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 Structure-Free Validation of Residual Dipolar Coupling and Paramagnetic Relaxation Enhancement Measurements of Disordered Proteins

Biochemistry ◽  
2015 ◽  
Vol 54 (46) ◽  
pp. 6876-6886 ◽  
Author(s):  
Francisco N. Newby ◽  
Alfonso De Simone ◽  
Maho Yagi-Utsumi ◽  
Xavier Salvatella ◽  
Christopher M. Dobson ◽  
...  
Keyword(s):  
Dipolar Coupling ◽  
Residual Dipolar Coupling ◽  
Paramagnetic Relaxation ◽  
Paramagnetic Relaxation Enhancement ◽  
Disordered Proteins

scholarly journals On the Use of the Discrete Constant pH Molecular Dynamics to Describe the Conformational Space of Peptides

Polymers ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 99
Author(s):  
Cristian Privat ◽  
Sergio Madurga ◽  
Francesc Mas ◽  
Jaime Rubio-Martínez
Keyword(s):  
Molecular Dynamics ◽  
Amino Acids ◽  
Md Simulations ◽  
Point Of View ◽  
Conformational Space ◽  
Conformational Sampling ◽  
Protonation State ◽  
Constant Ph ◽  
Biochemical Systems ◽  
Constant Ph Molecular Dynamics

Solvent pH is an important property that defines the protonation state of the amino acids and, therefore, modulates the interactions and the conformational space of the biochemical systems. Generally, this thermodynamic variable is poorly considered in Molecular Dynamics (MD) simulations. Fortunately, this lack has been overcome by means of the Constant pH Molecular Dynamics (CPHMD) methods in the recent decades. Several studies have reported promising results from these approaches that include pH in simulations but focus on the prediction of the effective pKa of the amino acids. In this work, we want to shed some light on the CPHMD method and its implementation in the AMBER suitcase from a conformational point of view. To achieve this goal, we performed CPHMD and conventional MD (CMD) simulations of six protonatable amino acids in a blocked tripeptide structure to compare the conformational sampling and energy distributions of both methods. The results reveal strengths and weaknesses of the CPHMD method in the implementation of AMBER18 version. The change of the protonation state according to the chemical environment is presumably an improvement in the accuracy of the simulations. However, the simulations of the deprotonated forms are not consistent, which is related to an inaccurate assignment of the partial charges of the backbone atoms in the CPHMD residues. Therefore, we recommend the CPHMD methods of AMBER program but pointing out the need to compare structural properties with experimental data to bring reliability to the conformational sampling of the simulations.

scholarly journals Real-time prediction of 1H and 13C chemical shifts with DFT accuracy using a 3D graph neural network

2021 ◽  
Author(s):  
Yanfei Guan ◽  
S. V. Shree Sowndarya ◽  
Liliana C. Gallegos ◽  
Peter C. St. John ◽  
Robert S. Paton
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Quantum Chemical ◽  
Organic Molecules ◽  
Chemical Shifts ◽  
13C Chemical Shifts ◽  
Time Prediction ◽  
Nmr Data ◽  
Proton Chemical Shifts

From quantum chemical and experimental NMR data, a 3D graph neural network, CASCADE, has been developed to predict carbon and proton chemical shifts. Stereoisomers and conformers of organic molecules can be correctly distinguished.

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

A Pyoverdin from the Antarctica Strain 51W of Pseudomonas fluorescens

1999 ◽  
Vol 54 (3-4) ◽  
pp. 156-162 ◽  
Author(s):  
Jessica Voss ◽  
Kambiz Taraz ◽  
Herbert Budzikiewicz
Keyword(s):  
Amino Acids ◽  
Pseudomonas Fluorescens ◽  
Binding Sites ◽  
Chemical Shifts ◽  
Chemical Degradation ◽  
Spectroscopic Methods ◽  
Extreme Conditions ◽  
Structural Elements ◽  
Nmr Data ◽  
Schirmacher Oasis

