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mzLogic Data Analysis Algorithm
Accelerate unknown small molecule identification with mzLogic
The mzLogic data analysis algorithm addresses one of the biggest challenges for small molecule characterization and identification—when, despite having sufficient high-quality and high-resolution MSn mass spectrometric data, there is no match in any spectral library, meaning you have an unknown.
mzLogic combines the extensive fragmentation information available within the mzCloud online advanced mass spectral library with the information in numerous online structural databases. As a result, mzLogic allows you to take thousands of potential structural candidates and automatically rank them based upon spectral similarity and sub-structural information – delivering confident candidates for your true unknowns.
Key benefits of the mzLogic algorithm
| Combine the benefits of multiple libraries and databases | Take lengthy guesswork out of unknown identification | Go from thousands of potential candidates to a handful |
|---|---|---|
| The ability of mzLogic to combine the extensive, high-quality and high-resolution accurate mass, multi-stage (MSn) fragmentation information in mzCloud, with numerous online structural databases provides the greatest chance to confidently identify unknowns. | mzLogic uses the data from mzCloud and multiple other data sources to identify common sub-structural features from your MSn data, explaining as much of your fragmentation data as possible before the next stages of identification. | Using elemental composition to search structural databases can return thousands of potential candidates; mzLogic uses MSn data and mzCloud fragmentation database information to explain as much structural information as possible, reducing thousands of potentials to a handful of confident candidates based upon structural similarity. |
When small molecule unknowns don't provide a spectral hit, how can we still identify them?
Learn more about mzLogic for unknown compound determination
mzLogic: Maximize MSn Data
Discover how mzLogic helps you to automate the process of identifying your true unknown unknowns when there is no spectral library match. Maximize your real fragmentation data by combining spectral library similarity searching with chemical database searching to rank and confidently propose the most likely candidate.
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.
High quality MS, MS/MS and MSn data can be used to interrogate the fully curated and annotated mass-spectral fragmentation library, mzCloud. Common substructural information can be used from the extensive fragmentation information to aid in unknown compound identification using mzLogic algorithm.
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.
mzLogic reduction of putative candidates using online structural databases and the extensive fragmentation information contained within mzCloud.
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:
- 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.
- The high-quality fragmentation information contained within mzCloud is compared to experimental data.
- mzLogic rapidly identifies structurally similar fragments through forward and reverse searches.
- 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.
- 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.
Confident structural elucidation using automated fragmentation annotation of MSn spectral trees. Fragmentation pathways can also be predicted based upon submission of a structure and makes full use of the HighChem Fragmentation Library. This library contains information from more than 52,000 fragmentation schemes, 217,000 individual reactions, 256,000 chemical structures and 216,000 decoded mechanisms from peer reviewed literature.
Accelerate your small molecule unknown identification studies with mzCloud and mzLogic, which leverage the streamlined data analysis and mass spectral prediction tools contained within the Compound Discoverer software and the Mass Frontier software.
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How was the world's largest mass spectral fragmentation library, mzCloud, created?
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