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

Abstract

Spectral 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 spectral 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 neutral loss matching depends on both the location and type of the modification. For full transparency and reproducibility, and reusability for any future comparisons of newly developed mass spectral alignment approaches, all code and benchmark data that was used in this work are freely accessible to the community.

MS2Query: Reliable and Scalable MS2 Mass Spectral-based Analogue Search

“…Current tools able to perform analogue searches often rely on a (modified) cosine score to predict chemical similarity 4,9,21 . However, a limitation of the cosine score (and its derivatives) is that small chemical modifications can, and multiple chemical modifications will, often result in a large decrease in mass spectral similarity which limits its ability to serve as a proxy for chemical similarity [22][23][24] . Recently, two machine learning-based methods were developed that outperform cosine-based scores in predicting chemical similarities from MS 2 mass spectral pairs; the unsupervised Spec2Vec 23 and the supervised MS2Deepscore 25 .…”