Open AccessStudy of real-valued distance prediction for protein structure prediction with deep learning
Author(s) -
Jin Li,
Jinbo Xu
Publication year - 2021
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab333
Subject(s) - residual , computer science , convolutional neural network , artificial intelligence , protein structure prediction , deep learning , range (aeronautics) , standard deviation , mean squared prediction error , algorithm , pattern recognition (psychology) , artificial neural network , data mining , mathematics , statistics , protein structure , physics , materials science , nuclear magnetic resonance , composite material
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.