Comparison of Cosine, Modified Cosine, and Neutral Loss Based Spectrum Alignment For Discovery of Structurally Related Molecules

Abstract

Spectrum alignment of tandem mass spectrometry (MS/MS) data using the modified cosine similarity and subsequent visualization as molecular networks have been demonstrated to be a useful strategy to discover analogs of molecules from untargeted MS/MS-based metabolomics experiments. Recently, a neutral loss matching approach has been introduced as an alternative to MS/MS-based molecular networking with an implied performance advantage in finding analogs that cannot be discovered using existing MS/MS spectrum alignment strategies. To comprehensively evaluate the scoring properties of neutral loss matching, the cosine similarity, and the modified cosine similarity, similarity measures of 955 228 peptide MS/MS spectrum pairs and 10 million small molecule MS/MS spectrum pairs were compared. This comparative analysis revealed that the modified cosine similarity outperformed neutral loss matching and the cosine similarity in all cases. The data further indicated that the performance of MS/MS spectrum alignment depends on the location and type of the modification, as well as the chemical compound class of fragmented molecules.

On the challenge of unambiguous identification of fentanyl analogs: exploring measurement diversity using standard reference mass spectral libraries

“…The similarity between any two EI full scan mass spectra can be measured using a variety of techniques [14][15][16][17][18][19][20][21][22]; one long-standing approach for approximating similarity is referred to as the dot-product, or cosine similarity. We denote the cosine similarity between mass spectra 𝑥 and 𝑦, measured with a low-resolution mass spectrometer and m/z tolerance 𝜖 #$ = 0, as 𝜃(𝑥, 𝑦, 𝜖 #$ ), and approximate the dissimilarity between any mass spectra as 𝜙 " (𝑥, 𝑦, 𝜖 #$ ) = 1 − 𝜃(𝑥, 𝑦, 𝜖 #$ ).…”

The Roots of Deguelia nitidula as a Natural Antibacterial Source against Staphylococcus aureus Strains

“…Overall, the main metabolites detected in the ethanolic extract (SREE), fractions SREFr2, were putatively annotated as flavonoid derivatives, and they are illustrated in Figure 2A. Thus, modified cosine [35] was applied to assess the MS/MS correspondence between experimental spectra and reference spectra available in GNPS libraries [18]. For instance, 5,7-Dihydroxy-8-C-geranylflavanone (Figure 2B3) showed a fragment of m/z 165 that could be generated by heterocyclic ring fission (HRF) [36] in the C-ring of the precursor ion, and pongaflavone (Figure 2B2) showed loss of CH2O (30 Da) from the pyrano.…”

Chemical Diversity and In Vitro/In Silico Antiviral Activity of Extracts from Different Parts of Phyllanthus brasiliensis (Aubl.) Poir

“…In this context, the last two decades have brought an explosion of new trends in natural products discovery such as liquid chromatography-mass spectrometry (LC-MS/MS) associated with machine learning-based tools, for instance, MolNetEnhancer [18] and ClassyFire [19], as well as MS-open databases, such as GNPS [20], have provided comprehensive support for the study of natural products. With the availability of reference spectra open databases, obtaining putative structures has become easier than a decade ago, with a high level of con dence in metabolite identi cation [21]. In addition, in silico methods have been developed to connect mass spectrometry data and in vitro bioactivity data of extracts and fractions, allowing for the in silico prediction of potential bioactivity for a single or group of metabolites.…”