Transferable deep generative modeling of intrinsically disordered protein conformations.

ABSTRACT: Intrinsically disordered proteins have dynamic structures through which they play key biological roles. The elucidation of their conformational ensembles is a challenging problem requiring an integrated use of computational and experimental methods. Molecular simulations are a valuable computational strategy for constructing structural ensembles of disordered proteins but are highly resource-intensive. Recently, machine learning approaches based on deep generative models that learn from simulation data have emerged as an efficient alternative for generating structural ensembles. However, such methods currently suffer from limited transferability when modeling sequences and conformations absent in the training data. Here, we develop a novel generative model that achieves high levels of tran

SUBMITTER: Janson G 

PROVIDER: S-EPMC10871340 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature