Spatiotemporal identification of druggable binding sites using deep learning.

ABSTRACT: Identification of novel protein binding sites expands druggable genome and opens new opportunities for drug discovery. Generally, presence or absence of a binding site depends on the three-dimensional conformation of a protein, making binding site identification resemble the object detection problem in computer vision. Here we introduce a computational approach for the large-scale detection of protein binding sites, that considers protein conformations as 3D-images, binding sites as objects on these images to detect, and conformational ensembles of proteins as 3D-videos to analyze. BiteNet is suitable for spatiotemporal detection of hard-to-spot allosteric binding sites, as we showed for conformation-specific binding site of the epidermal growth factor receptor, oligomer-specific binding s

SUBMITTER: Kozlovskii I 

PROVIDER: S-EPMC7591901 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature