scholarly journals Enhanced Conformational Space Sampling Improves the Prediction of Chemical Shifts in Proteins

2010 ◽  
Vol 132 (4) ◽  
pp. 1220-1221 ◽  
Author(s):  
Phineus R. L. Markwick ◽  
Carla F. Cervantes ◽  
Barrett L. Abel ◽  
Elizabeth A. Komives ◽  
Martin Blackledge ◽  
...  
Keyword(s):  
Chemical Shifts ◽  
Conformational Space ◽  
Conformational Space Sampling

scholarly journals Exploring the conformational space of amorphous cellulose using NMR chemical shifts

2012 ◽  
Vol 90 (3) ◽  
pp. 1197-1203 ◽  
Author(s):  
Tetsuya Mori ◽  
Eisuke Chikayama ◽  
Yuuri Tsuboi ◽  
Nobuhiro Ishida ◽  
Noriko Shisa ◽  
...  
Keyword(s):  
Chemical Shifts ◽  
Conformational Space ◽  
Amorphous Cellulose ◽  
Nmr Chemical Shifts

scholarly journals Characterizing RNA ensembles from NMR data with kinematic models

2014 ◽  
Vol 42 (15) ◽  
pp. 9562-9572 ◽  
Author(s):  
Rasmus Fonseca ◽  
Dimitar V. Pachov ◽  
Julie Bernauer ◽  
Henry van den Bedem
Keyword(s):  
Dipolar Coupling ◽  
Chemical Shifts ◽  
Residual Dipolar Coupling ◽  
Sampling Procedure ◽  
Conformational Space ◽  
Conformational Sampling ◽  
Computer Algorithms ◽  
Stem Loop ◽  
Nmr Data ◽  
Functional Mechanisms

Abstract Functional mechanisms of biomolecules often manifest themselves precisely in transient conformational substates. Researchers have long sought to structurally characterize dynamic processes in non-coding RNA, combining experimental data with computer algorithms. However, adequate exploration of conformational space for these highly dynamic molecules, starting from static crystal structures, remains challenging. Here, we report a new conformational sampling procedure, KGSrna, which can efficiently probe the native ensemble of RNA molecules in solution. We found that KGSrna ensembles accurately represent the conformational landscapes of 3D RNA encoded by NMR proton chemical shifts. KGSrna resolves motionally averaged NMR data into structural contributions; when coupled with residual dipolar coupling data, a KGSrna ensemble revealed a previously uncharacterized transient excited state of the HIV-1 trans-activation response element stem–loop. Ensemble-based interpretations of averaged data can aid in formulating and testing dynamic, motion-based hypotheses of functional mechanisms in RNAs with broad implications for RNA engineering and therapeutic intervention.

scholarly journals Classification of RNA backbone conformation into rotamers using 13C′ chemical shifts: How far we can go?

2019 ◽  
Author(s):  
A. A. Icazatti ◽  
J.M. Loyola ◽  
I. Szleifer ◽  
J.A. Vila ◽  
O. A. Martin
Keyword(s):  
Chemical Shifts ◽  
Conformational Space ◽  
Phosphate Backbone ◽  
Ribose Phosphate ◽  
Conformational Preferences ◽  
Backbone Conformation ◽  
Rna Backbone ◽  
Rna Backbone Conformation ◽  
Torsional Angles

ABSTRACTThe conformational space of the ribose–phosphate backbone is very complex as is defined in terms of six torsional angles. To help delimit the RNA backbone conformational preferences 46 rotamers have been defined in terms of the these torsional angles. In the present work, we use the ribose experimental and theoretical 13C′ chemical shifts data and machine learning methods to classify RNA backbone conformations into rotamers and families of rotamers. We show to what extent the use of experimental 13C′ chemical shifts can be used to identify rotamers and discuss some problem with the theoretical computations of 13C′ chemical shifts.

Synthesis, high-field NMR, X-ray structure, and conformational analysis of a 10-membered diamide disulfide ring

1997 ◽  
Vol 75 (2) ◽  
pp. 140-161 ◽  
Author(s):  
Rabindranath B. Maharajh ◽  
James P. Snyder ◽  
James F. Britten ◽  
Russell A. Bell
Keyword(s):  
Conformational Analysis ◽  
Molecular Modelling ◽  
Density Functional ◽  
Nuclear Overhauser Effect ◽  
Chemical Shifts ◽  
Lone Pair ◽  
Oxidative Cyclization ◽  
Conformational Space ◽  
Aromatic Solvent ◽  
X Ray

N,N′-[Dimethyl-(2,2′-dithiobisacetyl)]ethylenediamine (1) has been synthesized in 30% overall yield from N,N′-dimethylethylenediamine and thioacetic acid by an improved procedure involving simultaneous deprotection and oxidative cyclization with iodine. This cyclic diamide disulfide exists in solution as a mixture of two Z,Z and one Z,E disulfide, and amide ring conformers and has been characterized by nuclear Overhauser effect (NOE), 1H–1H, 1H–13C shift-correlated 2D-NMR and molecular modelling studies. Among the Z,Z ring conformers Z,Z1 and Z,Z2, the former predominates and interconverts with the latter isomer by rotation about the S—S bond with an activation energy of 14.5 ± 1.3 kcal/mol. Coalescence of N-CH3 signals occurred at ca. 127 °C (500 MHz), which corresponded to an approximate barrier to amide rotation of 19.3 kcal/mol. Aromatic solvent-induced shifts in C6D6 corroborated molecular mechanics and NOE predictions of amide stereochemistry. The structure of the Z,E stereoisomer of 1 has been determined by single-crystal X-ray diffraction at 296 K. A large geminal N-CH2 inequivalence (>2 ppm in CDCl3) was observed for the Z,Z conformers. Proton chemical shifts have been calculated for the conformers of 1 and related molecular fragments with DFT/GIAO theory. Absolute chemical shifts are modelled within 0.2 ppm of experiment. The unusual nonequivalence of geminal N-CH2 and S-CH2 protons can be understood as a combination of shielding mechanisms derived from short N-methyl contacts, amide group orientation, and sulfur lone-pair disposition. An implication of these results is the possibility of using α-CH (and eventually α-CH) shifts to probe the local conformational space in cyclic peptides and other conformationally constrained rings. Keywords: amide/disulfide rotamers, conformational analysis, density functional theory, DFT/GIAO NMR shift calculations, methylene nonequivalence, molecular modelling.

scholarly journals Artificial Intelligence Guided Conformational Mining of Intrinsically Disordered Proteins

2021 ◽  
Author(s):  
Aayush Gupta ◽  
Souvik Dey ◽  
Huan-Xiang Zhou
Keyword(s):  
Artificial Intelligence ◽  
Intrinsically Disordered Proteins ◽  
Chemical Shifts ◽  
Computational Cost ◽  
Md Simulations ◽  
Conformational Space ◽  
Disordered Proteins ◽  
Training Dataset ◽  
Multivariate Gaussian Distribution ◽  
Intrinsically Disordered

Artificial intelligence recently achieved the breakthrough of predicting the three-dimensional structures of proteins. The next frontier is presented by intrinsically disordered proteins (IDPs), which, representing 30% to 50% of proteomes, readily access vast conformational space. Molecular dynamics (MD) simulations are promising in sampling IDP conformations, but only at extremely high computational cost. Here, we developed generative autoencoders that learn from short MD simulations and generate full conformational ensembles. An encoder represents IDP conformations as vectors in a reduced-dimensional latent space. The mean vector and covariance matrix of the training dataset are calculated to define a multivariate Gaussian distribution, from which vectors are sampled and fed to a decoder to generate new conformations. The ensembles of generated conformations cover those sampled by long MD simulations and are validated by small-angle X-ray scattering profile and NMR chemical shifts. This work illustrates the vast potential of artificial intelligence in conformational mining of IDPs.

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