End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins.

ABSTRACT: Structural proteins are the basis of many biomaterials and key construction and functional components of all life. Further, it is well-known that the diversity of proteins' function relies on their local structures derived from their primary amino acid sequences. Here, we report a deep learning model to predict the secondary structure content of proteins directly from primary sequences, with high computational efficiency. Understanding the secondary structure content of proteins is crucial to designing proteins with targeted material functions, especially mechanical properties. Using convolutional and recurrent architectures and natural language models, our deep learning model predicts the content of two essential types of secondary structures, the α-helix and the β-sheet. The training dat

SUBMITTER: Yu CH 

PROVIDER: S-EPMC9347213 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature