLoS computational biology2023
GENERALIST: A latent space based generative model for protein sequence families.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Controllable protein design via autoregressive direct coupling analysis conditioned on principal components.PLoS computational biology · 2026Article
- Article
- Efficient Hi-C inversion facilitates chromatin folding mechanism discovery and structure prediction.Biophysical journal · 2023Article
- Efficient Hi-C inversion facilitates chromatin folding mechanism discovery and structure prediction.bioRxiv : the preprint server for biology · 2023Article
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6 authors.
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Abstract
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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