Evidence
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Generative models have thus emerged as promising candidates for sequence-data-driven protein design, enabling both the generation of novel sequences and the extraction of structural and functional information from rapidly growing sequence databases. Unlike conventional experimental methods, these models learn the underlying distribution of protein sequences directly from data, offering a computational route to explore the vast biochemical space at substantially lower cost.
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Efficient generative modeling of protein sequences using simple autoregressive models
Generative models emerge as promising candidates for novel sequence-data driven approaches to protein design, and for the extraction of structural and functional information about proteins deeply hidden in rapidly growing sequence databases. Here we propose simple autoregressive models as highly acc…
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Generative models emerge as promising candidates for novel sequence-data driven approaches to protein design, and for the extraction of structural and functional information about proteins deeply hidden in rapidly growing sequence databases. Here we propose simple autoregressive models as highly accurate but computationally extremely efficient generative sequence models. We show that they perform similarly to existing approaches based on Boltzmann machines or deep generative models, but at a substantially lower computational cost. Furthermore, the simple structure of our models has distinctive mathematical advantages, which translate into an improved applicability in sequence generation and evaluation. Using these models, we can easily estimate both the model probability of a given sequence, and the size of the functional sequence space related to a specific protein family.