Neural network-derived Potts models for structure-based protein design using backbone atomic coordinates and tertiary motifs.

ABSTRACT: Designing novel proteins to perform desired functions, such as binding or catalysis, is a major goal in synthetic biology. A variety of computational approaches can aid in this task. An energy-based framework rooted in the sequence-structure statistics of tertiary motifs (TERMs) can be used for sequence design on predefined backbones. Neural network models that use backbone coordinate-derived features provide another way to design new proteins. In this work, we combine the two methods to make neural structure-based models more suitable for protein design. Specifically, we supplement backbone-coordinate features with TERM-derived data, as inputs, and we generate energy functions as outputs. We present two architectures that generate Potts models over the sequence space: TERMinator, which us

SUBMITTER: Li AJ 

PROVIDER: S-EPMC9854172 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature