Analysis of several key factors influencing deep learning-based inter-residue contact prediction.

ABSTRACT:

Motivation

Deep learning has become the dominant technology for protein contact prediction. However, the factors that affect the performance of deep learning in contact prediction have not been systematically investigated.

Results

We analyzed the results of our three deep learning-based contact prediction methods (MULTICOM-CLUSTER, MULTICOM-CONSTRUCT and MULTICOM-NOVEL) in the CASP13 experiment and identified several key factors [i.e. deep learning technique, multiple sequence alignment (MSA), distance distribution prediction and domain-based contact integration] that influenced the contact prediction accuracy. We compared our convolutional neural network (CNN)-based contact prediction methods with three coevolution-based methods on 75 CASP13 targets consisting of 108 domain

SUBMITTER: Wu T 

PROVIDER: S-EPMC7703788 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature