Structure-based prediction of protein-nucleic acid binding using graph neural networks.

ABSTRACT: Protein-nucleic acid (PNA) binding plays critical roles in the transcription, translation, regulation, and three-dimensional organization of the genome. Structural models of proteins bound to nucleic acids (NA) provide insights into the chemical, electrostatic, and geometric properties of the protein structure that give rise to NA binding but are scarce relative to models of unbound proteins. We developed a deep learning approach for predicting PNA binding given the unbound structure of a protein that we call PNAbind. Our method utilizes graph neural networks to encode the spatial distribution of physicochemical and geometric properties of protein structures that are predictive of NA binding. Using global physicochemical encodings, our models predict the overall binding function of a prote

SUBMITTER: Sagendorf JM 

PROVIDER: S-EPMC11427629 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature