DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network.

ABSTRACT: Comprehensive characterization of ligand-binding sites is invaluable to infer molecular functions of hypothetical proteins, trace evolutionary relationships between proteins, engineer enzymes to achieve a desired substrate specificity, and develop drugs with improved selectivity profiles. These research efforts pose significant challenges owing to the fact that similar pockets are commonly observed across different folds, leading to the high degree of promiscuity of ligand-protein interactions at the system-level. On that account, novel algorithms to accurately classify binding sites are needed. Deep learning is attracting a significant attention due to its successful applications in a wide range of disciplines. In this communication, we present DeepDrug3D, a new approach to characterize a

SUBMITTER: Pu L 

PROVIDER: S-EPMC6375647 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature