Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins.

ABSTRACT: Post-translational modifications (PTMs) of proteins play a vital role in their function and stability. These modifications influence protein folding, signaling, protein-protein interactions, enzyme activity, binding affinity, aggregation, degradation, and much more. To date, over 400 types of PTMs have been described, representing chemical diversity well beyond the genetically encoded amino acids. Such modifications pose a challenge to the successful design of proteins, but also represent a major opportunity to diversify the protein engineering toolbox. To this end, we first trained artificial neural networks (ANNs) to predict eighteen of the most abundant PTMs, including protein glycosylation, phosphorylation, methylation, and deamidation. In a second step, these models were implemented i

SUBMITTER: Ertelt M 

PROVIDER: S-EPMC10965067 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature