Study of real-valued distance prediction for protein structure prediction with deep learning.

ABSTRACT:

Motivation

Inter-residue distance prediction by convolutional residual neural network (deep ResNet) has greatly advanced protein structure prediction. Currently, the most successful structure prediction methods predict distance by discretizing it into dozens of bins. Here, we study how well real-valued distance can be predicted and how useful it is for 3D structure modeling by comparing it with discrete-valued prediction based upon the same deep ResNet.

Results

Different from the recent methods that predict only a single real value for the distance of an atom pair, we predict both the mean and standard deviation of a distance and then fold a protein by the predicted mean and deviation. Our findings include: (i) tested on the CASP13 FM (free-modeling) targets, our real-valued

SUBMITTER: Li J 

PROVIDER: S-EPMC8504618 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature