Predicting protein structure using hidden Markov models.
about
Using 3D Hidden Markov Models that explicitly represent spatial coordinates to model and compare protein structuresBCL::Fold--de novo prediction of complex and large protein topologies by assembly of secondary structure elementsTy3 capsid mutations reveal early and late functions of the amino-terminal domainAn intron-containing glycoside hydrolase family 9 cellulase gene encodes the dominant 90 kDa component of the cellulosome of the anaerobic fungus Piromyces sp. strain E2PANTHER: a library of protein families and subfamilies indexed by functionImproved recognition of native-like protein structures using a family of designed sequencesThe CATH database: an extended protein family resource for structural and functional genomicsGenomic biodiversity, phylogenetics and coevolution in proteinsDe novo high-resolution protein structure determination from sparse spin-labeling EPR data.PREDICT-2ND: a tool for generalized protein local structure prediction.Hybrid approaches: applying computational methods in cryo-electron microscopyEM-fold: De novo folding of alpha-helical proteins guided by intermediate-resolution electron microscopy density maps.RosettaEPR: an integrated tool for protein structure determination from sparse EPR data.A DIRICHLET PROCESS MIXTURE OF HIDDEN MARKOV MODELS FOR PROTEIN STRUCTURE PREDICTIONCONTSOR--a new knowledge-based fold recognition potential, based on side chain orientation and contacts between residue terminal groups.Finding weak similarities between proteins by sequence profile comparison.A comparison of position-specific score matrices based on sequence and structure alignments.A homology identification method that combines protein sequence and structure information.Precise determination of the diversity of a combinatorial antibody library gives insight into the human immunoglobulin repertoire.GIGA: a simple, efficient algorithm for gene tree inference in the genomic age.PhyloFacts: an online structural phylogenomic encyclopedia for protein functional and structural classification.Evolutionary bidirectional expansion for the tracing of alpha helices in cryo-electron microscopy reconstructions.Fold-specific sequence scoring improves protein sequence matching.Use of residue pairs in protein sequence-sequence and sequence-structure alignments.Recent applications of Hidden Markov Models in computational biology.Wurst: a protein threading server with a structural scoring function, sequence profiles and optimized substitution matricesFrom endonucleases to transcription factors: evolution of the AP2 DNA binding domain in plants.
P2860
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P2860
Predicting protein structure using hidden Markov models.
description
1997 nî lūn-bûn
@nan
1997 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
1997 թվականի հունվարին հրատարակված գիտական հոդված
@hy
1997年の論文
@ja
1997年論文
@yue
1997年論文
@zh-hant
1997年論文
@zh-hk
1997年論文
@zh-mo
1997年論文
@zh-tw
1997年论文
@wuu
name
Predicting protein structure using hidden Markov models.
@ast
Predicting protein structure using hidden Markov models.
@en
type
label
Predicting protein structure using hidden Markov models.
@ast
Predicting protein structure using hidden Markov models.
@en
prefLabel
Predicting protein structure using hidden Markov models.
@ast
Predicting protein structure using hidden Markov models.
@en
P2093
P1433
P1476
Predicting protein structure using hidden Markov models.
@en
P2093
Haussler D
Sjölander K
P304
P356
10.1002/(SICI)1097-0134(1997)1+<134::AID-PROT18>3.3.CO;2-Q
P407
P478
P577
1997-01-01T00:00:00Z