Dataset Information

Biologically relevant oxidants and terminology, classification and nomenclature of oxidatively generated damage to nucleobases and 2-deoxyribose in nucleic acids.

ABSTRACT: A broad scientific community is involved in investigations aimed at delineating the mechanisms of formation and cellular processing of oxidatively generated damage to nucleic acids. Perhaps as a consequence of this breadth of research expertise, there are nomenclature problems for several of the oxidized bases including 8-oxo-7,8-dihydroguanine (8-oxoGua), a ubiquitous marker of almost every type of oxidative stress in cells. Efforts to standardize the nomenclature and abbreviations of the main DNA degradation products that arise from oxidative pathways are reported. Information is also provided on the main oxidative radicals, non-radical oxygen species, one-electron agents and enzymes involved in DNA degradation pathways as well in their targets and reactivity. A brief classification of oxidatively generated damage to DNA that may involve single modifications, tandem base modifications, intrastrand and interstrand cross-links together with DNA-protein cross-links and base adducts arising from the addition of lipid peroxides breakdown products is also included.

SUBMITTER: Cadet J

PROVIDER: S-EPMC3864884 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Biologically relevant oxidants and terminology, classification and nomenclature of oxidatively generated damage to nucleobases and 2-deoxyribose in nucleic acids.

Cadet Jean J Loft Steffen S Olinski Ryszard R Evans Mark D MD Bialkowski Karol K Richard Wagner J J Dedon Peter C PC Møller Peter P Greenberg Marc M MM Cooke Marcus S MS

Free radical research 20120222 4

A broad scientific community is involved in investigations aimed at delineating the mechanisms of formation and cellular processing of oxidatively generated damage to nucleic acids. Perhaps as a consequence of this breadth of research expertise, there are nomenclature problems for several of the oxidized bases including 8-oxo-7,8-dihydroguanine (8-oxoGua), a ubiquitous marker of almost every type of oxidative stress in cells. Efforts to standardize the nomenclature and abbreviations of the main ...[more]

PMID: 22263561

Similar Datasets

Project description:BackgroundThe use of 3-D similarity techniques in the analysis of biological data and virtual screening is pervasive, but what is a biologically meaningful 3-D similarity value? Can one find statistically significant separation between "active/active" and "active/inactive" spaces? These questions are explored using 734,486 biologically tested chemical structures, 1,389 biological assay data sets, and six different 3-D similarity types utilized by PubChem analysis tools.ResultsThe similarity value distributions of 269.7 billion unique conformer pairs from 734,486 biologically tested compounds (all-against-all) from PubChem were utilized to help work towards an answer to the question: what is a biologically meaningful 3-D similarity score? The average and standard deviation for the six similarity measures STST-opt, CTST-opt, ComboTST-opt, STCT-opt, CTCT-opt, and ComboTCT-opt were 0.54 ± 0.10, 0.07 ± 0.05, 0.62 ± 0.13, 0.41 ± 0.11, 0.18 ± 0.06, and 0.59 ± 0.14, respectively. Considering that this random distribution of biologically tested compounds was constructed using a single theoretical conformer per compound (the "default" conformer provided by PubChem), further study may be necessary using multiple diverse conformers per compound; however, given the breadth of the compound set, the single conformer per compound results may still apply to the case of multi-conformer per compound 3-D similarity value distributions. As such, this work is a critical step, covering a very wide corpus of chemical structures and biological assays, creating a statistical framework to build upon.The second part of this study explored the question of whether it was possible to realize a statistically meaningful 3-D similarity value separation between reputed biological assay "inactives" and "actives". Using the terminology of noninactive-noninactive (NN) pairs and the noninactive-inactive (NI) pairs to represent comparison of the "active/active" and "active/inactive" spaces, respectively, each of the 1,389 biological assays was examined by their 3-D similarity score differences between the NN and NI pairs and analyzed across all assays and by assay category types. While a consistent trend of separation was observed, this result was not statistically unambiguous after considering the respective standard deviations. While not all "actives" in a biological assay are amenable to this type of analysis, e.g., due to different mechanisms of action or binding configurations, the ambiguous separation may also be due to employing a single conformer per compound in this study. With that said, there were a subset of biological assays where a clear separation between the NN and NI pairs found. In addition, use of combo Tanimoto (ComboT) alone, independent of superposition optimization type, appears to be the most efficient 3-D score type in identifying these cases.ConclusionThis study provides a statistical guideline for analyzing biological assay data in terms of 3-D similarity and PubChem structure-activity analysis tools. When using a single conformer per compound, a relatively small number of assays appear to be able to separate "active/active" space from "active/inactive" space.

Dataset Information

Biologically relevant oxidants and terminology, classification and nomenclature of oxidatively generated damage to nucleobases and 2-deoxyribose in nucleic acids.

Publications

Biologically relevant oxidants and terminology, classification and nomenclature of oxidatively generated damage to nucleobases and 2-deoxyribose in nucleic acids.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure