Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments.
about
Prodepth: predict residue depth by support vector regression approach from protein sequences onlyProgress and challenges in protein structure predictionLow-homology protein threadingI-TASSER: a unified platform for automated protein structure and function predictionImproving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templatesMUSTER: Improving protein sequence profile-profile alignments by using multiple sources of structure informationLOMETS: a local meta-threading-server for protein structure predictionWeb-based toolkits for topology prediction of transmembrane helical proteins, fold recognition, structure and binding scoring, folding-kinetics analysis and comparative analysis of domain combinationsAutomated protein structure modeling in CASP9 by I-TASSER pipeline combined with QUARK-based ab initio folding and FG-MD-based structure refinementeMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein modelsComparative protein structure modeling using ModellerComputational protein design: validation and possible relevance as a tool for homology searching and fold recognitionTransmembrane protein alignment and fold recognition based on predicted topologyeVolver: an optimization engine for evolving protein sequences to stabilize the respective structures.Protein depth calculation and the use for improving accuracy of protein fold recognitionA machine learning information retrieval approach to protein fold recognition.A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction.Mass spectrometry coupled experiments and protein structure modeling methods.Novel knowledge-based mean force potential at the profile levelImproving the quality of protein structure models by selecting from alignment alternativesBioShell-Threading: versatile Monte Carlo package for protein 3D threading.Ab initio protein structure prediction using chunk-TASSERPcons.net: protein structure prediction meta server.Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profilesApplication of amino acid occurrence for discriminating different folding types of globular proteins.Comparative Protein Structure Modeling Using MODELLERReal-value prediction of backbone torsion angles.Improving protein fold recognition by random forest.A multi-template combination algorithm for protein comparative modeling.Benchmarking of TASSER_2.0: an improved protein structure prediction algorithm with more accurate predicted contact restraintsSP5: improving protein fold recognition by using torsion angle profiles and profile-based gap penalty modelMultiple sequence alignment by conformational space annealing.Protein structure prediction by pro-Sp3-TASSER.Improving consensus contact prediction via server correlation reduction@TOME-2: a new pipeline for comparative modeling of protein-ligand complexes.PoLi: A Virtual Screening Pipeline Based on Template Pocket and Ligand Similarity.Performance of the Pro-sp3-TASSER server in CASP8A large-scale conformation sampling and evaluation server for protein tertiary structure prediction and its assessment in CASP11.I-TASSER: fully automated protein structure prediction in CASP8Predicting local quality of a sequence-structure alignment.
P2860
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P2860
Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments.
description
2005 nî lūn-bûn
@nan
2005 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2005 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2005年の論文
@ja
2005年論文
@yue
2005年論文
@zh-hant
2005年論文
@zh-hk
2005年論文
@zh-mo
2005年論文
@zh-tw
2005年论文
@wuu
name
Fold recognition by combining ...... ctural alignment of fragments.
@ast
Fold recognition by combining ...... ctural alignment of fragments.
@en
type
label
Fold recognition by combining ...... ctural alignment of fragments.
@ast
Fold recognition by combining ...... ctural alignment of fragments.
@en
prefLabel
Fold recognition by combining ...... ctural alignment of fragments.
@ast
Fold recognition by combining ...... ctural alignment of fragments.
@en
P2860
P356
P1433
P1476
Fold recognition by combining ...... ctural alignment of fragments.
@en
P2093
Hongyi Zhou
Yaoqi Zhou
P2860
P304
P356
10.1002/PROT.20308
P407
P577
2005-02-01T00:00:00Z