ライフサイエンスコーパス: energy landscape

1 ewable energy input, would revolutionize the energy landscape.

2 hydride isomers, due to the relatively flat energy landscape.

3 stochastic exploration of a high-dimensional energy landscape.

4 to large electron-hole puddles smearing its energy landscape.

5 ning would have a large impact on the global energy landscape.

6 a rotation-coupled sliding over a corrugated energy landscape.

7 iate receptor conformations along the OFF-ON energy landscape.

8 ng trajectories that are shown upon the free energy landscape.

9 or the construction of a coarse-grained free energy landscape.

10 of attractor-like structure in the inferred energy landscape.

11 de detachment was defined solely by the free-energy landscape.

12 3(T2AG3)3]), computing also the binding free-energy landscape.

13 nanomagnet array, resulting in an asymmetric energy landscape.

14 units to exhaustively sample the interaction energy landscape.

15 otein folding can be described by a funneled energy landscape.

16 rain to a "target" strain over a path in the energy landscape.

17 resulting descriptions of the conformational energy landscape.

18 nsitions between structural states within an energy landscape.

19 d the protein as a random walker in the free energy landscape.

20 ncerted atomic motions on a multidimensional energy landscape.

21 arises from strain-induced smoothing of the energy landscape.

22 teract and travel through a static potential energy landscape.

23 rtainty regarding the order parameter's free-energy landscape.

24 imate the barriers on the corresponding free energy landscape.

25 barrier-crossing transitions on a potential energy landscape.

26 reaction rates defined by an underlying free energy landscape.

27 ontrol the reaction pathway through the free energy landscape.

28 ing environment can affect the nascent chain energy landscape.

29 d the G(i) to calculate two-dimensional free energy landscapes.

30 energy model (AWSEM) to construct their free energy landscapes.

31 variations are highlighted on projected free energy landscapes.

32 hilic zeolite catalysts modify reaction free energy landscapes.

33 nequilibrium conditions and to map out their energy landscapes.

34 isordered proteins (IDPs) by affecting their energy landscapes.

35 t photovoltaics that have transformed global energy landscapes.

36 arising from the combination of a flat free-energy landscape, a fragmented local structure, and the

37 e capability may be enabled by accessing the energy landscape above the ground state, which may have

38 Which features of the energy landscape affect the flux distribution?

39 Using a variety of energy landscape analysis tools, here we uncover the fea

40 aracterize brain dynamics in autism using an energy-landscape analysis applied to resting-state fMRI

41 ies in one of the minima of a conformational energy landscape and can be selected according to the ch

42 wn to arise from extended flat basins in the energy landscape and collective hopping behavior facilit

43 dramatically influences the permeation free energy landscape and explains why the conventional model

44 t kinetic traps in their conformational free energy landscape and fold efficiently to the native stat

45 f scaling parameters that are related to the energy landscape and geometric nature of the competitors

46 By calculating the energy landscape and minimum action paths for the T cell

47 iary structures can shape the ligand-binding energy landscape and modulate protein-protein interactio

48 that the LCO heightens the ruggedness of the energy landscape and raises activation barriers governin

49 d representations of the conformational free energy landscape and the complex folding mechanism inher

50 We report the characterization of the energy landscape and the folding/unfolding thermodynamic

51 ent experiments that revealed a quantitative energy landscape and the microscopic pathways underlying

52 sical SNARE properties such as the zippering energy landscape and the surface charge distribution.

53 We report the free-energy landscape and thermodynamics of the protein-prote

54 Here we show how the excited-state energy landscape and thus the coherence characteristics

55 etermination of transcription-factor binding-energy landscapes and mechanistic modeling, enabling us

56 force field, we compute and compare the free energy landscapes and relative stabilities of amyloid-be

57 ar dynamics (AWSEM)-MD] is used to study the energy landscapes and relative stabilities of amyloid-be

58 s unsolved is sampling high-dimensional free-energy landscapes and systems that are not easily descri

59 anging concentration on the aggregation free-energy landscapes and to predict the effects of phosphor

60 s ascribed to their hierarchical and fractal energy landscape, and is also different from [Formula: s

61 2018 on the need for decarbonization in our energy landscape, and specifically the status and challe

62 dynamics, the trompomyosin-actin interaction-energy landscape, and the generated force by the sarcome

63 r mutations, release of autoinhibition, free energy landscapes, and targeted pharmacology in precisio

64 Here, we show that an energy landscape approach elucidates the underlying phys

65 ultipathway protein folding transitions, our energy landscape approach from first principles is the b

66 nt thermal transport reflects macromolecular energy landscape architecture through the topological ch

67 ver, most biophysical studies of a protein's energy landscape are carried out in isolation under idea

68 ) O(4) thin films resulting from the complex energy landscape are reported.

69 ations under drug pressure remodels the free-energy landscape as a primary mechanism.

70 e point mutants at the interface altered the energy landscape as predicted, but were not enough to co

71 of apoSOD1(2SH) and characterize their free energy landscapes as a first step in understanding the i

72 sen-Shannon distance between sample-specific energy landscapes as a measure of epigenetic dissimilari

73 single coordinate in a multidimensional free energy landscape, as encountered in electrophysiology an

74 cular dynamics to dissect changes in folding energy landscape associated with cAMP-binding signals tr

75 By computing the conformational free-energy landscape associated with the activation of the r

76 machine learning framework that exploits the energy landscape associated with the structure space pro

77 me arises from an equivalent sampling of the energy landscape at the respective melting temperatures.

78 we describe the characterization of folding energy landscapes at high resolution, studies of structu

79 f advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and f

80 Energy landscapes based on F-actin-tropomyosin models sh

81 Recent investigations into energy landscape-based decoy selection approaches show p

82 or reversibility requires that the catalytic energy landscape be flat.

83 ates is determined not only by the potential-energy landscape, but also by selective energy dissipati

84 resulting structures to the underlying free-energy landscape by combining in-situ atomic force micro

85 ess enormous potential to reshape the global energy landscape by converting waste heat into electrici

86 that allowed for a precise modulation of the energy landscape by the solvent polarity.

87 one sampling followed by sequence design and energy landscape calculations.

88 Free energy landscapes can be generated for both cPCA and dpP

89 hallenging due to the need to host a complex energy landscape capable of learning, memory and electri

90 To study how a protein's energy landscape changed over time, we characterized the

91 The effects of these energy landscape changes on the conformational ensemble

92 otein folding model derived from theoretical energy landscape considerations and the defined-pathway

93 phase and mitotic chromosomes from effective energy landscapes constructed using Hi-C data.

94 irpin substrates with an optimized flat free energy landscape containing all binding motifs allows de

95 framework enables the estimation of the free energy landscape corresponding to the identified states.

96 so use enhanced sampling to compute the free-energy landscapes corresponding to our experiments and s

97 From the energy landscape, critical information about an interact

98 as a random walk on a rugged two-dimensional energy landscape defined by beta-sheet alignment and hyd

99 The final state of the system in the energy landscape depended on the pathway of preparation.

100 Our free energy landscape depicts a low barrier for the permeatio

101 Detailed energy landscapes derived from these data provide a rare

102 obal dynamics of thin filament components by energy landscape determination and molecular dynamics si

103 A complex conformational energy landscape determines G-protein-coupled receptor (

104 The current work demonstrates how the free-energy landscape determines the behaviour of different t

105 atic snapshots fail to represent a full free-energy landscape due to homogenization in structural det

106 e protein slides over a sequence-independent energy landscape during fast search but rapidly intercon

107 tions designed to explicitly incorporate the energy landscape (el-SSFs) to investigate the effects of

108 We predict that a simple and universal free-energy landscape enables electron bifurcation, and we sh

109 the microscope, have elucidated how protein energy landscapes facilitate folding and how they are su

110 Modeling of the conformational free energy landscape (FEL) of a thioglycoside strongly favor

111 he surface and corrugation of the adsorption energy landscape felt by water.

112 olecule force spectroscopy to probe the free energy landscape for an unconventional intercalator that

113 Using simulations, we predict the energy landscape for cadherin adhesion, the transition p

114 on donor structure strongly impacts the free energy landscape for CPET to extended solid surfaces and

115 Calculating the free-energy landscape for distinct tRNA species implicates tr

116 proteins typically exhibit a smooth folding energy landscape for fast and efficient folding by avoid

117 Information about the energy landscape for H2 production can be obtained by ch

118 The energy landscape for hSSTR5 activation is consistent wit

119 Although the energy landscape for sliding along a CC interface is dif

120 amics simulations of the conformational free energy landscape for the cyclopropyl inhibitors show a s

121 Upon constructing the energy landscape for the full chemical space as a functi

122 ctrolyte host framework modify the potential energy landscape for the mobile ions, resulting in an en

123 form, reflecting how S672R remodels the free energy landscape for the modulation of HCN4 by cAMP, i.e

124 cs to reconstruct the tension-dependent free-energy landscape for the opening transition in MscL.

125 These results underscore the dynamic energy landscape for transporters and demonstrate how co

126 The underpinning free-energy landscapes for electron bifurcation were also eni

127 Analogous to free-energy landscapes for multipathway protein folding trans

128 rgy model, we construct the aggregation free energy landscapes for polyQ peptides of different repeat

129 The model predicts free-energy landscapes for the different RNA hairpin-forming

130 rse-grained model enables estimation of free-energy landscapes for the interactions of 12 different P

131 asily described by order parameters and free-energy landscapes, for their non-stationary counterparts

132 hape to bind and recognize DNA, shifting the energy landscape from a weak binding, rapid search mode

133 and information theory, we derive epigenetic energy landscapes from whole-genome bisulfite sequencing

134 ion energies, we fully quantify the reaction energy landscape, gaining important predictive power for

135 Free-energy landscapes govern the behavior of all interaction

136 olding dynamics such as the roughness of the energy landscape governing the folding and the level of

137 re sampled via simulations with a predictive energy landscape Hamiltonian.

138 ent friction alone, with ruggedness of their energy landscapes having no consequences for their dynam

139 In principle, reconstructing a protein's energy landscape holds the key to characterizing the str

140 ystallization kinetics proceed down the free energy landscape in a multistage process where each succ

141 ake over the unfolding and dissociation free energy landscape in a vacuum.

142 how to chemically access the underlying free energy landscape in MOFs.

143 Modulation of its energy level on the energy landscape in photosynthetic vs. respiratory enzym

144 We use (1)H NMR to probe the energy landscape in the protein folding and unfolding pr

145 ditions and may not accurately report on the energy landscape in vivo.

146 We show that the M2R has a complex energy landscape in which ligands with different efficac

147 ther emphasizes the need to use well-defined energy landscapes in studying molecular motors in genera

148 n vitro methods, enabling the measurement of energy landscapes in vivo.

149 apoptotic agents fundamentally altered this energy landscape, inducing formation of additional energ

150 itical, detailed information on folding free energy landscapes, intermediates, and pathways.

151 The resolution requires that the diffusion energy landscape is correlated with the underlying speci

152 r understanding of pathway complexity in the energy landscape is crucial for the development of fuel-

153 The high-energy landscape is dominated by an energy ladder of par

154 How exactly a protein's energy landscape is maintained or altered throughout evo

155 tion of the simulation, the aggregation free energy landscape is nearly downhill.

156 This funneled energy landscape is the result of foldable protein seque

157 el, no matter how rugged its underlying free energy landscape is: In other words, this distribution c

158 initial state to better explore the complex energy landscape, is used to solve the highly non-convex

159 p97 and how subtle perturbations to its free-energy landscape lead to significant changes in NTD conf

160 l evolution of the protease to have a rugged energy landscape likely results from intrinsic pressures

161 High stresses modulate the energy landscape markedly and allow the dipoles to rotat

162 Given this forbidding free-energy landscape, mechanisms have evolved that contribut

163 is neural network are used as an input to an energy landscape model for chromatin organization [Minim

164 A basic tenet of the energy landscape model is that proteins fold through man

165 Consistent with this energy landscape model, in bulk experiments we observe p

166 Using a coarse-grained energy landscape model, we predict the structures of the

167 We call the model ELM for "energy landscape model." In ELM, the interaction of the

168 ics simulations of coarse-grained predictive energy landscape models for the constituent proteins by

169 optimization, making the funnel-like binding energy landscape more biased toward the native state.

170 xhibits upward curvature then the underlying energy landscape must be strongly multidimensional.

171 o able to access lower-energy regions of the energy landscape of a given protein with similar or bett

172 nal extent of disorder and the nature of the energy landscape of a highly reactive, intrinsically dis

173 the extent to which one can reconstruct the energy landscape of a protein in the absence of sufficie

174 shows that it is possible to reconstruct the energy landscape of a protein with reasonable detail and

175 The energy landscape of a supramolecular material can includ

176 The mutations alter the energy landscape of Abl in complex ways: increased kinas

177 estigate the effect of ligand binding on the energy landscape of acyl-coenzyme A (CoA)-binding protei

178 ate the effects of concentration on the free energy landscape of aggregation as well as the effects o

179 The folding energy landscape of an RNA is highly dependent on its nu

180 toward the formation of kinetic traps in the energy landscape of aS fibril disassembly and the presen

181 Our framework shows how the rugged energy landscape of disordered mechanical materials can

182 The complex folding energy landscape of DNA G-quadruplexes leads to numerous

183 computing via molecular simulations the free energy landscape of DNA origami hinges actuated between

184 r dynamics simulations to determine the free energy landscape of E7.

