Bond dissociation energies of X-H bonds in proteins
Knowledge of reliable X-H bond dissociation energies (X = C, N, O, S) for amino acids in proteins is key for studying the radical chemistry of proteins. X-H bond dissociation energies of model dipeptides were computed using the isodesmic reaction method at the BMK/6-31+G(2df,p) and G4(MP2)-6X levels...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
01 Dec 2022
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| In: |
RSC Advances
Year: 2022, Jahrgang: 12, Heft: 53, Pages: 34557-34564 |
| ISSN: | 2046-2069 |
| DOI: | 10.1039/D2RA04002F |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1039/D2RA04002F Verlag, lizenzpflichtig, Volltext: https://pubs.rsc.org/en/content/articlelanding/2022/ra/d2ra04002f 
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| Verfasserangaben: | Wojtek Treyde, Kai Riedmiller and Frauke Gräter |
| Zusammenfassung: | Knowledge of reliable X-H bond dissociation energies (X = C, N, O, S) for amino acids in proteins is key for studying the radical chemistry of proteins. X-H bond dissociation energies of model dipeptides were computed using the isodesmic reaction method at the BMK/6-31+G(2df,p) and G4(MP2)-6X levels of theory. The density functional theory values agree well with the composite-level calculations. By this high level of theory, combined with a careful choice of reference compounds and peptide model systems, our work provides a highly valuable data set of bond dissociation energies with unprecedented accuracy and comprehensiveness. It will likely prove useful to predict protein biochemistry involving radicals, e.g., by machine learning. |
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| Beschreibung: | Gesehen am 31.01.2023 |
| Beschreibung: | Online Resource |
| ISSN: | 2046-2069 |
| DOI: | 10.1039/D2RA04002F |