Ensembling multiple raw coevolutionary features with deep residual neural networks for contact-map prediction in CASP13.

ABSTRACT: We report the results of residue-residue contact prediction of a new pipeline built purely on the learning of coevolutionary features in the CASP13 experiment. For a query sequence, the pipeline starts with the collection of multiple sequence alignments (MSAs) from multiple genome and metagenome sequence databases using two complementary Hidden Markov Model (HMM)-based searching tools. Three profile matrices, built on covariance, precision, and pseudolikelihood maximization respectively, are then created from the MSAs, which are used as the input features of a deep residual convolutional neural network architecture for contact-map training and prediction. Two ensembling strategies have been proposed to integrate the matrix features through end-to-end training and stacking, resulting in two

SUBMITTER: Li Y 

PROVIDER: S-EPMC6851483 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature