DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.

ABSTRACT:

Motivation

Significant improvements in the prediction of protein residue-residue contacts are observed in the recent years. These contacts, predicted using a variety of coevolution-based and machine learning methods, are the key contributors to the recent progress in ab initio protein structure prediction, as demonstrated in the recent CASP experiments. Continuing the development of new methods to reliably predict contact maps is essential to further improve ab initio structure prediction.

Results

In this paper we discuss DNCON2, an improved protein contact map predictor based on two-level deep convolutional neural networks. It consists of six convolutional neural networks-the first five predict contacts at 6, 7.5, 8, 8.5 and 10 Å distance thresholds, and the last one uses t

SUBMITTER: Adhikari B 

PROVIDER: S-EPMC5925776 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature