ライフサイエンスコーパス: in silico

1 glia pathologies such as Parkinson's disease in silico.

2 igned and their binding to ALK5 investigated in silico.

3 in turn, affect the rate of EPC in vitro and in silico.

4 y BG pathologies such as Parkinson's disease in silico.

5 didates for bacterial actins were discovered in silico.

6 stems, but has been challenging to reproduce in silico.

7 ned to cover relevant epitopes and evaluated in silico.

8 w non-experts to explore biomolecular design in silico.

9 the role of all of the relevant interactions in silico.

10 cumulation in thrombi ex vivo, in vitro, and in silico.

11 erate a membrane-bound configuration of Tim1 in silico.

12 es on the 3'UTRs of the CYPs were identified in-silico.

13 In this work we propose, and investigate in silico, a possible experimental strategy to illuminat

14 P model led us to an unexpected finding that in silico Abeta10-40 and experimental Abeta1-40 constant

15 nd putative function analyses were performed in silico according to Gene Ontology annotations.

16 s pivotal protease of the complement system: in silico active site mapping for hot spot identificatio

17 guidance on how to initiate and maintain an in silico ADME-PK infrastructure in an industrial settin

18 view on the benefits, caveats, and impact of in silico ADME-PK should serve as a resource for medicin

19 ivity relationship (QSAR) models to generate in silico ADMET profiles for hit series to enable more c

20 00,000 small molecule compounds was screened in silico against the available crystal structure of the

21 This is also in line with systematic, in silico Alanine scanning free-energy simulations, whic

22 th increased concordance that should improve in silico algorithm usage during assessment of clinicall

23 There exists a plethora of in silico algorithms designed to help identify and prior

24 omplete concordance of predictions among all in silico algorithms used without specifying the number

25 human leukocyte antigen-binding peptides by in silico algorithms, but the predictive power of this a

26 Using extensive in silico analyses and comparison to cytometry immunophe

27 In silico analyses and experimental validation demonstra

28 In silico analyses confirmed their pathogenic potential.

29 lycosylation are modulated by radiation, and in silico analyses give insight into the mechanism by wh

30 In silico analyses indicated a periplasmic transaminase

31 In silico analyses indicated that IPF lung fibroblasts h

32 In silico analyses located a DNase I hypersensitivity si

33 In silico analyses of HCC, utilizing published profiling

34 In silico analyses suggest that membrane-exposed polar r

35 We performed in silico analyses to identify targets of MIR122 and chr

36 iants were predicted as likely pathogenic by in silico analyses.

37 pneumoniae pneumonia and performed in depth in silico analyses.

38 In silico analysis and chromatin immunoprecipitation ass

39 A comprehensive in silico analysis assessed homologies between virus- an

40 ach SMA-like point mutant of LEN followed by in silico analysis for a subset of these proteins.

41 In silico analysis has not identified any plant CCR4 pro

42 In silico analysis identified 390 novel anticancer drug

43 In silico analysis identified additional regions for PAR

44 In silico analysis identified that several putative Klf5

45 We performed in silico analysis in online platforms, in pooled datase

46 In silico analysis indicated that coding amino acids enc

47 In silico analysis indicated that this 49-mer Aalpha-cha

48 In silico analysis of genome-wide data from >4,000 psori

49 In silico analysis of metabolic processes likely affecte

50 In silico analysis of RNA-seq data from The Cancer Genom

51 In silico analysis of Sap6 predicted four amyloidogenic

52 the developing mouse lung were combined with in silico analysis of the developing mouse salivary glan

53 Homology modeling and in silico analysis of the GmSACPD-C enzyme revealed that

54 Further in silico analysis of this SVA sequence revealed multipl

55 f our unbiased genetic linkage study and the in silico analysis positions genes known to affect NK ce

56 In silico analysis revealed the presence of a core micro

57 In silico analysis showed that the E. coli stringent sta

58 In silico analysis suggested an important role of ZNF143

59 All electrophysiological data and in silico analysis suggested that JZTx-27 trapped VSM of

60 We performed an in silico analysis to identify potential epitopes that m

61 In silico analysis using a computer model of ventricular

62 A preliminary in silico analysis using an electrolyte thermodynamic mo

63 Our in silico analysis, suggesting a novel function for APOE

64 Moreover, In-silico analysis confirmed probable binding polar and

65 In-silico analysis further highlighted potential associa

66 In-silico analysis of human brain expression and network

67 Although in-silico analysis replicated both variants in Europeans

68 In silico analyzes of 19 to 25-nt tRFs derived from 5' (

69 Using in silico and biochemical approaches, we identified two

70 hibited tyrosine kinase inhibitory potential in silico and biochemically; cyanidin-3-O-glucoside had

