ECNet is an evolutionary context-integrated deep learning framework for protein engineering.

ABSTRACT: Machine learning has been increasingly used for protein engineering. However, because the general sequence contexts they capture are not specific to the protein being engineered, the accuracy of existing machine learning algorithms is rather limited. Here, we report ECNet (evolutionary context-integrated neural network), a deep-learning algorithm that exploits evolutionary contexts to predict functional fitness for protein engineering. This algorithm integrates local evolutionary context from homologous sequences that explicitly model residue-residue epistasis for the protein of interest with the global evolutionary context that encodes rich semantic and structural features from the enormous protein sequence universe. As such, it enables accurate mapping from sequence to function and provi

SUBMITTER: Luo Y 

PROVIDER: S-EPMC8484459 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature