Correction of precursor and product ion relative abundances in order to standardize CID spectra and improve Ecom50 accuracy for non-targeted metabolomics

Dubey, Ritvik; Hill, Dennis W.; Lai, Steven; Chen, Ming‐Hui; Grant, David F.

doi:10.1007/s11306-014-0732-0

Cited by 13 publications

(17 citation statements)

References 26 publications

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“…This allows for low energy fragmentation of molecules that undergo water loss but with the specificity of the precursor ion retained . Additional parameters such as in‐source voltage and RF voltage can also influence peak abundances of product ion spectra …”

Section: Ms/ms Search Algorithms and Scoringmentioning

confidence: 99%

“…119 Additional parameters such as in-source voltage and RF voltage can also influence peak abundances of product ion spectra. 120 There are around 300 "adduct ions" and in-source fragments that are covered in the NIST14 MS/MS database. 121 Certain substance classes such as flavonoids and nucleotides have a higher probability of in-source fragmentations.…”

Section: Reports From Several Critical Assessment Of Small Moleculementioning

confidence: 99%

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Identification of small molecules using accurate mass MS/MS search

Kind

Tsugawa

Čajka

et al. 2017

Mass Spectrometry Reviews

371

325

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Tandem mass spectral library search (MS/MS) is the fastest way to correctly annotate MS/MS spectra from screening small molecules in fields such as environmental analysis, drug screening, lipid analysis, and metabolomics. The confidence in MS/MS-based annotation of chemical structures is impacted by instrumental settings and requirements, data acquisition modes including data-dependent and data-independent methods, library scoring algorithms, as well as post-curation steps. We critically discuss parameters that influence search results, such as mass accuracy, precursor ion isolation width, intensity thresholds, centroiding algorithms, and acquisition speed. A range of publicly and commercially available MS/MS databases such as NIST, MassBank, MoNA, LipidBlast, Wiley MSforID, and METLIN are surveyed. In addition, software tools including NIST MS Search, MS-DIAL, Mass Frontier, SmileMS, Mass++, and XCMS to perform fast MS/MS search are discussed. MS/MS scoring algorithms and challenges during compound annotation are reviewed. Advanced methods such as the in silico generation of tandem mass spectra using quantum chemistry and machine learning methods are covered. Community efforts for curation and sharing of tandem mass spectra that will allow for faster distribution of scientific discoveries are discussed.

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Section: Ms/ms Search Algorithms and Scoringmentioning

confidence: 99%

Section: Reports From Several Critical Assessment Of Small Moleculementioning

confidence: 99%

Identification of small molecules using accurate mass MS/MS search

Kind

Tsugawa

Čajka

et al. 2017

Mass Spectrometry Reviews

371

325

View full text Add to dashboard Cite

show abstract

“…Tandem MS databases (containing ion intensities) would be highly useful for structure analysis, especially in proteomics [6]. The main difficulty is that tandem mass spectra strongly depend on experimental conditions, which are difficult to standardize [7]. The most important experimental parameter in tandem mass spectra is energetics [8], in most cases controlled by collision energy.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Comparison of Tandem Mass Spectra Obtained on Various Instruments

Bazsó

Ozohanics

Schlosser

et al. 2016

J. Am. Soc. Mass Spectrom.

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Abstract. The similarity between two tandem mass spectra, which were measured on different instruments, was compared quantitatively using the similarity index (SI), defined as the dot product of the square root of peak intensities in the respective spectra. This function was found to be useful for comparing energy-dependent tandem mass spectra obtained on various instruments. Spectral comparisons show the similarity index in a 2D Bheat map^, indicating which collision energy combinations result in similar spectra, and how good this agreement is. The results and methodology can be used in the pharma industry to design experiments and equipment well suited for good reproducibility. We suggest that to get good long-term reproducibility, it is best to adjust the collision energy to yield a spectrum very similar to a reference spectrum. It is likely to yield better results than using the same tuning file, which, for example, does not take into account that contamination of the ion source due to extended use may influence instrument tuning. The methodology may be used to characterize energy dependence on various instrument types, to optimize instrumentation, and to study the influence or correlation between various experimental parameters.

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“…Thus, we and others have proposed the inclusion of multiple orthogonal, analytical, and computational methods to improve the likelihood that the correct candidate is at, or near, the top of the ranked candidate list. These include augmenting GC/LC–MS data with retention time/index, − Ecom50, , ion mobility, − and biochemical substructure matching …”

Section: Introductionmentioning

confidence: 99%

High-Throughput Non-targeted Chemical Structure Identification Using Gas-Phase Infrared Spectra

et al. 2021

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The high-throughput identification of unknown metabolites in biological samples remains challenging. Most current non-targeted metabolomics studies rely on mass spectrometry, followed by computational methods that rank thousands of candidate structures based on how closely their predicted mass spectra match the experimental mass spectrum of an unknown. We reasoned that the infrared (IR) spectra could be used in an analogous manner and could add orthologous structure discrimination; however, this has never been evaluated on large data sets. Here, we present results of a high-throughput computational method for predicting IR spectra of candidate compounds obtained from the PubChem database. Predicted spectra were ranked based on their similarity to gas-phase experimental IR spectra of test compounds obtained from the NIST. Our computational workflow (IRdentify) consists of a fast semiempirical quantum mechanical method for initial IR spectra prediction, ranking, and triaging, followed by a final IR spectra prediction and ranking using density functional theory. This approach resulted in the correct identification of 47% of 258 test compounds. On average, there were 2152 candidate structures evaluated for each test compound, giving a total of approximately 555,200 candidate structures evaluated. We discuss several variables that influenced the identification accuracy and then demonstrate the potential application of this approach in three areas: (1) combining IR and mass spectra rankings into a single composite rank score, (2) identifying the precursor and fragment ions using cryogenic ion vibrational spectroscopy, and (3) the incorporation of a trimethylsilyl derivatization step to extend the method compatibility to less-volatile compounds. Overall, our results suggest that matching computational with experimental IR spectra is a potentially powerful orthogonal option for adding significant highthroughput chemical structure discrimination when used with other non-targeted chemical structure identification methods.

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Correction of precursor and product ion relative abundances in order to standardize CID spectra and improve Ecom50 accuracy for non-targeted metabolomics

Cited by 13 publications

References 26 publications

Identification of small molecules using accurate mass MS/MS search

Identification of small molecules using accurate mass MS/MS search

Quantitative Comparison of Tandem Mass Spectra Obtained on Various Instruments

High-Throughput Non-targeted Chemical Structure Identification Using Gas-Phase Infrared Spectra

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