From the strain 51W of Pseudomonas fluorescens living under extreme conditions at the Schirmacher Oasis (Antarctica) a pyoverdin was obtained. Its structure was elucidated by chemical degradation and spectroscopic methods. The NMR data of the pyoverdin and of its Ga(III) complex were compared. Appreciable influences of the metal on the chemical shifts of the atoms at its binding sites were observed. Thus the structural elements involved in the complexation can be identified and coinciding signals of amino acids occurring more than once in the peptide chain can be separated.

scholarly journals 29Si NMR Chemical Shifts in Crystalline and Amorphous Silicon Nitrides

Materials ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 1646 ◽  
Author(s):  
Ilia Ponomarev ◽  
Peter Kroll
Keyword(s):  
Silicon Nitride ◽  
Amorphous Silicon ◽  
Density Functional ◽  
Chemical Shifts ◽  
29Si Nmr ◽  
Hydrogenated Silicon ◽  
Nmr Chemical Shifts ◽  
Nmr Data ◽  
29Si Nmr Spectra ◽  
The Impact

We investigate 29Si nuclear magnetic resonance (NMR) chemical shifts, δiso, of silicon nitride. Our goal is to relate the local structure to the NMR signal and, thus, provide the means to extract more information from the experimental 29Si NMR spectra in this family of compounds. We apply structural modeling and the gauge-included projector augmented wave (GIPAW) method within density functional theory (DFT) calculations. Our models comprise known and hypothetical crystalline Si3N4, as well as amorphous Si3N4 structures. We find good agreement with available experimental 29Si NMR data for tetrahedral Si[4] and octahedral Si[6] in crystalline Si3N4, predict the chemical shift of a trigonal-bipyramidal Si[5] to be about −120 ppm, and quantify the impact of Si-N bond lengths on 29Si δiso. We show through computations that experimental 29Si NMR data indicates that silicon dicarbodiimide, Si(NCN)2 exhibits bent Si-N-C units with angles of about 143° in its structure. A detailed investigation of amorphous silicon nitride shows that an observed peak asymmetry relates to the proximity of a fifth N neighbor in non-bonding distance between 2.5 and 2.8 Å to Si. We reveal the impact of both Si-N(H)-Si bond angle and Si-N bond length on 29Si δiso in hydrogenated silicon nitride structure, silicon diimide Si(NH)2.

scholarly journals Enriched Conformational Sampling of DNA and Proteins with a Hybrid Hamiltonian Derived from the Protein Data Bank

2018 ◽  
Vol 19 (11) ◽  
pp. 3405 ◽  
Author(s):  
Emanuel Peter ◽  
Jiří Černý
Keyword(s):  
Partition Function ◽  
Protein Data Bank ◽  
Protein Structures ◽  
Data Bank ◽  
Weighting Factor ◽  
Potential Of Mean Force ◽  
Conformational Space ◽  
Dynamics Simulation ◽  
Conformational Sampling ◽  
Speed Increase

In this article, we present a method for the enhanced molecular dynamics simulation of protein and DNA systems called potential of mean force (PMF)-enriched sampling. The method uses partitions derived from the potentials of mean force, which we determined from DNA and protein structures in the Protein Data Bank (PDB). We define a partition function from a set of PDB-derived PMFs, which efficiently compensates for the error introduced by the assumption of a homogeneous partition function from the PDB datasets. The bias based on the PDB-derived partitions is added in the form of a hybrid Hamiltonian using a renormalization method, which adds the PMF-enriched gradient to the system depending on a linear weighting factor and the underlying force field. We validated the method using simulations of dialanine, the folding of TrpCage, and the conformational sampling of the Dickerson–Drew DNA dodecamer. Our results show the potential for the PMF-enriched simulation technique to enrich the conformational space of biomolecules along their order parameters, while we also observe a considerable speed increase in the sampling by factors ranging from 13.1 to 82. The novel method can effectively be combined with enhanced sampling or coarse-graining methods to enrich conformational sampling with a partition derived from the PDB.

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