Why 1,2-quinone derivatives are more stable than their 2,3-analogues?

Szatyłowicz, Halina; Krygowski, Tadeusz M.; Solà, Miquel; Palusiak, Marcin; Dominikowska, Justyna; Stasyuk, Olga A.; Poater, Jordi

doi:10.1007/s00214-015-1635-5

Cited by 17 publications

(11 citation statements)

References 70 publications

(86 reference statements)

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“…To gain deeper insight into the physical nature of the noncovalent interactions, a reasonable and clear description of the component interaction energies contributing to the total noncovalent interaction energy is desirable …”

Section: Resultsmentioning

confidence: 99%

Evaluating the Free Energies of Solvation and Electronic Structures of Lithium‐Ion Battery Electrolytes

2016

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Adaptive biasing force molecular dynamics simulations and density functional theory calculations were performed to understand the interaction of Li(+) with pure carbonates and ethylene carbonate (EC)-based binary mixtures. The most favorable Li carbonate cluster configurations obtained from molecular dynamics simulations were subjected to detailed structural and thermochemistry calculations on the basis of the M06-2X/6-311++G(d,p) level of theory. We report the ranking of these electrolytes on the basis of the free energies of Li-ion solvation in carbonates and EC-based mixtures. A strong local tetrahedral order involving four carbonates around the Li(+) was seen in the first solvation shell. Thermochemistry calculations revealed that the enthalpy of solvation and the Gibbs free energy of solvation of the Li(+) ion with carbonates are negative and suggested the ion-carbonate complexation process to be exothermic and spontaneous. Natural bond orbital analysis indicated that Li(+) interacts with the lone pairs of electrons on the carbonyl oxygen atom in the primary solvation sphere. These interactions lead to an increase in the carbonyl (C=O) bond lengths, as evidenced by a redshift in the vibrational frequencies [ν(C=O)] and a decrease in the electron density values at the C=O bond critical points in the primary solvation sphere. Quantum theory of atoms in molecules, localized molecular orbital energy decomposition analysis (LMO-EDA), and noncovalent interaction plots revealed the electrostatic nature of the Li(+) ion interactions with the carbonyl oxygen atoms in these complexes. On the basis of LMO-EDA, the strongest attractive interaction in these complexes was found to be the electrostatic interaction followed by polarization, dispersion, and exchange interactions. Overall, our calculations predicted EC and a binary mixture of EC/dimethyl carbonate to be appropriate electrolytes for Li-ion batteries, which complies with experiments and other theoretical results.

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Section: Resultsmentioning

confidence: 99%

Evaluating the Free Energies of Solvation and Electronic Structures of Lithium‐Ion Battery Electrolytes

2016

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“…As an aside, the quinoid fragment in KEGG Compound entry C03861 is likely mislabeled as aromatic, since quinoid fragments are standardly antiaromatic [ 32 ]. This quinoid fragment was likely mislabeled as aromatic due to the whole three-ring structure obeying Huckel’s rule.…”

Section: Resultsmentioning

confidence: 99%

Atom Identifiers Generated by a Neighborhood-Specific Graph Coloring Method Enable Compound Harmonization across Metabolic Databases

2020

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Metabolic flux analysis requires both a reliable metabolic model and reliable metabolic profiles in characterizing metabolic reprogramming. Advances in analytic methodologies enable production of high-quality metabolomics datasets capturing isotopic flux. However, useful metabolic models can be difficult to derive due to the lack of relatively complete atom-resolved metabolic networks for a variety of organisms, including human. Here, we developed a neighborhood-specific graph coloring method that creates unique identifiers for each atom in a compound facilitating construction of an atom-resolved metabolic network. What is more, this method is guaranteed to generate the same identifier for symmetric atoms, enabling automatic identification of possible additional mappings caused by molecular symmetry. Furthermore, a compound coloring identifier derived from the corresponding atom coloring identifiers can be used for compound harmonization across various metabolic network databases, which is an essential first step in network integration. With the compound coloring identifiers, 8865 correspondences between KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc compounds are detected, with 5451 of them confirmed by other identifiers provided by the two databases. In addition, we found that the Enzyme Commission numbers (EC) of reactions can be used to validate possible correspondence pairs, with 1848 unconfirmed pairs validated by commonality in reaction ECs. Moreover, we were able to detect various issues and errors with compound representation in KEGG and MetaCyc databases by compound coloring identifiers, demonstrating the usefulness of this methodology for database curation.

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“…As an aside, the quinoid fragment in KEGG Compound entry C03861 is likely mislabeled as aromatic, since quinoid fragments are standardly antiaromatic[23]. This quinoid fragment was likely mislabeled as aromatic due to the whole three-ring structure obeying Huckel’s rule.…”

Section: Resultsmentioning

confidence: 99%

Atom Identifiers Generated by a Graph Coloring Method Enable Compound Harmonization Across Metabolic Databases

Huang

Mitchell

Moseley

2020

Preprint

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Metabolic flux analysis requires both a reliable metabolic model and metabolic profiles in 15 characterizing metabolic reprogramming. Advances in analytic methodologies enable production 16of high-quality metabolomics datasets capturing isotopic flux. However, useful metabolic models 17 can be difficult to derive due to the lack of relatively complete atom-resolved metabolic networks 18 for a variety of organisms, including human. Here, we developed a graph coloring method that 19 creates unique identifiers for each atom in a compound facilitating construction of an atom-resolved 20 metabolic network. What is more, this method is guaranteed to generate the same identifier for 21 symmetric atoms, enabling automatic identification of possible additional mappings caused by 22 molecular symmetry. Furthermore, a compound coloring identifier derived from the corresponding 23 atom coloring identifiers can be used for compound harmonization across various metabolic 24 network databases, which is an essential first step in network integration. With the compound 25 coloring identifiers, 8865 correspondences between KEGG and MetaCyc compounds are detected, 26 with 5451 of them confirmed by other identifiers provided by the two databases. In addition, we 27 found that the Enzyme Commission numbers (EC) of reactions can be used to validate possible 28 correspondence pairs, with 1848 unconfirmed pairs validated by commonality in reaction ECs. 29Moreover, we were able to detect various issues and errors with compound representation in KEGG 30 and MetaCyc databases by compound coloring identifiers, demonstrating the usefulness of this 31 methodology for database curation.32 Keywords: Metabolomics; atom-resolved metabolic network; atom identifier; compound identifier; 33 database harmonization; graph theory; common subgraph isomorphism 34 35 1. Introduction 36Metabolic flux analysis is an essential approach to access metabolic phenotypes[1-2] that 37 requires both reliable metabolic profiles as well as metabolic models [3][4][5]. Advances in analytical 38 technologies like mass spectrometry (MS) and nuclear magnetic resonance (NMR) greatly 39 contribute to the detection of thousands of metabolites from biofluids, cells and tissues [6]. 40Application of those analytical techniques to stable isotope resolved metabolomics (SIRM) 41 experiments facilitates production of high-quality metabolomics datasets capturing isotopic flux 42 through cellular and systemic metabolism [7][8]. Now, the challenge is to construct meaningful 43 metabolic models from the corresponding metabolic profiles for downstream metabolic flux 44 2 of 15 analysis. A metabolic network is usually represented by compounds connected via 45 biotransformation routes [9]. Obviously, information at the atom level is not represented in such 46 metabolic networks, making it impractical to derive appropriate metabolic models for SIRM 47 datasets. However, currently there is no relatively complete atom-resolved databases of metabolic 48 networks available for human metabolis...

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Why 1,2-quinone derivatives are more stable than their 2,3-analogues?

Cited by 17 publications

References 70 publications

Evaluating the Free Energies of Solvation and Electronic Structures of Lithium‐Ion Battery Electrolytes

Evaluating the Free Energies of Solvation and Electronic Structures of Lithium‐Ion Battery Electrolytes

Atom Identifiers Generated by a Neighborhood-Specific Graph Coloring Method Enable Compound Harmonization across Metabolic Databases

Atom Identifiers Generated by a Graph Coloring Method Enable Compound Harmonization Across Metabolic Databases

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