End-to-end sequence-structure-function meta-learning predicts genome-wide chemical-protein interactions for dark proteins.

ABSTRACT: Systematically discovering protein-ligand interactions across the entire human and pathogen genomes is critical in chemical genomics, protein function prediction, drug discovery, and many other areas. However, more than 90% of gene families remain "dark"-i.e., their small-molecule ligands are undiscovered due to experimental limitations or human/historical biases. Existing computational approaches typically fail when the dark protein differs from those with known ligands. To address this challenge, we have developed a deep learning framework, called PortalCG, which consists of four novel components: (i) a 3-dimensional ligand binding site enhanced sequence pre-training strategy to encode the evolutionary links between ligand-binding sites across gene families; (ii) an end-to-end pretrainin

SUBMITTER: Cai T 

PROVIDER: S-EPMC9886305 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature