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.
|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.|
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.
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.
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.
Download a free 60-day trial to discover for yourself how Compound Discoverer software and Mass Frontier software can harness the power of mass spectral libraries and unknown compound analysis tools, as well as numerous other valuable tools to get the most out of your data.
In this webinar we discuss a fundamental new approach to untargeted small molecule analysis involving optimized mass spectrometers, powerful new data acquisition strategies, and an arsenal of new software tools to translate high-quality Orbitrap mass spectra into more, confidently-assigned small molecule structures.
Small molecule characterization and identification clouding your decision making? Cloud-based technologies, including mass spectrometry analysis software, are becoming more prevalent in laboratories.
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