ライフサイエンスコーパス: structure prediction

1 tact prediction and contact-assisted de novo structure prediction.

2 contiguous in sequence-a major bottleneck in structure prediction.

3 imilar category in both contact and tertiary structure prediction.

4 and methods that provide both alignment and structure prediction.

5 lopment of accurate physics-based models for structure prediction.

6 s 77 to 83) were generated using comparative structure prediction.

7 them are the two major challenges of protein structure prediction.

8 l can be used as constraints in biomolecular structure prediction.

9 rimary sequence can be used to guide protein structure prediction.

10 eir successful use in de novo protein native structure prediction.

11 low quality and not very useful for de novo structure prediction.

12 before conducting any fragment-based protein structure prediction.

13 ve the reliability and robustness of protein structure prediction.

14 roups based on sequence analysis and protein structure prediction.

15 e machine learning algorithms for 3D protein structure prediction.

16 orphs is a fundamental bottleneck in crystal structure prediction.

17 of competing polymorphs in molecular crystal structure prediction.

18 erization of adjacent peaks leading to their structure prediction.

19 lidated in the context of protein design and structure prediction.

20 ithin loops of a different length to improve structure prediction.

21 eatly improves the accuracy of RNA secondary structure prediction.

22 ision of genome-wide scans for consensus RNA structure prediction.

23 , is the most reliable method for protein 3D structure prediction.

24 st reliable method for protein 3-dimensional structure prediction.

25 ur approach a promising method for shape and structure prediction.

26 rstand the sources of error in contact-based structure prediction.

27 and contains key information for protein 3D structure prediction.

28 theophylline riboswitches based on secondary structure prediction.

29 loop regions, can improve the performance of structure prediction.

30 ng short of the level required for ab initio structure prediction.

31 and performance assessment for benchmarks of structure prediction.

32 ide structural constraints for RNA secondary structure prediction.

33 conformations, a major bottleneck for RNA 3D structure prediction.

34 n belong to the current challenges in RNA 3D structure prediction.

35 (QA) plays a very important role in protein structure prediction.

36 II from ChR2, is used as a template for ChR2 structure prediction.

37 re key features for accurate de novo protein structure prediction.

38 c energy minimization were used in secondary structure prediction.

39 r study in order to improve the precision of structure prediction.

40 ce the success rate of the ab initio protein structure prediction.

41 single model quality assessment and protein structure prediction.

42 ure range up to 800 GPa through evolutionary structure prediction.

43 quality assessment and is useful for protein structure prediction.

44 ge-based energy function for de novo protein structure prediction.

45 of the major challenges for protein tertiary structure prediction.

46 cular RNA structures was assembled to assess structure prediction.

47 contact prediction dictates the accuracy of structure prediction.

48 (QA) has played an important role in protein structure prediction.

49 s domain (CD) and discontinuous domain (DCD) structure predictions.

50 n be used as an intermediary step in protein structure predictions.

51 These results often rely on RNA secondary structure predictions.

52 o be important for making accurate secondary structure predictions.

53 nergy change constraints to direct secondary structure predictions.

54 s to quantify uncertainty in alignment-based structure predictions.

55 ased solvation, torsion angle, and secondary structure predictions.

56 Surprisingly, both deviate from crystal structure predictions.

57 s an efficient way for accurate RNA tertiary structure predictions.

58 , as it requires making two, albeit related, structure predictions.

59 bining recent structural studies and de novo structure predictions.

60 TurboFold II also has comparable structure prediction accuracy as the original TurboFold

61 alpha-helix, beta-strand and coil) secondary structure prediction accuracy of 82.0% while solvent acc

62 e SHAPE mapping data for a sequence improves structure prediction accuracy of other homologous sequen

63 oth learning paradigms and that search-based structured prediction achieves better recall at all prec

64 An RNA folding/RNA secondary structure prediction algorithm determines the non-nested

65 Using the variable-composition structure prediction algorithm USPEX, in addition to the

66 Employing a tandem protein structure prediction algorithmic and molecular dynamics

67 dition, our benchmark indicates that general structure prediction algorithms (e.g. RNAfold and RNAstr

68 benchmarking data obtained from two refined structure prediction algorithms, RNAz and SISSIz, were t

69 of RNA sequences against which to benchmark structure prediction algorithms.

70 arable with the most accurate template-based structure prediction algorithms.

71 l NMR experimental data with Rosetta de novo structure prediction algorithms.

72 Pseudoknots are usually excluded from RNA structure prediction algorithms.

73 Using predicted shapes in structure prediction allows us to achieve approximate 29

74 s in the latest CASP (Critical Assessment of Structure Prediction), although it was not fully impleme

75 nce alignment with known GKAPs and secondary structure prediction analysis, we defined a small sequen

76 NA 3D structure, with applications in RNA 3D structure prediction and analysis of RNA sequence evolut

77 ture is a software package for RNA secondary structure prediction and analysis.

78 nges of RNA secondary structures are used in structure prediction and analysis.

79 ble to the important problems of protein 3-D structure prediction and association of gene-gene networ

80 C and type IV collagen, confirmed by protein structure prediction and co-immunoprecipitation.

81 ibodies is critically important for antibody structure prediction and computational design.

82 We review advances and challenges in protein structure prediction and de novo protein design, and hig

83 de a good foundation for further work on RNA structure prediction and design applications.

84 a new undergraduate program in biomolecular structure prediction and design in which students conduc

85 pansions to the ROSETTA platform that enable structure prediction and design of five non-peptidic oli

86 cleic acid (RNA) molecules, for example, for structure prediction and design, as they simplify the RN

87 l features can similarly be utilized for RNA structure prediction and design.

88 acterial Tat signal peptides using secondary structure prediction and docking algorithms suggest that

89 g a cell-based reporter assay, computational structure prediction and energetic analysis, fluorescenc

90 By structure prediction and experimental analysis, we also

91 tested and rapidly evolving tools for the 3D structure prediction and high-resolution design of prote

92 mputing have enabled current protein and RNA structure prediction and molecular simulation methods to

93 ign is also applied to template-based glycan structure prediction and monosaccharide substitution mat

94 crystallographic structure determination and structure prediction and optimization has begun to be ex

95 de it possible to develop powerful tools for structure prediction and optimization in the absence of

96 ms will speed-up many tasks, such as protein structure prediction and orthology mapping, which rely h

97 contact prediction is important for protein structure prediction and other applications.

98 Through computational crystal-structure prediction and powder X-ray diffraction method

99 Here we combine computational crystal structure prediction and property prediction to build en

100 ergy minimization, maximum expected accuracy structure prediction and pseudoknot prediction.

101 usefulness of predicted HSEalpha for protein structure prediction and refinement as well as function

102 ages and describe how methods from ab initio structure prediction and refinement in Rosetta have been

103 Genome-wide protein structure prediction and structure-based function annota

104 However, these approaches depend on structure predictions and have limited accuracy, arguabl

105 g solid-state (1)H NMR spectroscopy, crystal structure prediction, and density functional theory chem

106 esidue-residue contact prediction, secondary structure prediction, and fold recognition.

107 proach that combines structure solution with structure prediction, and inspires the targeted synthesi

108 meters with the greatest impact on secondary structure prediction, and the subset which should be pri

109 Many computational methods for RNA secondary structure prediction, and, in particular, for the predic

110 were assessed: (i) single sequence secondary structure predictions, and (ii) modulation of gap costs

111 and template-based modeling for genome-wide structure prediction applied to the Escherichia coli gen

112 The protein structure prediction approaches can be categorized into

113 ular dynamics simulations, and crowd-sourced structure-prediction approaches, however, computational

114 A(Phe) and the adenine riboswitch, secondary structure predictions are nearly perfect if no experimen

115 families to become accessible to accurate 3D structure prediction as the number of available sequence

116 Secondary structure predictions as well as mature miRNA and target

117 predicted contacts allow all-atom blinded 3D structure prediction at good accuracy for several known

118 State of the art variable composition structure prediction based on density functional theory

119 lly automated pipeline for ab initio protein structure prediction based on evolutionary information.

120 Computer-based structure prediction becomes a major tool to provide lar

121 Structure prediction by density functional theory sugges

122 asks, protein homology detection and protein structure prediction, by querying all 8332 PDB40 protein

123 ork has shown that the accuracy of ab initio structure prediction can be significantly improved by in

124 show how rational design based on secondary structure predictions can also direct the use of AEGIS t

125 TurboFold II augments the structure prediction capabilities of TurboFold by additi

126 from the 11th Critical Assessment of Protein Structure Prediction (CASP) experiment and on 15 difficu

127 dels from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicl

128 ritical Assessment of Techniques for Protein Structure Prediction (CASP).

129 ritical Assessment of Techniques for Protein Structure Prediction (CASP10).

130 ritical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM group.

131 ritical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM-NOVEL server.

132 ritical Assessment of Techniques for Protein Structure Prediction (CASP11) in 2014.

133 In the Critical Assessment of protein Structure Prediction (CASP11), the FALCON@home-based pre

134 ritical Assessment of Techniques for Protein Structure Prediction (CASP11).

135 ritical assessment of techniques for protein structure prediction (CASP7) and it was also shown to pr

136 our group's performance in the main tertiary structure prediction category.

137 nce of weak intermolecular forces that makes structure prediction challenging.

138 snoReport uses RNA secondary structure prediction combined with machine learning as t

139 e therefore instrumental to membrane protein structure prediction, consequently increasing our unders

140 Ribonucleic acid (RNA) secondary structure prediction continues to be a significant chall

141 Currently, organic crystal structure prediction (CSP) methods are based on searchin

142 n be used as an intermediary step in protein structure predictions either on its own or complemented

143 The workhorses of the RNA secondary structure prediction engine are recursions first describ

144 This insight may aid structure prediction, engineering, and design of membran

145 Beyond 3D structure prediction, evolutionary couplings help identi

146 a length up to 250 residues from the CASP11 structure prediction exercise.

147 sequence and structure databases from recent structure prediction experiments.

148 ritical Assessment of Techniques for Protein Structure Prediction) experiments, as well as being cont

149 RACER achieves accurate native structure prediction for a number of RNAs (average RMSD

150 architecture of chromosomes and that de novo structure prediction for whole genomes may be increasing

151 also provide in silico and in vivo secondary structure predictions for comparison, visualized in the

152 ecific features, which include RNA secondary structure prediction from multiple alignments using eith

153 ategy for constraint satisfaction, including structure prediction from predicted pairwise distances.

154 interacting amino-acid residues for protein structure prediction from sequence alignments; and disti

155 a general approach to the problem of RNA 3D structure prediction from sequence.

156 identified by the alignment analysis and 2nd structure prediction from the selected, cloned sequences

157 We further leverage the structure predictions generated by our algorithm to faci

158 r these RNAs, we achieved highly significant structure predictions given the inputs of RNA sequence a

159 integrated pipeline of methods for: tertiary structure prediction, global and local 3D model quality

160 We show that Rosetta structure prediction guided by residue-residue contacts

161 es are not available, fragment-based protein structure prediction has become the approach of choice.

162 Additionally, chemical structure prediction has been improved by incorporating

163 Ab structure prediction has made great strides, but accurat

164 e distance maps and applying them in protein structure predictions has been relatively unexplored in

165 d into an online resource, the Hemagglutinin Structure Prediction (HASP) server.

166 Secondary structure prediction identified three disordered loops i

167 alization and reactivity derivation, and RNA structure prediction in a single user-friendly web inter

168 By implementing RNA secondary structure prediction in a statistical alignment framewor

169 gous ncRNAs and apply the predicted shape to structure prediction including pseudoknots.

170 The web server offers RNA secondary structure prediction, including free energy minimization

171 pular computational method for RNA consensus structure prediction, incorporates covarying mutations i

172 Moreover, secondary structure predictions increase in accuracy as more seque

173 Computational RNA structure prediction is a mature important problem that

174 Bimolecular secondary structure prediction is also provided.

175 Secondary structure prediction is an important problem in RNA bioi

176 ly uncharacterized and their high-resolution structure prediction is currently hindered by the lack o

177 eld of experimentally directed RNA secondary structure prediction is entering a phase focused on the

178 e time and cost consuming, in silico protein structure prediction is essential to produce conformatio

179 tionally is no rescue, since single sequence structure prediction is highly unreliable.

180 A current challenge in RNA structure prediction is the description of global helica

181 A key aspect of RNA secondary structure prediction is the identification of novel func

182 The first step toward high-accuracy structure prediction is to pick an energy model that is

183 We discuss various modeling approaches for structure prediction, mechanistic analysis of RNA reacti

184 ge 0 approximately 300 GPa using an unbiased structure prediction method based on evolutionary algori

185 A new de novo protein structure prediction method for transmembrane proteins (

186 Here, we report a new RNA secondary structure prediction method, restrained MaxExpect (RME),

187 Theoretical structure prediction methods are an attractive alternati

188 odynamic model refinements and alternate RNA structure prediction methods beyond the physics-based on

189 s in the recent CASP11 blind test of protein structure prediction methods by incorporating residue-re

190 Understanding how RNA secondary structure prediction methods depend on the underlying ne

191 Traditional protein structure prediction methods generally use one or a few

192 methods and contact-guided ab initio protein structure prediction methods have highlighted the import

193 Protein tertiary structure prediction methods have matured in recent year

194 Traditional template-based protein structure prediction methods tend to focus on identifyin

195 s domains using an ensemble of two secondary structure prediction methods to guide fragment selection

196 porates this information into current RNA 3D structure prediction methods, specifically 3dRNA.

197 and the CASP-winning template-based protein structure prediction methods.

198 oses significant challenge for computational structure prediction methods.

199 potentials and increase the power of protein structure prediction methods.

200 es the recent progress and challenges in RNA structure prediction methods.

201 corporating contact information into protein structure prediction methods.

202 s have important implications for quaternary structure prediction, modeling, and engineering.

203 Our results suggest that structure prediction/modeling of N-glycans of glycoconju

204 In this article, we review RNA structure prediction models and models for ion-RNA and l

205 used to constrain and improve RNA secondary structure prediction models.

206 In protein structure prediction, multiple loops often need to be mo

207 Finally, tertiary structure prediction of E4 34K and its comparison with t

208 Encouraged by successful de novo 3D structure prediction of globular and alpha-helical membr

209 ve sequence analysis algorithm for secondary structure prediction of multiple homologs, whereby the m

210 aid in interpretation of experiment, and for structure prediction of natively and nonnatively unfolde

211 rimentally determined structures transformed structure prediction of proteins and protein complexes.

212 structures, useful for homology modeling and structure prediction of receptors.

213 Structure prediction of stable and metastable phases is

214 RNA secondary structure prediction of the 383-base 5' untranslated reg

215 Secondary structure prediction of the N terminus of the gamma subu

216 to our functional results, the computational structure prediction of the Q239-D258 fragment confirmed

217 reactivity allow us to improve thermodynamic structure predictions of riboSNitches.

218 We first describe de novo blind structure predictions of unprecendented accuracy we made

219 Despite large differences in model structure, predictions of sagebrush response to climate

220 computational approaches for de novo protein structure prediction often randomly sample protein confo

221 a, a state-of-the-art fragment-based protein structure prediction package, we evaluated our proposed

222 structural class annotations, enhancement of structure prediction performance is highly significant i

223 restraints in RME, we compared its secondary structure prediction performance with two other well-kno

224 with lower probing efficiency, the secondary structure prediction performances of the tested tools we

225 methods are applied as input to higher-level structure prediction pipelines, even small errors may ha

226 predicted models, is however missed in most structure prediction pipelines.

227 nd function and as a component of protein 3D structure prediction pipelines.

228 Template-based protein structure prediction plays an important role in Function

229 luding lattice energies, structures, crystal structure prediction, polymorphism, phase diagrams, vibr

230 Besides, the proteome-wide structure prediction poses another challenge of increasi

231 lation between parameter usage and impact on structure prediction precision.

232 ides a promising direction in addressing the structure prediction problem, especially when targeting

233 a method to tackle a key step in the RNA 3D structure prediction problem, the prediction of the nucl

234 important subproblem of the broader protein structure prediction problem.

235 ion approach in conjunction with the Rosetta structure prediction program to construct a structural m

236 idue co-evolution information in the Rosetta structure prediction program.

237 Secondary structure prediction programs predicted a short beta-str

238 In addition, secondary structure prediction programs predicted an alpha-helical

239 a community-wide, blind assessment of RNA 3D structure prediction programs to determine the capabilit

240 chers working on modeling of macromolecules, structure prediction, properties of polymers, entangleme

241 applications in homology modelling, protein structure prediction, protein design (e.g. enzyme design

242 in protein sequence database search, protein structure prediction, protein function prediction, and p

243 is report have immediate applications for 3D structure prediction, protein model assessment, and prot

244 Our structure prediction protocol RAGTOP (RNA-As-Graphs Topo

245 Here, we present a structure prediction protocol that combines evolutionary

246 there is also a substantial need to develop structure prediction protocols tailored to the type of r

247 ve to inspire new protein design and protein structure prediction protocols.

248 Accurate secondary structure prediction provides important information to u

249 s which can contribute to poor RNA secondary structure prediction quality.

250 erable progress in the past decades, protein structure prediction remains one of the major unsolved p

251 Leading structure prediction resources (DomSerf, FUGUE, Gene3D,

252 sed on an evolutionary algorithm for crystal structure prediction revealed that Forms I and II are am

253 RNA secondary structure prediction revealed that the FR region of both

254 study, we presented FALCON@home as a protein structure prediction server focusing on remote homologue

255 is particularly useful for automated protein structure prediction servers.

256 The method was implemented as two protein structure prediction servers: MULTICOM-CONSTRUCT and MUL

257 ve important implications in de novo protein structure prediction since fragment-based methods are on

258 Structure prediction software verified that the AtRBSK s

259 derived for use in free energy and secondary structure prediction software.

260 Protein secondary structure prediction (SSP) has been an area of intense r

261 ch substructuring could be useful for RNA 3D structure prediction, structure/function inference and i

262 cores relating to the quality of a secondary structure prediction, such as information entropy values

263 Sequence comparison and structure prediction suggest that ZCD is an N-truncated

264 Structure predictions suggest that CLASPs have at least

265 Sequence-based structure predictions suggest that the thiol groups pres

266 ackage for performing supervised learning in structured prediction tasks.

267 djacencies can be included as constraints in structure prediction techniques to predict high-resoluti

268 Our method makes much more accurate RNA 3D structure prediction than the original 3dRNA as well as

269 six small RNAs, yielded poorer RNA secondary structure predictions than expected on the basis of prio

270 Starting from low-resolution protein structure predictions, the methods successfully recogniz

271 According to sequence alignment and protein structure predictions, the putative catalytic site of SM

272 n using sequence data for quaternary protein structure prediction; they require, however, large joint

273 Given the fast progress in protein structure prediction, this work explores the possibility

274 xperiments of critical assessment of protein structure prediction to compare predicted models with ex

275 it of experimental mapping data in secondary structure prediction to homologous sequences.

276 erformance has been benchmarked by comparing structure predictions to reference secondary structures.

277 ructural fold is the starting point for many structure prediction tools and protein function inferenc

278 single-stranded RNAs, which use generic RNA structure prediction tools and thus can be universally a

279 In recent years, a set of diverse RNA structure prediction tools have become available, which

280 RNA structure prediction tools such as PPfold or RNAalifold

281 However, most secondary structure prediction tools that incorporate probing data

282 We employed de novo RNA structure prediction tools to screen intronic sequences

283 results indicate that optimization of RNA 3D structure prediction using evolutionary restraints of nu

284 In de novo protein structure prediction using FRAGFOLD, MetaPSICOV is able

285 he utility of predicted contacts for protein structure prediction using large and representative sequ

286 s and underestimates confidence in secondary structure prediction using SHAPE data.

287 GPa using variable-composition evolutionary structure predictions using the USPEX code.

288 d/or ribonucleases to restrain RNA secondary structure prediction via the RNAstructure and ViennaRNA

289 from the most recent Critical Assessment of Structure Prediction, we demonstrate that FGRAGSION prov

290 As in protein structure prediction, we use maximum entropy global prob

291 Structure similarity and secondary structure prediction were combined underlying localized

292 onfidence levels of SHAPE-directed secondary structure prediction were significantly higher than thos

293 ess TurboFold II, its sequence alignment and structure predictions were compared with leading tools,

294 s a reverse procedure of protein folding and structure prediction, where constructing structures from

295 ts secondary and tertiary structures through structure prediction, which is used to create models for

296 A typical structure prediction will be returned between 30 min and

297 ics-based coarse-grained approach to protein-structure prediction will eventually reach global predic

298 ed that RME substantially improved secondary structure prediction with perfect restraints (base pair

299 These maps fuse crystal-structure prediction with the computation of physical pr

300 he transmembrane helices, these two types of structure predictions yield roughly equivalent quality s