Efficient generative modeling of protein sequences using simple autoregressive models.

ABSTRACT: 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 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 (by a factor between 10² and 10³). Furthermore, the simple structure of our models has distinctive mathematical advantages, which translate into an improved applicability in sequence generation and evaluation. Within these models, we can easily estimat

SUBMITTER: Trinquier J 

PROVIDER: S-EPMC8490405 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature