Parametrically guided design of beta barrels and transmembrane nanopores using deep learning.

ABSTRACT: Francis Crick's global parameterization of coiled coil geometry has been widely useful for guiding design of new protein structures and functions. However, design guided by similar global parameterization of beta barrel structures has been less successful, likely due to the deviations from ideal barrel geometry required to maintain interstrand hydrogen bonding without introducing backbone strain. Instead, beta barrels have been designed using two-dimensional structural blueprints; while this approach has successfully generated new fluorescent proteins, transmembrane nanopores, and other structures, it requires expert knowledge and provides only indirect control over the global shape. Here, we show that the simplicity and control over shape and structure provided by parametric representatio

SUBMITTER: Kim DE 

PROVIDER: S-EPMC12478100 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature