“MS-Ready” structures for non-targeted high-resolution mass spectrometry screening studies

McEachran, Andrew D.; Mansouri, Kamel; Grulke, Christopher M.; Schymanski, Emma L.; Ruttkies, Christoph; Williams, Antony J.

doi:10.1186/s13321-018-0299-2

Cited by 67 publications

(61 citation statements)

References 41 publications

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“…The KNIME (Konstanz Information Miner) chemical structure standardization workflow developed for CERAPP was applied to the available structures and generated a total of 1,688 unique, organic, desalted QSAR-ready structures (Mansouri et al 2016a;McEachran et al 2018).…”

Section: Training Set the Toxcast™ Ar Pathway Modelmentioning

confidence: 99%

“…• Inactive agonists and antagonists were considered nonbinders. The KNIME standardization workflow referenced earlier was applied to the chemical structures (Mansouri et al 2016a;McEachran et al 2018). After removing ToxCast™ chemicals (used for the training set), the generated standard InChI codes matched 7,281 chemicals from the CoMPARA list (prediction set).…”

Section: Evaluation Set Literature Search and Curationmentioning

confidence: 99%

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CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

Mansouri

Kleinstreuer

Abdelaziz

et al. 2020

Environ Health Perspect

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BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).

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Section: Training Set the Toxcast™ Ar Pathway Modelmentioning

confidence: 99%

Section: Evaluation Set Literature Search and Curationmentioning

confidence: 99%

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

Mansouri

Kleinstreuer

Abdelaziz

et al. 2020

Environ Health Perspect

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152

140

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“…Each chemical substance within DSSTox is identified by a unique DSSTox substance identifier (DTXSID) and is also mapped to a "MS-Ready" structure corresponding to the form that would be observed by MS analysis. "MS-Ready" structures are identified by DSSTox chemical identifiers (DTXCID) [26]. The entirety of the 1269 unique ENTACT mixture substances is registered within DSSTox, with unique DTXSIDs and associated MS-Ready DTXCIDs.…”

Section: Chemical Substance Databasementioning

confidence: 99%

In silico MS/MS spectra for identifying unknowns: a critical examination using CFM-ID algorithms and ENTACT mixture samples

et al. 2020

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High-resolution mass spectrometry (HRMS) enables rapid chemical annotation via accurate mass measurements and matching of experimentally derived spectra with reference spectra. Reference libraries are generated from chemical standards and are therefore limited in size relative to known chemical space. To address this limitation, in silico spectra (i.e., MS/MS or MS2 spectra), predicted via Competitive Fragmentation Modeling-ID (CFM-ID) algorithms, were generated for compounds within the U.S. Environmental Protection Agency's (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database (totaling, at the time of analysis,~765,000 substances). Experimental spectra from EPA's Non-Targeted Analysis Collaborative Trial (ENTACT) mixtures (n = 10) were then used to evaluate the performance of the in silico spectra. Overall, MS2 spectra were acquired for 377 unique compounds from the ENTACT mixtures. Approximately 53% of these compounds were correctly identified using a commercial reference library, whereas up to 50% were correctly identified as the top hit using the in silico library. Together, the reference and in silico libraries were able to correctly identify 73% of the 377 ENTACT substances. When using the in silico spectra for candidate filtering, an examination of binary classifiers showed a true positive rate (TPR) of 0.90 associated with false positive rates (FPRs) of 0.10 to 0.85, depending on the sample and method of candidate filtering. Taken together, these findings show the abilities of in silico spectra to correctly identify true positives in complex samples (at rates comparable to those observed with reference spectra), and efficiently filter large numbers of potential false positives from further consideration.

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“…8. It is well recognized that stereoisomers can hardly be distinguished from each other by tandem mass spectral fragmentation [13,60]. But even the differentiation of constitutional isomers can be challenging.…”

Section: Multi-partner Acquisition Of New Tandem Mass Spectral Datamentioning

confidence: 99%

A European proposal for quality control and quality assurance of tandem mass spectral libraries

et al. 2020

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Background: High resolution mass spectrometry (HRMS) is being used increasingly in the context of suspect and non-targeted screening for the identification of bioorganic molecules. There is correspondingly increasing awareness that higher confidence identification will require a systematic, group effort to increase the fraction of compounds with tandem mass spectra available in central, publicly available resources. While typical suspect screening efforts will only result in tentative annotations with a moderate level of confidence, library spectral matches will yield higher confidence or even full confirmation of the identity if the reference standards are available. Results: This article first explores representative percent coverage of measured tandem mass spectra in selected major environmental suspect databases of interest in the context of human biomonitoring, demonstrating the current extensive gap between the number of potential substances of interest (up to hundreds of thousands) and measured spectra (0.57-3.6% of the total chemicals have spectral information available). Furthermore, certain datasets are benchmarked, based on previous efforts, to show the extent to which acquired experimental data were comparable between laboratories, even with HRMS instruments based on different technologies (i.e., quadrupole-quadrupole-time of flight versus ion trap/quadrupole-Orbitrap). Instruments and settings that are less comparable are also revealed, primarily linear ion trap instruments, which show distinctly lower comparability. Conclusions: Based on these efforts, harmonization guidelines for the acquisition and processing of tandem mass spectrometry data are proposed to enable European (and ideally worldwide) laboratories to contribute to common resources, without requiring extensive changes to their current in house methods.

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“MS-Ready” structures for non-targeted high-resolution mass spectrometry screening studies

Cited by 67 publications

References 41 publications

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

In silico MS/MS spectra for identifying unknowns: a critical examination using CFM-ID algorithms and ENTACT mixture samples

A European proposal for quality control and quality assurance of tandem mass spectral libraries

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