Latent generative landscapes as maps of functional diversity in protein sequence space.

ABSTRACT: Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β-lactamases, ion channels, and transcription factors. We provide support on how the landscape h

SUBMITTER: Ziegler C 

PROVIDER: S-EPMC10113739 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature