Structure-based protein function prediction using graph convolutional networks.

ABSTRACT: The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures. It outperforms current leading methods and sequence-based Convolutional Neural Networks and scales to the size of current sequence repositories. Augmenting the training set of experimental structures with homology models allows us to significantly expand the number of predictable functions. DeepFRI has significant de-noising capability, with only a minor drop in performance when experimental structures are replaced by protein models. C

SUBMITTER: Gligorijevic V 

PROVIDER: S-EPMC8155034 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature