research data . Dataset . 2017

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Wang, Sheng; Sun, Siqi; Li, Zhen; Zhang, Renyu; Xu, Jinbo;
Open Access
  • Published: 01 Jan 2017
  • Publisher: Figshare
Abstract
<div><p>Motivation</p><p>Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction.</p><p>Method</p><p>This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residua...
Subjects
free text keywords: Biophysics, Biochemistry, Genetics, Molecular Biology, Biotechnology, 110309 Infectious Diseases, 69999 Biological Sciences not elsewhere classified, 80699 Information Systems not elsewhere classified, CASP 11 winner MetaPSICOV, 579 test proteins, representative EC method CCMpred, 398 membrane proteins, membrane proteins, 0.3 L -2.3L, contact prediction, sequence homologs, contact occurrence patterns, Accurate De Novo Prediction, Protein Contact Map, F 1 score, 105 CASP 11 targets, 3 D models, convolutional transformation, residue, fully-automated web server, sequence conservation information, CAMEO, Ultra-Deep Learning Model Motivation Protein contacts
Download from
figshare
Dataset . 2017
Provider: figshare
Any information missing or wrong?Report an Issue