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Degtyarenko, T. V.; Ponomarchouk, V. S.; Chaura, A. G.

doi:10.1023/a:1023749317438

Cited by 5 publications

(3 citation statements)

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“…The classes and properties of the imported ontology are then available to be referenced in the importing ontology. Thus, to determine the appropriate classes and properties of the Lineage ontology, we first examined existing chemical ontologies (32,33,16) and, from this information, determined that the existing ontologies did not provide an explicit, generic structure to formally describe the various components of a chemical reaction. Such formal descriptions are needed in order to aggregate reactions into lineages by matching the products of one reaction with the reactants of another.…”

Section: Methodology Ontology Modelingmentioning

confidence: 99%

Toward Automated Inventory Modeling in Life Cycle Assessment: The Utility of Semantic Data Modeling to Predict Real-World Chemical Production

Mittal

Bailin²,

Gonzalez

et al. 2017

ACS Sustainable Chem. Eng.

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A set of coupled semantic data models, i.e., ontologies, are presented to advance a methodology toward automated inventory modeling of chemical manufacturing in life cycle assessment. The cradle-to-gate life cycle inventory for chemical manufacturing is a detailed collection of the material and energy flows associated with a chemical's supply chain. Thus, there is a need to manage data describing both the lineage (or synthesis pathway) and processing conditions for a chemical. To this end, a Lineage ontology is proposed to reveal all the synthesis steps required to produce a chemical from raw materials, such as crude oil or biomaterials, while a Process ontology is developed to manage data describing the various unit processes associated with each synthesis step. The two ontologies are coupled such that process data, which is the basis for inventory modeling, is linked to lineage data through key concepts like the chemical reaction and reaction participants. To facilitate automated inventory modeling, a series of SPARQL queries, based on the concepts of ancestor and parent, are presented to generate a lineage for a chemical of interest from a set of reaction data. The proposed ontologies and SPARQL queries are evaluated and tested using a case study of nylon-6 production. Once a lineage is established, the process ontology can be used to guide inventory modeling based on both data mining (top-down) and simulation (bottom-up) approaches. The ability to generate a cradle-to-gate life cycle for a Terms & Conditions Electronic Supporting Information files are available without a subscription to ACS Web Editions.

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Section: Methodology Ontology Modelingmentioning

confidence: 99%

Toward Automated Inventory Modeling in Life Cycle Assessment: The Utility of Semantic Data Modeling to Predict Real-World Chemical Production

Mittal

Bailin²,

Gonzalez

et al. 2017

ACS Sustainable Chem. Eng.

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“…Although, the proteins that bind to heme depict just a little fraction of the existing chemical diversity in metalloproteins that occur naturally. Over 30 percent of the entire proteins are metalloproteins [41] and they yield most of the primary biological reactions such as synthesis of DNA, nitrogen fixation and photosynthesis. Metalloproteins that occur naturally bind different metals in a broad spectrum of sites for metal binding, which coordinates either a complex metal-containing cofactor or the metal iron itself.…”

Section: Catalytic Reactions Through Nitrene Radical Species Generate...mentioning

confidence: 99%

Understanding the Chemistry of Nitrene and Highlighting its Remarkable Catalytic Capabilities as a Non-Heme Iron Enzyme

Ajayi,

Boyi

et al. 2024

AJOCS

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Nitrogen is a crucial ingredient for biological processes and is necessary for several cellular activities, including metabolic processes, nucleic acid generation, and protein synthesis. Herein we looked at the intricate chemical properties of nitrene, a molecule that contains nitrogen at its core. Nitrene, akin to carbene, exhibits unique reactivity as an electrophile due to its unpaired octet. The electrical arrangement of nitrene, namely in its most basic form as imidogen (HN), is analyzed, with an emphasis on its sp hybridization and spin density characteristics. The formation of nitrene, which is known for its strong reactivity, occurs as an intermediate species through two primary mechanisms: the photolysis or thermolysis of azides, and the decomposition of isocyanates. This study offers a concise elucidation of significant chemical occurrences involving nitrenes, such as the incorporation of C-H bonds, cycloaddition reactions, the observed phenomena of ring contraction and ring expansion in aryl nitrenes and the catalytic reactions through Nitrene radical. The final section of the paper provides a summary focused on a specific study involving the transfer of nitrene, which is assisted by a non-heme iron enzyme. The research examines the catalytic prowess of PsEFE, a non-heme iron enzyme derived from Pseudomonas savastanoi, in nitrene transfer processes. Through the utilization of directed evolution and the introduction of non-native small-molecule ligands, PsEFE demonstrated an elevated level of aziridination activity. This emphasizes the capability to enhance catalysis by modifying the reliance on ligands. This study advances the understanding of nitrene chemistry and highlights the remarkable catalytic capabilities of a non-heme iron enzyme, opening possibilities for further exploration in the area of biocatalysis with transition metals.

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“…P450-containing systems can be classified according to the number of their protein components. 10) Mitochondrial and most bacterial P450-containing systems have three components: an FAD-containing flavoprotein (ferredoxin reductase), an iron-sulphur protein (ferredoxin), and a P450.…”

mentioning

confidence: 99%

Efficient Biotransformations UsingEscherichia coliwithtolCacrABMutations Expressing Cytochrome P450 Genes

Fujii¹,

Fujii²,

Machida³

et al. 2009

Bioscience, Biotechnology, and Biochemistry

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Cited by 5 publications

References 0 publications

Toward Automated Inventory Modeling in Life Cycle Assessment: The Utility of Semantic Data Modeling to Predict Real-World Chemical Production

Toward Automated Inventory Modeling in Life Cycle Assessment: The Utility of Semantic Data Modeling to Predict Real-World Chemical Production

Understanding the Chemistry of Nitrene and Highlighting its Remarkable Catalytic Capabilities as a Non-Heme Iron Enzyme

Efficient Biotransformations UsingEscherichia coliwithtolCacrABMutations Expressing Cytochrome P450 Genes

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