71 Through in silico and biological analyses, we identified a novel

72 al tools that predict fragmentation patterns in silico and compare these to experimental MS/MS spectr

73 independent variants were taken forward for in silico and de novo replication (11 common and 5 rare)

74 Furthermore, our in silico and experimental analyses identified miR-141-3

75 hypersensitive sites in breast cancer (using in silico and experimental approaches) confirms that the

76 Based on published data and our in silico and high-throughput analyses, we propose a sys

77 In silico and in vitro analyses were used to predict how

78 Here we combine in silico and in vitro approaches to characterize the SO

79 In silico and in vitro characterisation show that these

80 D-kinase domain interface, we show with both in silico and in vitro experiments that perturbation of

81 rary of 1780 prescription drugs by combining in silico and in vitro methods.

82 Finally, both in silico and in vitro results exploring two derivatives

83 In silico and in vivo expression analyses unraveled diff

84 We also provide in silico and in vivo secondary structure predictions fo

85 Mechanisms were identified by in silico and molecular approaches and validated in mous

86 In silico and phylogenetic analyses of these protein fam

87 ors, kinases and phosphatases were estimated in silico and these were capable of building predictive

88 Corresponding target transcripts, predicted in silico and validated by RT-qPCR, often showed opposit

89 a need to predict protein-protein interfaces in silico and various methods for this purpose.

90 This finding, along with in-silico and biological evidence indicating the potenti

91 We created in-silico and in-vitro mixtures of tumour clones, in whi

92 inding toward C-RING1B as shown by in vitro, in silico, and in cellulo studies.

93 een NUPR1 and the paralogue RING1B in vitro, in silico, and in cellulo.

94 We also show that the in-silico annotation by pathway enrichment analysis of t

95 karyotic species using an intra/interspecies in silico approach based on a cross-species similarity s

96 This in silico approach indicates the potential of this molec

97 respective 2D separations assessed using an in silico approach, followed by testing examples of one

98 Here we report an in-silico approach for identification, characterization

99 However, current in silico approaches for protease specificity prediction

100 lidation, and implementation of in vitro and in silico approaches that reduce and replace the use of

101 y screened 961 random radiolabeled molecules in silico as substrates for essential metabolic pathways

102 e OCT1 ligands, defined as ligands predicted in silico as well as found by HTS, were identified.

103 The sequenced genomes can be sorted in silico based on characteristic sequences.

104 le benefits and limitations of interventions in silico, before their implementation in human populati

105 In silico benchmarking on simulated tumour phylogenies a

106 An in silico bioinformatic analysis identified 4 influenza-

107 Four antibodies, designed entirely in silico, bound the minimal FLAG sequence with high spe

108 Together with in silico breeding, GS is now being used in oil palm bre

109 py we here show that short peptides designed in silico by a recently developed algorithm are capable

110 e analytical procedure on patterns generated in silico by computationally pooling Saccharomyces cerev

111 ection on a pseudodiploid genome constructed in silico by merging the two haploids, we find that appr

112 tional candidate genes have been prioritized in silico by their co-expression in the brain.

113 The use of in silico calculations allowed us to rationalize the obt

114 these metabolites, a result consistent with in silico calculations of reactivity parameters.

115 In silico calculations predicted residue substitutions t

116 These results support in silico CDR design of antibody specificity as an emerg

117 In silico cell sorting identified macrophages/microglia,

118 In this article, we study the dynamics of an in silico chemical network with random connectivity in a

119 Two in silico clinical trials with experimental agents ricol

120 riments and simulations but manage to create in silico configurations that have no experimental analo

121 e also observed the same motif module in the in silico constructed ancestral TE that also acted coope

122 solely to membrane structural changes since in silico cooling of the membrane alone, while maintaini

123 nd extract lattice level information through in silico correlation provides fundamental insights into

124 Accordingly, these in silico data can direct ADMET experimentation and prof

125 Together, our in vivo and in silico data provide a framework for understanding how

126 Using in silico data we were able to detect populations at as

127 Furthermore, in silico data, based on the molecular docking simulatio

128 ngth and up to four bonds linkers to give an in silico database of approximately 14 million molecules

129 , using our model as a framework, artificial in silico DBS was applied to find potential alternative

130 ) (n - 1) = 0.83) derived indexes along with in silico descriptors.

131 ther computational work a second generation, in silico designed catalyst emerged, where replacing Bpi

132 these were not even accessible by additional in silico digestion with either Asp-N, Arg-C, Glu-C, Lys

133 In silico, direct numerical simulations of whole blood p

134 We used both in silico docking software, and in vitro molecular and b

135 orming detailed kinetic characterization and in silico docking studies, we found that replacing this

136 In this study we evaluated in silico docking to develop MMP-subtype-selective tumor

137 two plausible binding modes obtained through in silico docking, which provide insights into the struc

138 By exploitation of the obtained models, an in silico drug repositioning approach allowed for the id

139 By using an "in silico drug repurposing" approach and by validating o

140 In silico drug-target interaction (DTI) prediction plays

141 with the cellular metabolic activity and an in silico electrophysiology model.

142 bstrate regulatory interactions were used to in silico estimate the enzymatic activity of signalling

143 eloped, enabling independent verification of in silico estimates for relative organelle abundance.

144 In silico estimates of protein abundances from publicly

145 In summary, signalling entropy allows in silico estimation of the differentiation potency and

146 Having used a similar strategy for the in silico evaluation of 150 mutations of CYP21A2, the di

147 A simple in silico evaluation of novel missense mutations could h

148 Recently, we reported for the first time in silico evidence of RIP encoding genes in metazoans, i

149 Overall, our in silico evolution experiment offers a window to study

150 ed with a fitness-based approach inspired by in silico evolution.

151 which are then used to query the "metabolic in-silico expansion" database (MINE DB) to obtain possib

152 ch is capable of performing an unprecedented in silico experiment-simulating an entire mammal red blo

153 Assisted by the model, we conduct in silico experiments to compare the efficacy of differe

154 We next perform over 80,000 in silico experiments to infer how metabolic interdepend

155 wall interaction, laying the foundations for in-silico experiments of zebrafish behaviour.

156 struct a valid publicly available method for in silico fimH subtyping of Escherichia coli particularl

157 is modeled on that of 1A1 and used to screen in silico for endogenous metabolite 1A3 allosteres.

158 bines the results from database searches and in silico fragmentation analyses and places these result

159 LipidMatch leverages the most extensive in silico fragmentation libraries of freely available so

160 species spanning 56 lipid types contained in in silico fragmentation libraries.

161 formation metabolites, wrapper functions for in silico fragmentation software, nearest neighbor chemi

162 a, it becomes practical to mine STR profiles in silico from genome sequences.

163 In combination with results from a range of in silico functional analyses and wet bench experiments,

164 Gene set analysis and in silico functional evaluation revealed pathways and ce

165 lies of the NIMH BD Family Study followed by in silico functional prediction.

166 Furthermore, models developed using in silico generated mixture gene expression profiles fro

167 Using in silico genome-wide sequence analyses, we identified m

168 Next, we used a novel in silico genomic analysis, searchable platform-independ

169 We treat in silico granulomas with recommended daily doses of eac

170 ally, we developed infrastructure to perform in silico, high-throughput hypotheses testing on such pr

171 lead optimization of the benzene-sulfonamide in silico hit compound 3.

172 16 novel variants predicted to be pathogenic in silico in 15 cases.

173 In sum, our complementary in silico, in vitro, and in vivo analysis argues that in

174 al analysis of ANGPT1 variants in a combined in silico, in vitro, and in vivo approach, supporting a

175 A systematic approach involving in silico, in vitro, ex vivo and in vivo studies is empl

176 An integrated in silico/in vitro approach was used to investigate the

177 These in silico insights guided corroborating functional studi

178 onships between model constituents following in silico knockouts were uncovered, and steady-state ana

179 f >1,000 compounds on the Galphai-GIV PPI by in silico ligand screening and separately by a chemical

180 In silico methods for phosphorylation site prediction ca

181 igen interactions using previously described in silico methods.

182 Using in silico miRNA target database analyses combined with p

183 tion-maps of adhering particles using a new, in silico model confirmed that adhesion to surfaces is i

184 analysis across E1E2 in order to propose an in silico model for the ectodomain of the E1E2 heterodim

185 To fill the gap, we present an in silico model of affinity maturation to examine two re

186 n vitro and using these data in a predictive in silico model of the adult human ventricular myocyte.

187 We developed an in silico model of two-dimensional actomyosin meshwork c

188 sk was selected to develop and calibrate the in silico model.

189 The proposed three-dimensional, multiscale, in-silico model of dynamically coupled angiogenic tumour

190 sful site-directed mutagenesis combined with in silico modeling and docking studies for the first tim

191 More importantly, by combining in silico modeling and our BiLC assay, we identified a s

192 from a high throughput screen and subsequent in silico modeling approaches.

193 In silico modeling confirms that AMHB effects outweigh t

194 NHP) and human studies with PF-04958242, and in silico modeling of AMPAR-NMDAR interactions in the hi

195 Hence, in silico modeling of brain co-expression is an efficien

196 cordings from auditory brainstem neurons and in silico modeling revealed that application of AUT00063

197 In silico modeling showed that theaflavins bind to Asn26

198 Additionally, in silico modeling suggests that the neutralizing VHH bi

199 Importantly, in silico modeling validated that this Ser-to-Arg mutati

200 Using in silico modeling, we discovered that anisotropic proli

201 sing systems immunology approaches combining in silico modelling of a reconstructed gene regulatory n

202 cinogens; incorporate epigenetic biomarkers, in silico modelling, high-performance computing and high

203 We also discuss in silico models that reach the same general conclusions

204 provides valuable information for developing in silico models to simulate the lipid digestibility and

205 Using a combination of in silico molecular docking and in vitro directed evolut

206 In silico molecular modeling using atomic resolution and

207 tions with RAD51 and DNA are correlated with in silico molecular modeling.

208 We apply our technique to an in silico motor control neuroscience experiment, using t

209 Here, the authors take an in silico naive Bayesian classifier approach to integrat

210 plings to infer the synaptic connectivity in in silico networks of neurons and compare its performanc

211 Docking in silico of 121 pesticide contaminants of American hive

212 452 putative single nucleotide polymorphisms in-silico, of which 8,967 high value SNPs were incorpora

213 o wild-type inhibitors, previously predicted in silico, offer an explanation for the lack of antivira

214 We carried out dynamic force manipulations in silico on a variety of coiled-coil protein fragments

215 At the same time, in silico orthology prediction tools often require large

216 le associations of our scores with diversity in-silico (p < 0.001) and moderate associations in-vitro

217 ere not identical for patients and pigs, but in-silico pathway analysis of proteins with >/=2-fold hi

218 Thus, we used in silico pattern searches to define a pneumococcal secr

219 scribed, we evaluate by Boolean modeling and in silico perturbations the import of given circuit feat

220 The model may be used as an in silico platform for future research on the regulation

221 The model is used to explore in silico potential drugs that could slow the progressio

222 In silico predicted target genes were confirmed in repor

223 Prunin 1 and 2, were enzymatically digested in silico predicting 10 and 14 peptides, respectively.

224 The server summarizes several in silico prediction algorithms and conservation scores:

225 Using in silico prediction and functional validation, we ident

226 of ANGPT1 variants was investigated by using in silico prediction and plasma and transfected cells fr

227 Through a combination of in silico prediction and site-directed mutagenesis, we h

228 ndary structure revealed differences between in silico prediction and structure probing.

229 By means of in silico prediction and subsequent functional validatio

230 ss (MHC-II) epitope was identified, based on in silico prediction combined with ex vivo screening, an

231 ed that OeGLU is a homomultimer with high Mr In silico prediction modeling of the complex structure a

232 miRNA on FXI regulation was performed using in silico prediction tools and in vitro luciferase assay

233 or integration of other analytical data, and in silico prediction tools for modern drug discovery.

234 whole exome sequencing (WES), burden tests, in silico prediction, unbiased in vivo analyses of the m

235 In silico predictions and in vitro transport studies acr

236 etation (false concordance) where concordant in silico predictions are opposite to the evidence provi

237 iding increased specificity as compared with in silico predictions based on motifs from methods such

238 bservation highlights the need for improving in silico predictions of peptide immunogenicity.

239 computational tools have been developed for in silico predictions of protein stability in recent yea

240 Our in silico predictions were experimentally confirmed usin

241 genes, frequency, mutation hotspot residues, in silico predictions, and functional assays were all in

242 ent of the inhibitory capacity supported the in silico predictions, suggesting that evaluating the el

243 gi of two cell lines, further confirming the in silico predictions.

244 o acids, and are predicted to be damaging by in silico programs.

245 and delivered complementary constraints for in silico protein docking.

246 S-based proteomic analysis was combined with in silico quantum mechanical calculations to improve und

247 of a large space of experimental parameters in silico, rather than through costly experimental trial

248 In silico reconstruction of the proliferation process an

249 screen of 1.5 million compounds, followed by in silico refinement and screening for biological activi

250 and beta-cells were detected using ANOVA and in silico replications of mouse and human islet cell gen

251 In silico results indicated that fatty acid (but not cho

252 Although in silico Rosetta simulations correctly identified posit

253 ontribute to cardiac disease, we employed an in silico screen for cardiac-enriched cDNAs.

254 al lattice-energy searches, which provide an in silico screening method to evaluate candidate molecul

255 In silico screening reveals that the majority of the pre

256 ganisms like Pseudomonas sp. were chosen for in-silico screening toward polyester hydrolyzing enzymes

257 They were subjected to 2 different in silico screenings and 6 peptides were shortlisted.

258 Overall, our in silico search for post-transcriptional regulators ide

259 Based on the in-silico search, a cutinase from Pseudomonas pseudoalca

260 lyses, live-cell microscopy, and simulations in silico showed that fused mitochondria become critical

261 ested SELDOM on a number of experimental and in silico signal transduction case-studies, including th

262 In summary we present an in silico simulation of the glucocorticoid receptor inte

263 Here we uncover, using in silico simulation, two feed-forward Hox-miRNA loops a

264 In silico simulations performed after model parameterisa

265 babilistic model of cell fate allocation and in silico simulations predict a transient wave of acinar

266 synthesis, or complex mathematical models or in-silico simulations.

267 ely related species, followed by RNA-seq and in silico species separation.

268 e mass fragmentation rules are combined with in silico spectra prediction programs CFM-ID and MS-FIND

269 Here we present SVScore, a tool for in silico structural variation (SV) impact prediction.

270 TK therapeutics would benefit from improved, in-silico structural modeling of the kinase's solution e

271 Here we identify anti-VEEV agents using in silico structure-based-drug-design (SBDD) for the fir

272 In silico studies suggested that the Si horizontal lineS

273 ing target oriented synthesis (TOS) based on in silico studies, molecules with significant docking sc

274 the type of evidence ( in vivo , in vitro or in silico ) supporting these influence the predictions.

275 In silico target prediction identified 519 potential cro

276 In silico target prediction identified that insulin-like

277 In Silico target predictions revealed that miR-100-5p ta

278 Here, using a broad set of in vitro and in silico techniques we addressed molecular mechanisms o

279 for optimizing the dialysis regimen and for in silico testing of novel approaches to enhance removal

280 Moreover, we show in silico that cuRRBS-defined restriction enzymes consis

281 technique to simulate enzymatic fingerprints in silico that were used to build the PLS models for FA

282 ant HLA-A*0201 (A2) as a model and predicted in silico the 41 highest-affinity, A2-binding 8-11-mer p

283 istic and stochastic modeling, we reproduced in silico the different dynamic responses of Msn2 to glu

284 codynamic mathematical model that identifies in silico the most effective administration schedule for

285 s able to markedly increase therapy response in silico These results can help guide the rational desi

286 icacy of SINCERITIES in inferring GRNs using in silico time-stamped single cell expression data and s

287 ontext could be rationally combined together in silico to create promoters with highly predictable ac

288 irus species, and second, 26 genes predicted in silico to have metabolic functions likely involved in

289 The domain structures were then docked in silico to provide a generic model for the NOX family.

290 harmacokinetic (PBPK) modeling is a powerful in silico tool that can be used to simulate the toxicoki

291 We developed an in-silico tool to identify physical clusters of co-regul

292 gy of identified variants was examined using in silico tools.

293 Reporter gene and in silico transcription factor binding analyses indicate

294 We assessed in-silico tumour-specific neoantigen predictions by muta

295 This study used in silico virtual screening methodology to identify seve

296 ation of rigorous experimental screening and in silico virtual screening, we recently identified nove

297 framework to optimize combinatorial therapy in silico We constructed a detailed kinetic model of the

298 easure how these could impact their analyses in silico , we have developed gargammel, a package that

299 In silico, we identified 19,420 EPIC probes (referred as

300 are the first to predict hydroxymethylation in silico with high whole-genome accuracy, paving the wa