185 In this study, we explore the underlying energy landscape of enzyme-substrate interactions and in

186 we characterize the unusually rugged folding energy landscape of human immunodeficiency virus-1 prote

187 ynamic force spectroscopy can probe the free energy landscape of interacting bonds, but interpretatio

188 tanding how lipids impact the conformational energy landscape of macromolecular membrane complexes wh

189 embrane can largely shape the conformational energy landscape of membrane proteins and impact the ene

190 d membrane composition modulate the complete energy landscape of membrane-bound proteins.

191 be conveniently used to derive a simplified energy landscape of protein folding.

192 gand binding on the mechanical stability and energy landscape of proteins are incompletely understood

193 way or as a reflection of a multidimensional energy landscape of proteins under force.

194 and show how DNA supercoiling modulates the energy landscape of R-loop formation and dictates access

195 Thus, the energy landscape of ribosome nascent chains and the effe

196 sible structures, we compute the entire free energy landscape of secondary structures resulting from

197 lts reveal a weakly funneled and rugged free energy landscape of SH4UD, which gives rise to a heterog

198 es taking the effective disordered potential energy landscape of strongly excited crystals and dopant

199 increase in the degeneracy of the potential energy landscape of the BiFeO(3) system exemplified by a

200 helices in membrane, thus leading to a free energy landscape of the dimerization process.

201 tional methods to explore the global binding energy landscape of the Fis1:Fis2:DNA ternary complex.

202 r dynamics simulations to determine the free energy landscape of the L99A cavity mutant of T4 lysozym

203 Our exploration of the energy landscape of the Li-CO2 binary phase diagram usin

204 mbly behavior permitted the unveiling of the energy landscape of the living CDSA process.

205 It is shown that the conformational energy landscape of the Michaelis complex analogue is sh

206 The energy landscape of the model was derived by using the m

207 We used our OP technique to map the energy landscape of the protein-induced looping dynamics

208 f SAM and magnesium ions on the folding free energy landscape of the SAM-I riboswitch.

209 s are employed to calculate the folding free energy landscape of the SAM-II riboswitch.

210 The binding energy landscape of the second receptor, in contrast, pa

211 table mechanical equilibria in the effective energy landscape of the spindle.

212 he transition field [Formula: see text], the energy landscape of the system becomes completely flat,

213 er (molecules), thus completely changing the energy landscape of the system.

214 ine the thermodynamic stability and the free-energy landscape of the tetraloop.

215 We characterize the free-energy landscape of these three fragments in terms of a

216 prefusion trimer and rationalizing the free-energy landscape of this conformational machine.

217 ic models are capable of sampling the entire energy landscape of TIM barrels and offer the possibilit

218 halpies and entropies that comprise the free energy landscape of transfer hydrogenation catalysis.

219 Our current system with an energy landscape of two competing nucleated aggregation

220 minimum of the equilibrium unperturbed free-energy landscape of two K+ ions that can be 'locked' in

221 The conformational free energy landscapes of free alpha-l-arabinofuranose and se

222 the applicability of GaMD for exploring free energy landscapes of large biomolecules and the simulati

223 demonstrates the ability to traverse complex energy landscapes of metal-organic systems using the com

224 d energy model (AWSEM), is used to study the energy landscapes of nucleation of the two different fib

225 ics simulations to explore the eversion free energy landscapes of oxoG and G by Fpg, focusing on stru

226 ework to reveal the nanoscale and metastable energy landscapes of Pourbaix (E-pH) diagrams, providing

227 r finding is explained in the context of the energy landscapes of self-assembly.

228 les have successfully reconstructed detailed energy landscapes of several medium-size proteins.

229 metry methods were developed to describe the energy landscapes of six polyoxometalates (POMs), Li-U(2

230 s highlight the importance of characterizing energy landscapes of targets and its changes by drug bin

231 Mapping the energy landscapes of these interactions is essential for

232 w variations in sequence perturb the folding energy landscapes of three model sequences with 3alpha,

233 , and computational analysis, we defined the energy landscapes of WT and 14 mutated CRPs to determine

234 along with coevolutionary information and an energy landscape optimized force field (AWSEM), we predi

235 ms, most experiments do not directly measure energy landscapes, particularly for interactions with st

236 ure cruciality by changes in the capsid free-energy landscape partition function when an interaction

237 ier-hopping processes on a fractal potential energy landscape (PEL) in which shear transformations an

238 ses can be neatly expressed by the potential energy landscape (PEL).

239 at is characterised by complicated potential energy landscapes (PEL) consisting of sets of barriers a

240 n (the proofreading step) through the use of energy landscape principles, molecular dynamics simulati

241 Quantum mechanical calculations of the free energy landscapes reveal how the neutral inhibitors prov

242 Molecular modeling of the energy landscape reveals a lower barrier for the kinetic

243 Free energy landscape reveals that inserted dimers represent

244 Quantitative characterization of the free-energy landscapes reveals the mechanism of nucleosome un

245 skyrmion dynamics is dominated by the local energy landscape such as materials defects and the local

246 e transition, we construct an effective free-energy landscape that describes the formation jitter and

247 ng so allows for the study of the underlying energy landscape that governs the mechanism of Rsn-2 int

248 ion and therefore occupy local minima on the energy landscape that have relatively narrow basins.

249 s, as the many constituents lead to a rugged energy landscape that increases the resistance to disloc

250 ystal structures, revealing a conformational energy landscape that is characterized by multiple struc

251 In addition to predicting a free-energy landscape that is consistent with previous experi

252 e ensembles begins to complete the catalytic energy landscape that is generally characterized by stru

253 t of near-barrierless diffusion on a protein energy landscape that is radically reshaped by membrane

254 The first receptor follows a binding energy landscape that partitions the energy provided by

255 rrier creates a transition state in the free energy landscape that slows fibril formation and creates

256 distorts the actin-tropomyosin electrostatic energy landscape that, in muscle, result in aberrant con

257 pen up the possibility to encode the complex energy landscapes that are required for active biologica

258 tion coordinates were used to calculate free-energy landscapes that capture the full process and end

259 changes during protein synthesis of the free energy landscapes that underlie co-translational folding

260 data of methods designed to compute protein energy landscapes, the work opens up interesting venues

261 The generalized energy landscape then served as a basis for developing a

262 The modern energy landscape theory of protein folding predicts mult

263 computes an energy function derived from the energy landscape theory of protein folding.

264 Energy landscape theory, developed in the context of pro

265 network ideas that has been optimized using energy landscape theory.

266 Cellular molecules sometimes alter the energy landscape, thereby changing the ensemble of likel

267 s for activating GPCRs and the corresponding energy landscapes, thereby providing detailed structural

268 Energy landscape thinking raises new questions about the

269 s the observation of systems exploring their energy landscape through monopole quasiparticle creation

270 lization can involve funnel-shaped potential energy landscapes through a detailed analysis of mixed g

271 between such states enabled the folding free-energy landscape to be deduced.

272 hand, allowing the protein to adapt its free-energy landscape to incoming signals.

273 insight into how ClpPs exploit their rugged energy landscapes to enable key conformational changes t

274 Despite the importance of energy landscapes to understanding reaction mechanisms,

275 ysics-based concept and method show that the energy landscape topography is valuable for understandin

276 d proteins, amyloids do not follow a defined energy landscape toward the fibrillary state and often g

277 vigate through the thermodynamic and kinetic energy landscape towards the rational synthesis of targe

278 the complex details of the multidimensional energy landscape traversed by the transition paths from

279 uctuations to escape local minima in complex energy landscapes typical of NP - hard problems.

280 We use this framework to map the effective energy landscape underlying the cytomorphological state

281 The energy landscape underscores the inherent nature of prot

282 We find that the response of the energy landscape upon cAMP binding is domain specific, r

283 hin the open state pore revealed more rugged energy landscapes using polarizable force fields, and th

284 In this work, we study paths in the energy landscape via which the transition between the sk

285 exchange with solute tempering, and the free energy landscape was explored by metadynamics.

286 Using the free energy landscape we propose the pathway of Abeta25-35 bi

287 s and their length and sequence modulate the energy landscape, we obtain design rules for tuning the

288 Based on the energy landscape, we quantify missing information, emerg

289 g the ruggedness of the associated potential energy landscape, we underpin the molecular origin of th

290 ts the importance of describing the complete energy landscape when studying the elongation mechanism

291 nal freedom along its reaction path over the energy landscape, which in turn allows the phosphoryl tr

292 gram: (i) the marginal stability of the free-energy landscape, which induces a gapless phase responsi

293 terestingly, the single nanoparticle elastic energy landscape, which we map with attojoule precision,

294 The renewable energy landscape will be reshaped if the current trend i

295 Their structures correspond to an energy landscape with a single, albeit highly functional

296 he three wetting modes by analyzing the free energy landscape with many local minima originated from

297 ential to accurately reconstruct interfacial energy landscapes with steep gradients.

298 find that all glasses evolve in a very rough energy landscape, with a hierarchy of barrier sizes corr

299 s simulations uncover a rich structural free energy landscape, with secondary building units (SBUs) a

300 ionalized by a remodeling of its rugged free-energy landscape, with very subtle shifts in the populat