The diversity of experimental workflows involving LC-MS/MS and the extended range of mass spectrometers tend to produce extremely variable spectra. Variability reduces the accuracy of compound identification produced by commonly available software for a spectral library search. We introduce here a new algorithm that successfully matches MS/MS spectra generated by a range of instruments, acquired under different conditions. Our algorithm called X-Rank first sorts peak intensities of a spectrum and second establishes a correlation between two sorted spectra. X-Rank then computes the probability that a rank from an experimental spectrum matches a rank from a reference library spectrum. In a training step, characteristic parameter values are generated for a given data set. We compared the efficiency of the X-Rank algorithm with the dot-product algorithm implemented by MS Search from the National Institute of Standards and Technology (NIST) on two test sets produced with different instruments. Overall the X-Rank algorithm accurately discriminates correct from wrong matches and detects more correct substances than the MS Search. Furthermore, X-Rank could correctly identify and top rank eight chemical compounds in a commercially available test mix. This confirms the ability of the algorithm to perform both a straight single-platform identification and a cross-platform library search in comparison to other tools. It also opens the possibility for efficient general unknown screening (GUS) against large compound libraries.
The new software platform developed for screening and identification of small molecules (SmileMS) allows an easy and reproducible detection of drugs and toxic compounds in blood for general unknown screening. It offers automated generation of reports, which makes the LC-MS/MS easier to use without having specialised skills in mass spectrometry. This LC-MS/MS screening method will be a reliable alternative to the Remedi® instrument in the global process of screening in emergency clinical toxicology laboratories.
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