GGN-GO: geometric graph networks for predicting protein function by multi-scale structure features.

ABSTRACT: Recent advances in high-throughput sequencing have led to an explosion of genomic and transcriptomic data, offering a wealth of protein sequence information. However, the functions of most proteins remain unannotated. Traditional experimental methods for annotation of protein functions are costly and time-consuming. Current deep learning methods typically rely on Graph Convolutional Networks to propagate features between protein residues. However, these methods fail to capture fine atomic-level geometric structural features and cannot directly compute or propagate structural features (such as distances, directions, and angles) when transmitting features, often simplifying them to scalars. Additionally, difficulties in capturing long-range dependencies limit the model's ability to identify

SUBMITTER: Mi J 

PROVIDER: S-EPMC11530295 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature