ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks.

ABSTRACT:

Motivation

Contact-map of a protein sequence dictates the global topology of structural fold. Accurate prediction of the contact-map is thus essential to protein 3D structure prediction, which is particularly useful for the protein sequences that do not have close homology templates in the Protein Data Bank.

Results

We developed a new method, ResPRE, to predict residue-level protein contacts using inverse covariance matrix (or precision matrix) of multiple sequence alignments (MSAs) through deep residual convolutional neural network training. The approach was tested on a set of 158 non-homologous proteins collected from the CASP experiments and achieved an average accuracy of 50.6% in the top-L long-range contact prediction with L being the sequence length, which is 11.7% hi

SUBMITTER: Li Y 

PROVIDER: S-EPMC6853658 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature