RNA3DCNN: Local and global quality assessments of RNA 3D structures using 3D deep convolutional neural networks.

ABSTRACT: Quality assessment is essential for the computational prediction and design of RNA tertiary structures. To date, several knowledge-based statistical potentials have been proposed and proved to be effective in identifying native and near-native RNA structures. All these potentials are based on the inverse Boltzmann formula, while differing in the choice of the geometrical descriptor, reference state, and training dataset. Via an approach that diverges completely from the conventional statistical potentials, our work explored the power of a 3D convolutional neural network (CNN)-based approach as a quality evaluator for RNA 3D structures, which used a 3D grid representation of the structure as input without extracting features manually. The RNA structures were evaluated by examining each nucl

SUBMITTER: Li J 

PROVIDER: S-EPMC6258470 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature