{"id":19375,"date":"2025-11-18T07:24:44","date_gmt":"2025-11-18T07:24:44","guid":{"rendered":"https:\/\/www.thermofisher.com\/blog\/analyteguru\/?p=19375"},"modified":"2025-11-18T07:24:46","modified_gmt":"2025-11-18T07:24:46","slug":"identification-of-substructures-in-unknowns-through-msn-data","status":"publish","type":"post","link":"https:\/\/www.thermofisher.com\/blog\/analyteguru\/identification-of-substructures-in-unknowns-through-msn-data\/","title":{"rendered":"Identification of Substructures in Unknowns Through MSn Data"},"content":{"rendered":"\n<p><em>This is the second part of a multi-part Library blog series. For additional information, see blog <\/em><a href=\"https:\/\/www.thermofisher.com\/blog\/analyteguru\/generating-quantitative-methods-automatically-from-spectral-library-data\/\" target=\"_blank\" rel=\"noreferrer noopener\"><em>Part 1- Generating Quantitative Methods Automatically From Spectral Library Data<\/em><\/a><em> and stay tuned for more blogs on our website.<\/em><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"740\" src=\"https:\/\/admin.acceleratingscience.com\/analyteguru\/wp-content\/uploads\/sites\/25\/2025\/11\/library-sized-1024x740.jpg\" alt=\"Lab vial on a chemical reaction diagram.\" class=\"wp-image-19377\" srcset=\"https:\/\/admin.acceleratingscience.com\/analyteguru\/wp-content\/uploads\/sites\/25\/2025\/11\/library-sized-1024x740.jpg 1024w, https:\/\/admin.acceleratingscience.com\/analyteguru\/wp-content\/uploads\/sites\/25\/2025\/11\/library-sized-300x217.jpg 300w, https:\/\/admin.acceleratingscience.com\/analyteguru\/wp-content\/uploads\/sites\/25\/2025\/11\/library-sized-768x555.jpg 768w, https:\/\/admin.acceleratingscience.com\/analyteguru\/wp-content\/uploads\/sites\/25\/2025\/11\/library-sized.jpg 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>While identification of unknowns remains a significant challenge, it has become easier over the years with the growth of large MS<sup>n<\/sup> spectral libraries. These libraries, such as <a href=\"https:\/\/www.mzcloud.org\/\" target=\"_blank\" rel=\"noreferrer noopener\">mzCloud<\/a>, can provide substructure data on unknowns.\u00a0 Such information can help us to elucidate the structure of compounds that would previously remain unknown.<\/p>\n\n\n\n<p>Substructure identification such as this takes advantage of the fact that a structure will always create the same fragments under a given set of fragmentation conditions regardless of if that structure was from MS<sup>1<\/sup> or an MS<sup>n<\/sup> level of fragmentation.&nbsp; While no library will ever contain fragmentation data on all possible chemicals, what we need with this approach is enough breadth of coverage in the various substructures commonly occurring to \u201cbuild up\u201d larger molecules.&nbsp; What that means is we can take the MS<sup>n<\/sup> data on many structurally diverse molecules and use it to identify substructures in our unknown.<\/p>\n\n\n\n<p>As an example of using MS<sup>n<\/sup> data in this fashion, figure 1 shows the query MS<sup>n<\/sup> spectra for an unknown and two of the library hits.\u00a0 The hits are from MS<sup>n<\/sup> spectra of two known compounds and the precursor of the specific MS<sup>n<\/sup> fragment spectra is shown.\u00a0 Both hits from the library provide a similar structure \u2013 an aromatic benzyne linked to either an amide or linked to a carbonyl and amine group.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"288\" src=\"https:\/\/admin.acceleratingscience.com\/analyteguru\/wp-content\/uploads\/sites\/25\/2025\/11\/Blog2_Fig1-1200x337-1-1024x288.png\" alt=\"The MS3 spectra of the unknown (A) and two matching fragment spectra from the library search along with the precursors of those spectra (B).\" class=\"wp-image-19378\" srcset=\"https:\/\/admin.acceleratingscience.com\/analyteguru\/wp-content\/uploads\/sites\/25\/2025\/11\/Blog2_Fig1-1200x337-1-1024x288.png 1024w, https:\/\/admin.acceleratingscience.com\/analyteguru\/wp-content\/uploads\/sites\/25\/2025\/11\/Blog2_Fig1-1200x337-1-300x84.png 300w, https:\/\/admin.acceleratingscience.com\/analyteguru\/wp-content\/uploads\/sites\/25\/2025\/11\/Blog2_Fig1-1200x337-1-768x216.png 768w, https:\/\/admin.acceleratingscience.com\/analyteguru\/wp-content\/uploads\/sites\/25\/2025\/11\/Blog2_Fig1-1200x337-1.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"has-text-align-center has-small-font-size\"><em>Figure 1: The MS3 spectra of the unknown (A) and two matching fragment spectra from the library search along with the precursors of those spectra (B).<\/em><\/p>\n\n\n\n<p>While in this example, the specific orientation could not be determined, we have at least a very good starting point in our structure determination by knowing at least one piece of our unknown.\u00a0 Further searches with other MS<sup>n<\/sup> spectra from our molecule may uncover other substructures bringing us close to a final identification.\u00a0 In fact, the unknown in this case was isoproturon (Figure 2) which indeed has the substructure indicated from the search.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large is-style-default\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"578\" src=\"https:\/\/admin.acceleratingscience.com\/analyteguru\/wp-content\/uploads\/sites\/25\/2025\/11\/Blog2_Fig2-1200x677-1-1024x578.png\" alt=\"Structure of Isoproturon\" class=\"wp-image-19379\" srcset=\"https:\/\/admin.acceleratingscience.com\/analyteguru\/wp-content\/uploads\/sites\/25\/2025\/11\/Blog2_Fig2-1200x677-1-1024x578.png 1024w, https:\/\/admin.acceleratingscience.com\/analyteguru\/wp-content\/uploads\/sites\/25\/2025\/11\/Blog2_Fig2-1200x677-1-300x169.png 300w, https:\/\/admin.acceleratingscience.com\/analyteguru\/wp-content\/uploads\/sites\/25\/2025\/11\/Blog2_Fig2-1200x677-1-768x433.png 768w, https:\/\/admin.acceleratingscience.com\/analyteguru\/wp-content\/uploads\/sites\/25\/2025\/11\/Blog2_Fig2-1200x677-1.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"has-text-align-center has-small-font-size\"><em>Figure 2: Structure of Isoproturon<\/em><\/p>\n\n\n\n<p>By utilizing a large library of MS<sup>n<\/sup> reference data and acquiring the same MS<sup>n<\/sup> query data on our unknowns, we can begin to build up a picture based on substructures we identify.\u00a0 Visit <a href=\"https:\/\/www.mzcloud.org\/\" target=\"_blank\" rel=\"noreferrer noopener\">mzCloud<\/a> to see the breadth of MS<sup>n<\/sup> fragmentation spectra on a diverse range of chemicals that can be used for such an approach.<\/p>\n\n\n\n<p>Visit us on\u00a0<a href=\"https:\/\/www.linkedin.com\/showcase\/72003132\/admin\/dashboard\/\" target=\"_blank\" rel=\"noreferrer noopener\"><u>LinkedIn:<\/u><\/a>\u00a0#mzCloud #MassSpectralLibraries<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This is the second part of a multi-part Library blog series. For additional information, see blog Part 1- Generating Quantitative Methods Automatically From Spectral Library Data and stay tuned for more blogs on our website. While identification of unknowns remains a significant challenge, it has become easier over the years with the growth of large<\/p>\n","protected":false},"author":1760,"featured_media":19377,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","_monsterinsights_skip_tracking":false,"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1809,34],"tags":[1810,35,587],"division":[],"class_list":{"0":"post-19375","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-mass-spectral-libraries","8":"category-mass-spectrometry","9":"tag-mass-spectral-libraries","10":"tag-mass-spectrometry","11":"tag-mzcloud","12":"entry"},"_selected_authors":[],"_selected_reviewers":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.8 (Yoast SEO v27.8) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Identification of Substructures in Unknowns Through MSn Data - AnalyteGuru<\/title>\n<meta name=\"description\" content=\"Discover how MSn spectral libraries like mzCloud enable the identification of substructures in unknown compounds. 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