scholarly journals Assigning NMR spectra of irregular RNAs by heuristic algorithms

2015 ◽  
Vol 63 (1) ◽  
pp. 329-338
Author(s):  
M. Szachniuk
Keyword(s):  
Nmr Spectra ◽  
Heuristic Algorithms ◽  
Expert Knowledge ◽  
Molecular Structures ◽  
Peak Picking ◽  
Rna Structures ◽  
Processing Stage ◽  
Signal Assignment ◽  
Computer Aided Analysis ◽  
Manual Assistance

Abstract Computer-aided analysis and preprocessing of spectral data is a prerequisite for any study of molecular structures by Nuclear Magnetic Resonance (NMR) spectroscopy. The data processing stage usually involves a considerable dedication of time and expert knowledge to cope with peak picking, resonance signal assignment and calculation of structure parameters. A significant part of the latter step is performed in an automated way. However, in peak picking and resonance assignment a multistage manual assistance is still essential. The work presented here is focused on the theoretical modeling and analyzing the assignment problem by applying heuristic approaches to the NMR spectra recorded for RNA structures containing irregular regions.

Molecular structures of regioisomeric 7-arylidene hexahydroindazoles from1H NMR spectra

2009 ◽  
Vol 47 (6) ◽  
pp. 488-496 ◽  
Author(s):  
Nikolay S. Pivnenko ◽  
Alexander V. Turov ◽  
Vladimir V. Abakumov ◽  
Lidiya A. Kutulya ◽  
Svetlana V. Shishkina ◽  
...  
Keyword(s):  
Nmr Spectra ◽  
Molecular Structures

scholarly journals Evolving state grammar for modeling DNA and RNA structures

2017 ◽  
Vol 7 (1) ◽  
pp. 19 ◽  
Author(s):  
Ajay Kumar ◽  
Nidhi Kalra ◽  
Sunita Garhwal
Keyword(s):  
Molecular Structures ◽  
Type Structure ◽  
Rna Structures ◽  
Production Rules ◽  
Pseudoknot Structure ◽  
Descriptional Complexity ◽  
Dna And Rna ◽  
Pushdown Automata ◽  
State Grammar

In this paper, we represent bio-molecular structures (Attenuator, Extended Pseudoknot Structure, Kissing Hairpin, Simple H-type structure, Recursive Pseudoknot and Three-knot Structure) using state grammar. These representations will be measured using descriptional complexity point of views. Results indicate that the proposed approach is more succinct in terms of production rules and variables over the existing approaches. Another major advantage of the proposed approach is state grammar can be represented by deep pushdown automata, whereas no such automaton exists for matrix ins-del system.

Computer-aided analysis of protein NMR spectra

1991 ◽  
Vol 1 (6) ◽  
pp. 1036-1041 ◽  
Author(s):  
Jeffrey C. Hoch ◽  
Christina Redfield ◽  
Alan S. Stern
Keyword(s):  
Nmr Spectra ◽  
Protein Nmr ◽  
Computer Aided Analysis ◽  
Computer Aided

Molecular Structures from 1H NMR Spectra: Education Aided by Internet Programs

2007 ◽  
Vol 84 (3) ◽  
pp. 556 ◽  
Author(s):  
Barbara Debska ◽  
Barbara Guzowska-Swider
Keyword(s):  
1H Nmr ◽  
Nmr Spectra ◽  
Molecular Structures ◽  
1H Nmr Spectra

Purine complexes of platinum: synthesis, nmr, and crystal and molecular structures of cis-chloro(caffeine)bis(triethylphosphine)platinum(II) fluoroborate and cis-bis(isocaffeine)bis(triethylphosphine)platinum (II) fluoroborate

1983 ◽  
Vol 61 (6) ◽  
pp. 1132-1141 ◽  
Author(s):  
Gordon William Bushnell ◽  
Roderick James Densmore ◽  
Keith Roger Dixon ◽  
Arthur Charles Ralfs
Keyword(s):  
Crystal Structure ◽  
Nmr Spectra ◽  
Molecular Structures ◽  
Platinum Drugs ◽  
Orthorhombic Space Group ◽  
Space Group ◽  
Bond Lengths ◽  
Antitumour Agents ◽  
Crystal And Molecular Structures ◽  
Square Planar

