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• W4311450855 abstract "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. To that end, we present a simple to learn, tunable, and accurate generative model, GENERALIST: GENERAtive nonLInear tenSor-factorizaTion for protein sequences. Compared to state-of-the-art methods, 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 other methods, the density of sequences in GENERALIST-modeled sequence ensembles closely resembles the corresponding natural ensembles. GENERALIST will be an important tool to study protein sequence variability." @default.
• W4311450855 created "2022-12-26" @default.
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• W4311450855 date "2022-12-14" @default.
• W4311450855 modified "2023-10-18" @default.
• W4311450855 title "GENERALIST: An efficient generative model for protein sequence families" @default.
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• W4311450855 doi "https://doi.org/10.1101/2022.12.12.520114" @default.
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