3D deep convolutional neural networks for amino acid environment similarity analysis.

ABSTRACT:

Background

Central to protein biology is the understanding of how structural elements give rise to observed function. The surfeit of protein structural data enables development of computational methods to systematically derive rules governing structural-functional relationships. However, performance of these methods depends critically on the choice of protein structural representation. Most current methods rely on features that are manually selected based on knowledge about protein structures. These are often general-purpose but not optimized for the specific application of interest. In this paper, we present a general framework that applies 3D convolutional neural network (3DCNN) technology to structure-based protein analysis. The framework automatically extracts task-specific fea

SUBMITTER: Torng W 

PROVIDER: S-EPMC5472009 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature