ライフサイエンスコーパス: structure prediction

1 ure determination) or computational methods (structure prediction).

2 available for both 3- and 8-state secondary structure prediction.

3 ccording to their importance to RNA tertiary structure prediction.

4 and methods that provide both alignment and structure prediction.

5 for decoy selection in template-free protein structure prediction.

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

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

8 represents a considerable advance in protein-structure prediction.

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

10 theophylline riboswitches based on secondary structure prediction.

11 ide structural constraints for RNA secondary structure prediction.

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

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

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

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

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

17 c energy minimization were used in secondary structure prediction.

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

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

20 single model quality assessment and protein structure prediction.

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

22 quality assessment and is useful for protein structure prediction.

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

24 of the major challenges for protein tertiary structure prediction.

25 cular RNA structures was assembled to assess structure prediction.

26 contact prediction dictates the accuracy of structure prediction.

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

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

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

30 imilar category in both contact and tertiary structure prediction.

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

32 e refinement is an important step of protein structure prediction.

33 tical step forward in template-based protein structure prediction.

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

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

36 mprove computational protein-protein complex structure prediction.

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

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

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

40 improves the predictive power of pure AWSEM structure prediction.

41 before conducting any fragment-based protein structure prediction.

42 ve the reliability and robustness of protein structure prediction.

43 ng superhighway of deep learning and protein structure prediction.

44 ing that has recently revolutionized protein structure prediction.

45 insertion process of fragment-based protein structure prediction.

46 ty estimation, in many ways, informs protein structure prediction.

47 sequence are crucial subproblems in protein structure prediction.

48 bone torsion angles prediction and secondary structure prediction.

49 nt (QA) is an essential procedure in protein structure prediction.

50 ional methods is a powerful tool for protein structure prediction.

51 quence alignment, phylogenetic analysis, and structure prediction.

52 remains the most accurate method for protein structure prediction.

53 t of an RNA family for the success of RNA 3D structure prediction.

54 ints to improve the accuracy of RNA tertiary structure prediction.

55 appear to have lower accuracy for secondary structure prediction.

56 ng of RNA homologs can improve ab initio RNA structure prediction.

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

58 bining recent structural studies and de novo structure predictions.

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

60 nd these pairwise couplings have improved 3D structure predictions.

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

62 In the recent Critical Assessment of Protein Structure Prediction(5) (CASP13)-a blind assessment of t

63 ing algorithms drive the progress in protein structure prediction, a lot remains to be studied at thi

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

65 netic traps, the greatest improvement in the structure prediction accuracy is achieved when we utiliz

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

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

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

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

70 Employing a tandem protein structure prediction algorithmic and molecular dynamics

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

72 To link secondary structure prediction algorithms developed for folded pro

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

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

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

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

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

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

81 We launch a webserver for RNA structure prediction and design corresponding to tools d

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

83 od advances high-resolution membrane protein structure prediction and design toward tackling key biol

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

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

86 to bioinformatics problems including protein structure prediction and design.

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

88 tionship is likely to be relevant to protein structure prediction and functional analysis of protein

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

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

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

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

93 ing (PSCP) is a critical step toward protein structure prediction and protein design.

94 lemented in Rosetta and is suitable for both structure prediction and protein design.

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

96 ic resonance spectroscopy to improve protein structure prediction and refinement outcomes.

97 of the proposed pipeline lies in the uniform structure prediction and refinement protocol, as well as

98 Finally, DeepMSA was used for secondary structure prediction and resulted in statistically signi

99 ne, which starts with low-resolution protein structure prediction and structure-based binding-site id

100 d on candidate precursors through to crystal structure prediction and synthesis using robotic screeni

101 new construct has the potential for both RNA structure prediction and the inverse folding problem.

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

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

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

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

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

107 ucture and the 3D templates, we develop a 3D structure prediction approach.

108 The protein structure prediction approaches can be categorized into

109 Protein folding and structure prediction are two sides of the same coin.

110 the accuracies for 3- and 8-state secondary structure predictions are 87.72 and 77.15%, respectively

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

112 Secondary structure predictions as well as mature miRNA and target

113 utational tools as well as a standard RNA 3D structure prediction assessment protocol for the communi

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

115 e-residue level (e.g., for assessing protein structure predictions at atomistic level).

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

117 t available, usage of fragment-based protein structure prediction becomes the only practical alternat

118 asingly guided by quantum-mechanical crystal-structure prediction, but the structural complexity in b

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

120 investigates whether the accuracy of protein structure prediction can be improved using a loop-specif

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

122 Protein structure prediction can be used to determine the three-

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

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

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

126 two years the Critical Assessment of protein Structure Prediction (CASP) experiments are held to asse

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 (CASP11) as MULTICOM group.

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

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

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

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

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

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

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

137 Chemical shift-based secondary structure prediction confirms that in solution leptin fo

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

139 mbine state-of-the-art computational crystal structure prediction (CSP) techniques with a wide range

140 We use crystal structure prediction (CSP) to understand the underlying

141 the-art tools when computing their secondary structure prediction do not explicitly leverage the vast

142 The accuracy of chemical structure prediction enables the development of machine-

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 antly improve the accuracy of template-based structure prediction, especially for distant-homology pr

146 Beyond 3D structure prediction, evolutionary couplings help identi

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

148 of past CASP (Critical Assessment of protein Structure Prediction) experiments by resetting protein s

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

150 create initial models using I-TASSER protein structure prediction, followed by EM density map-based r

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

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

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

154 ing the available structural information and structure predictions for the transmembrane helices to t

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

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

157 The potential for improving secondary structure predictions from FTIR spectra has been tested

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

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 ver the last few years, the field of protein structure prediction has been transformed by increasingl

164 Ab structure prediction has made great strides, but accurat

165 nding-interface determination and quaternary structure prediction highlight the effectiveness and cap

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 used on performance improvements in tertiary structure predictions, in terms of global 3D model quali

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

170 Protein secondary structure prediction is a fundamental precursor to many

171 Protein structure prediction is a grand challenge.

172 Structure prediction is an important and widely studied

173 Secondary structure prediction is an important problem in RNA bioi

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

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

176 to these challenges in template-free protein structure prediction is to generate large numbers of low

177 challenges in computational protein complex structure prediction is to identify near-native models f

178 RNA secondary structure prediction is widely used to understand RNA fu

179 The concept is applied to crystal structure prediction landscapes and reveals a promising

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

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

182 A novel de novo protein structure prediction method that combines global explora

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

184 (i) decoy sets generated by different RNA 3D structure prediction methods (raw, for-evaluation and st

185 es is at the heart of development of protein structure prediction methods and comparison of their per

186 Theoretical structure prediction methods are an attractive alternati

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

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

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

190 ade in the efficiency and accuracy of RNA 3D structure prediction methods during the succeeding chall

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 roaches involving the combination of crystal structure prediction methods, ab initio calculated chemi

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

197 dentifying limitations of the current RNA 3D structure prediction methods, this work is bringing us c

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

199 corporating contact information into protein structure prediction methods.

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

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

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

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

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

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

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

207 We used structure prediction of JIP60 to identify potential cata

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

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

210 is demonstrated in the large-scale tertiary structure prediction of over 1,200 single-domain protein

211 pectrometry is a powerful tool to assist the structure prediction of protein complexes but has been l

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

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

214 set or decoy structures available for the 3D structure prediction of RNA, hindering the standardizati

215 h the coupling of evolutionary profiles with structure predictions of proteins and protein-protein in

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

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

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

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

220 ccuracy improvement when used for downstream structure prediction on families with the longest length

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 RK, a deep-learning contact-guided ab initio structure prediction pipeline, to model 27 families, whe

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

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

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

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

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

231 e has focused on RNA design as either an RNA structure prediction problem or an RNA inverse folding p

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 omising strategy towards solving the 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 In this model, secondary structure prediction programs are used to calculate dive

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

239 The most popular RNA secondary structure prediction programs utilize free energy (Delta

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

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

242 fferent biological fields, including de novo structure prediction, protein-protein partner identifica

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

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

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

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

247 eukaryotes, together with ab initio protein structure predictions, provide evidence for homology bet

248 9.06 and 78.87% for 3- and 8-state secondary structure predictions, respectively.

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

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

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

252 is particularly useful for automated protein structure prediction servers.

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

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

255 Structure prediction software verified that the AtRBSK s

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

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

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

259 Low accuracy structure predictions suggest a lack of static structure

260 Crystal structure prediction suggested a rich polymorphic landsc

261 ackage for performing supervised learning in structured prediction tasks.

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

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

264 dynamic programing algorithms for secondary structure prediction that could incorporate these models

265 e present a new hybrid approach to secondary structure prediction that gains the advantages of both t

266 Inspired by crystal structure prediction, the search for neat polymorphs was

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

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

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

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

271 Here we extend the use of Potts models from structure prediction to sequence alignment and homology

272 aining GABA(A) receptors and show atom-level structure predictions to provide hypotheses for the impr

273 We present TCRBuilder, a multi-state TCR structure prediction tool.

274 f the popular Rosetta fragment-based protein structure prediction tool.

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

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

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

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

279 elerated RNA ensemble determination by using structure prediction tools that leverage the growing dat

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

281 Secondary structure prediction uncovered a previously undetected h

282 However, accurate structure prediction using computational NMR techniques

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

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

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

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

289 he high accuracy of sequence-based secondary structure predictions, we showed the value of that infor

290 Structure similarity and secondary structure prediction were combined underlying localized

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

292 e results highlight a possible deficiency in structure predictions when based upon assumptions of the

293 contact-map is thus essential to protein 3D structure prediction, which is particularly useful for t

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

295 h human diseases from low-resolution protein structure prediction, which should find important useful

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 g decoy generation for template-free protein structure prediction with regards to balancing of multip

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