Synthesis and 31P nmr spectra of the complex cations, cis-[PtCl(L)(PEt3)2]+, L= theophylline, caffeine, or isocaffeine, and cis[Pt(isocaff)2(PEt3)2]2+ are reported. The crystal structure of cis-[PtCl(caffeine)(PEt3)2][BF4] is determined, space group [Formula: see text], a = 1.1766(6), b = 1.4428(5), c = 0.9002(4) nm, α = 97.28(4)°, β = 97.69(4)°, γ = 100.96(5)°, Dm = 1.649 g cm−1, the bond lengths are Pt—Cl= 233.4(4) pm, Pt—N = 215(1) pm, Pt—P = 225.4(5) pm (mean), and the residual R = 0.071. The crystal structure of cis-[Pt(isocaffeine)2(PEt3)2][BF4]2 is orthorhombic, space group Pbca, a = 2.317(3), b = 1.717(3), c = 2.130(3) nm, Dm = 1.574 g cm−3, with an opposing isocaffeine conformation, bond lengths Pt—N = 211(2) pm, Pt—P = 227.6(9) pm (mean), and R = 0.073. Both crystal structures contain approximately square planar Pt(II) coordination with the purine coordinated via an imidazole nitrogen. The structures are discussed as models for the possible involvement of [Formula: see text] chelation of guanine to platinum when platinum drugs act as antitumour agents, but there is no evidence that isocaffeine acts as an [Formula: see text] chelate.

scholarly journals NMR-TS: De Novo Molecule Identification from NMR Spectra

2020 ◽  
Author(s):  
Jinzhe Zhang ◽  
Kei Terayama ◽  
Masato Sumita ◽  
Kazuki Yoshizoe ◽  
Kengo Ito ◽  
...  
Keyword(s):  
Density Functional ◽  
Nmr Spectra ◽  
De Novo ◽  
Expert Knowledge ◽  
Chemical Space ◽  
Automated Identification ◽  
Functional Theory ◽  
Nmr Spectrum ◽  
Chemical Structures ◽  
Target Spectrum

<div>NMR spectroscopy is an effective tool for identifying molecules in a sample. Although many previously observed NMR spectra are accumulated in public databases, they cover only a tiny fraction of the chemical space, and molecule identification is typically accomplished manually based on expert knowledge. Herein, we propose NMR-TS, a machine-learning-based python library, to automatically identify any molecule from its NMR spectrum. NMR-TS discovers candidate molecules whose NMR spectra match the target spectrum by using deep learning and density functional theory (DFT)-computed spectra. As a proof-of-concept, we identify prototypical metabolites from their computed spectra. After an average 5451 DFT runs for each spectrum, six of the nine molecules are identified correctly, and proximal molecules are obtained in the other cases. This encouraging result implies that de novo molecule generation can contribute to the fully automated identification of chemical structures. NMR-TS is available at https://github.com/tsudalab/NMR-TS. <br></div>

scholarly journals Managing the solvent water polarization to obtain improved NMR spectra of large molecular structures

2005 ◽  
Vol 32 (1) ◽  
pp. 61-70 ◽  
Author(s):  
Sebastian Hiller ◽  
Gerhard Wider ◽  
Touraj Etezady-Esfarjani ◽  
Reto Horst ◽  
Kurt Wüthrich
Keyword(s):  
Nmr Spectra ◽  
Molecular Structures ◽  
Solvent Water

Carbon-13 and nitrogen-15 NMR spectra of cis- and trans-azobenzene, 4-monosubstituted and 4,4'-disubstituted trans-azobenzenes

1982 ◽  
Vol 47 (4) ◽  
pp. 1112-1120 ◽  
Author(s):  
Antonín Lyčka
Keyword(s):  
Nmr Spectra ◽  
Chemical Shifts ◽  
Coupling Constants ◽  
Downfield Shift ◽  
Signal Assignment ◽  
In Series ◽  
Characteristic Values ◽  
The Individual ◽  
Cis And Trans ◽  
Trans Azobenzene

13C and 15N NMR spectra of cis- and trans-azobenzene, 4-substituted trans-azobenzenes (N(CH3)2; NH2; OH; OCH3; CH3; Br; NO2) and 4,4'-disubstituted trans-azobenzenes (OH; NO2; NH2; OH; N(CH3)2, CH3; N(CH3)2, NO2) were measured. In comparison with trans-azobenzene, cis-azobenzene exhibits a downfield shift of nitrogen and C(1) signals and an upfield one of the C(2) and C(4) signals. The individual coupling constants nJ(15N13C) in 4-substituted and 4,4'-disubstituted trans-azobenzenes, respectively, have characteristic values and can be used for carbon signal assignment. With 4-substituted trans-azobenzenes, the 15N substitution chemical shifts of the nitrogen of the azo-bond were determined and their additivity in series of 4,4'-disubstituted trans-azobenzenes was proved.

Automated Peak Picking and Peak Integration in Macromolecular NMR Spectra Using AUTOPSY

1998 ◽  
Vol 135 (2) ◽  
pp. 288-297 ◽  
Author(s):  
Reto Koradi ◽  
Martin Billeter ◽  
Max Engeli ◽  
Peter Güntert ◽  
Kurt Wüthrich
Keyword(s):  
Nmr Spectra ◽  
Peak Picking ◽  
Peak Integration ◽  
Automated Peak Picking
Sign in / Sign up

Export Citation Format

Share Document