mzLogic Data Analysis Algorithm

Whether you are working to better characterize and understand metabolism and drug analytes, food and environmental samples, designer drugs or extractables and leachables, the use of mass spectral libraries and online databases can help identify compounds in your samples. However, with increasing crossover between compound classes, new impurities, subtle compound transformations or metabolism and degradation products, sometimes no compelling match is made to the libraries you use.

Acquiring comprehensive high-resolution accurate- mass MS/MS and MSⁿ data has been simplified with data acquisition tools such as AcquireX, and general hardware improvements. Obtaining mass spectral libraries that contain broad chemical diversity, extensive fragmentation and reliable and concise data has been the biggest challenge.

The mzCloud mass spectral database is the largest (in terms of total spectra and data per compound) publicly available online mass spectral fragmentation library. It contains high-resolution accurate-mass (HRAM) spectra, including exhaustive high-resolution MS/MS and multi-stage MSⁿ spectra. Each entry contains considerable metadata and, most importantly, has been extensively curated (i.e., filtered, recalibrated, averaged, and annotated) to provide absolute confidence in the quality of its contents.

Each mzCloud library entry contains extensive fragmentation information, which has been acquired using both a range of collision energies and fragmentation techniques (collision induced and higher energy collisional dissociation, CID and HCD). When this information is combined with the broad chemical diversity of library entries, the likelihood that some or all of an unknown structure can be matched against an existing fragment within the library increases.

As shown in the figure above, mzLogic is available as part of the Mass Frontier software and Compound Discoverer software packages, and works as follows:

1. Online databases, such as ChemSpider, which contains more than 70 million structures, can be searched using an elemental composition. Even with the ability to use the high-resolution and accurate mass capabilities of Orbitrap mass analyzers to reveal fine isotopic information, many hundreds or even thousands of potential structures could match the elemental composition.
2. The high-quality fragmentation information contained within mzCloud is compared to experimental data.
3. mzLogic rapidly identifies structurally similar fragments through forward and reverse searches.
4. Based upon the identified sub-structures, the structural candidates from the online search are ranked in order of how much of the structure can be explained through the sub-structural matching.
5. This ranking based upon the use of real data, takes many hundreds or thousands of potential candidates and reduces them to a significantly smaller list of putative structures, with visualization tools to explain the ranking and structure accounted for by library fragments.

The ranked results from using mzLogic will either show that all the structure can be accounted for in that candidate or that there is still some structural information from the proposed candidate that cannot be accounted for. MS/MS and/or MSⁿ fragmentation spectra typically contains substantial amounts of structural information, which can be further investigated using Compound Discoverer or Mass Frontier software.

Where a complete, in-depth analysis of MSⁿ fragmentation data is required, Mass Frontier software provides visualization and understanding of fragmentation pathways using extensive MSⁿ data. The Fragments and Mechanism tool simulates unimolecular dissociation of the proposed structures, including rearrangements and predicted fragments, and can be used to explain the experimental fragmentation data through fragment annotations to help identify which candidates are indeed plausible. Rather than coded fragmentation algorithms, Mass Frontier software bases fragmentation on mechanisms published in more than 95% of all peer-reviewed scientific journals.

This approach adds greater certainty to putative structure identification through the deep fragmentation and characterization information provided by the high-quality data and extensive spectral trees in mzCloud.

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