CONFOLD: Residue-residue contact-guided ab initio protein folding.
about
DeepQA: improving the estimation of single protein model quality with deep belief networksAccurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning ModelAn Improved Integration of Template-Based and Template-Free Protein Structure Modeling Methods and its Assessment in CASP11Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction.Protein single-model quality assessment by feature-based probability density functions.Applying PyRosetta molecular energies to separate properly oriented protein models from mirror models, obtained from contact maps.Protein Residue Contacts and Prediction MethodsResidue contacts predicted by evolutionary covariance extend the application of ab initio molecular replacement to larger and more challenging protein folds.Recent advances in sequence-based protein structure prediction.Assessing Predicted Contacts for Building Protein Three-Dimensional Models.ConEVA: a toolbox for comprehensive assessment of protein contacts.A deep learning framework for improving long-range residue-residue contact prediction using a hierarchical strategy.Applications of contact predictions to structural biologyEPSILON-CP: using deep learning to combine information from multiple sources for protein contact prediction.CoinFold: a web server for protein contact prediction and contact-assisted protein foldingXSuLT: a web server for structural annotation and representation of sequence-structure alignments.Improved protein structure reconstruction using secondary structures, contacts at higher distance thresholds, and non-contacts.Predicting accurate contacts in thousands of Pfam domain families using PconsC3.Improving Protein Fold Recognition by Extracting Fold-specific Features from Predicted Residue-residue Contacts.DNCON2: Improved protein contact prediction using two-level deep convolutional neural networks.Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning.Large-scale structure prediction by improved contact predictions and model quality assessment.Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks.CONFOLD2: improved contact-driven ab initio protein structure modeling.Folding Membrane Proteins by Deep Transfer Learning.Identification of residue pairing in interacting β-strands from a predicted residue contact map.Ordering Protein Contact Matrices.Automated method to differentiate between native and mirror protein models obtained from contact maps.MISTIC2: comprehensive server to study coevolution in protein families.
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P2860
CONFOLD: Residue-residue contact-guided ab initio protein folding.
description
2015 nî lūn-bûn
@nan
2015 թուականի Յունիսին հրատարակուած գիտական յօդուած
@hyw
2015 թվականի հունիսին հրատարակված գիտական հոդված
@hy
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
name
CONFOLD: Residue-residue contact-guided ab initio protein folding.
@ast
CONFOLD: Residue-residue contact-guided ab initio protein folding.
@en
type
label
CONFOLD: Residue-residue contact-guided ab initio protein folding.
@ast
CONFOLD: Residue-residue contact-guided ab initio protein folding.
@en
prefLabel
CONFOLD: Residue-residue contact-guided ab initio protein folding.
@ast
CONFOLD: Residue-residue contact-guided ab initio protein folding.
@en
P2093
P2860
P356
P1433
P1476
CONFOLD: Residue-residue contact-guided ab initio protein folding.
@en
P2093
Badri Adhikari
Debswapna Bhattacharya
Renzhi Cao
P2860
P304
P356
10.1002/PROT.24829
P407
P577
2015-06-06T00:00:00Z