A Novel “Reactomics” Approach for Cancer Diagnostics

Kolusheva, Sofiya; Yossef, Rami; Kugel, Aleksandra; Hanin-Avraham, Nirit; Cohen, Meital; Rubin, Eitan; Porgador, Angel

doi:10.3390/s120505572

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…S1). [51, 52] The discriminatory performances of all three M. tuberculosis ‐DNA gyrase models (3K9F mod , 2XKK mod , and 2XCT mod ) were assessed within a CSS range of 0.90 ≤ CSS ≤ 0.99 (Supporting information, Table S3, Fig. S1) and the optimal upshot (maximal value for both the C and MCC quantifiers)[51, 52] was obtained under CSS cutoff value of (CSS = 0.99; see Table 3 and Fig.…”

Section: Resultsmentioning

confidence: 99%

Cluster‐based molecular docking study for in silico identification of novel 6‐fluoroquinolones as potential inhibitors against Mycobacterium tuberculosis

et al. 2012

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A classical protein sequence alignment and homology modeling strategy were used for building three Mycobacterium tuberculosis-DNA gyrase protein models using the available topoII-DNA-6FQ crystal structure complexes originating from different organisms. The recently determined M. tuberculosis-DNA gyrase apoprotein structures and topoII-DNA-6FQ complexes were used for defining the 6-fluoroquinolones (6-FQs) binding pockets. The quality of the generated models was initially validated by docking of the cocrystallized ligands into their binding site, and subsequently by quantitative evaluation of their discriminatory performances (identification of active/inactive 6-FQs) for a set of 145 6-FQs with known biological activity values. The M. tuberculosis-DNA gyrase model with the highest estimated discriminatory power was selected and used afterwards in an additional molecular docking experiment on a mixed combinatorial set of 427 drug-like 6-FQ analogs for which the biological activity values were predicted using a prebuilt counter-propagation artificial neural network model. A novel three-level Boolean-based [T/F (true/false)] clustering algorithm was used to assess the generated binding poses: Level 1 (geometry properties assessment), Level 2 (score-based clustering and selection of the (T)-signed highly scored Level 1 poses), and Level 3 (activity-based clustering and selection of the most "active" (T)-signed Level 2 hits). The frequency analysis of occurrence of the fragments attached at R(1) and R(7) position of the (T)-signed 6-FQs selected in Level 3 revealed several novel attractive fragments and confirmed some previous findings. We believe that this methodology could be successfully used in establishing novel possible structure-activity relationship recommendations in the 6-FQs optimization, which could be of great importance in the current antimycobacterial hit-to-lead processes.

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Section: Resultsmentioning

confidence: 99%

Cluster‐based molecular docking study for in silico identification of novel 6‐fluoroquinolones as potential inhibitors against Mycobacterium tuberculosis

et al. 2012

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“…Here, we propose that PMD can be used directly, skipping the step for annotation or identification of individual compounds, to aggregate information at the reaction level, called "PMD-based Reactomics". Noticing "reactomics" has been used in previous studies based on chromatic patterns 17 or NMR spectroscopy 18 , reactomics in this work will actually be PMD-based reactomics.…”

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confidence: 99%

Untargeted high-resolution paired mass distance data mining for retrieving general chemical relationships

Petrick

2020

Commun Chem

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Untargeted metabolomics analysis captures chemical reactions among small molecules. Common mass spectrometry-based metabolomics workflows first identify the small molecules significantly associated with the outcome of interest, then begin exploring their biochemical relationships to understand biological fate or impact. We suggest an alternative by which general chemical relationships including abiotic reactions can be directly retrieved through untargeted high-resolution paired mass distance (PMD) analysis without a priori knowledge of the identities of participating compounds. PMDs calculated from the mass spectrometry data are linked to chemical reactions obtained via data mining of small molecule and reaction databases, i.e. ‘PMD-based reactomics’. We demonstrate applications of PMD-based reactomics including PMD network analysis, source appointment of unknown compounds, and biomarker reaction discovery as complements to compound discovery analyses used in traditional untargeted workflows. An R implementation of reactomics analysis and the reaction/PMD databases is available as the pmd package.

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