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Several automation approaches have been introduced to accelerate the enzymatic synthesis of complex glycans. These methodologies have, however, provided only relatively simple oligosaccharides due to limitations of glycosyl transferase selectivity. Here, we describe an automation platform that makes it possible, for the first time, to prepare in an automated fashion sulfated polylactosamines and asymmetric multiantennary complex \u003Ci>N\u003C/i>-glycans via sequential enzymatic and chemical reaction cycles. It integrates glycosyltransferase catalyzed glycosylations, the use of the unnatural sugar nucleotide donor 5′-diphosphate-2-deoxy-2-trifluoro-\u003Ci>N\u003C/i>-acetamido-glucose (UDP-GlcNHTFA), and chemical manipulations including base-mediated trifluoroacetamido (TFA) removal, azido transfer and azido reduction, \u003Ci>tert\u003C/i>-butyloxycarbonyl (Boc) protection, acid mediated deprotection, and amine acylation. The latter transformations are important for \u003Ci>stop-and-go\u003C/i> chemoenzymatic synthetic strategies in which unnatural monosaccharides are introduced to temporarily disable specific sites from enzymatic modification. It is shown that, due to the modular architecture of glycans, a limited number of glycosyl transferases can provide access to large numbers of structurally diverse glycans. In this study, only 11 recombinant human glycosyl- and sulfo transferases were employed to prepare highly complex glycans. Removal of the Nap tag can be performed by hydrogenation to give oligosaccharides that are ready for microarray printing or bioconjugation.  ",{"alignment":66},{"alignment":60},{"id":68,"type":55,"data":69,"tunes":71},"FYm-eldTvl",{"text":70},"For details: ",{"alignment":72},{"alignment":60},{"id":74,"type":55,"data":75,"tunes":76},"9SmqI6c2GE",{"text":47},{"alignment":77},{"alignment":60},{"id":79,"type":55,"data":80,"tunes":82},"14HN3iIeRb",{"text":81},"Saptashwa Chakraborty\n\n 1, Kyle Minder 1,2, Anthony Robert Prudden 1, Geert-Jan Boons\n\n 1,2,3",{"alignment":83},{"alignment":60},{"id":85,"type":55,"data":86,"tunes":88},"y2JZgd4Xkh",{"text":87},"\n\n1) Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia 30602, United States\u003Cbr>2) Department of Chemistry, University of Georgia, Athens, Georgia 30602, United States\u003Cbr>3) Chemical Biology and Drug Discovery, Utrecht Institute for Pharmaceutical Sciences, and Bijvoet Center for Biomolecular Research, Utrecht University, 3584 CG Utrecht, The Netherlands\u003Cbr>",{"alignment":89},{"alignment":60},{"id":91,"type":55,"data":92,"tunes":94},"Kzn5_nBRPt",{"text":93},"ACS Publications\u003Cbr>https://pubs.acs.org/doi/10.1021/jacs.5c22181",{"alignment":95},{"alignment":60},{"id":97,"type":98,"data":99,"tunes":102},"alN4g0Kxqq","header",{"text":100,"level":101},"Contact us to learn more about this exciting article:\u003Ca href=\"https://www.chemspeed.com/contact/\">https://www.chemspeed.com/contact/\u003C/a>",3,{"alignment":103},{"alignment":60},"2.31.2","2026-04-28",[107,109],{"id":108,"news_id":44,"industry_id":4},1302,{"id":110,"news_id":44,"industry_id":111},1303,6,[113,116,118,121],{"id":114,"news_id":44,"workflow_id":115},1255,8,{"id":117,"news_id":44,"workflow_id":4},1256,{"id":119,"news_id":44,"workflow_id":120},1257,7,{"id":122,"news_id":44,"workflow_id":123},1258,2,{"id":4,"sort":49,"user_created":5,"date_created":125,"user_updated":126,"date_updated":127,"internal_name":128,"header_presentation":49,"cta_text":49,"cta_link":49,"preview":129,"items":130},"2025-07-03T09:39:06.000Z","396bdb39-b8ef-4bbd-ad5d-54f68f2da054","2026-04-24T06:51:26.000Z","Default Header","42627e3f-7447-4f40-8aa6-1aedfb54c7f6",[131,206,318,362,399,443],{"item":132},{"title":133,"style":134,"items":135},"Portfolio","double",[136,184],{"nova_blocks_header_submenu_items_id":137},{"title":138,"items":139},"Scalable Automation",[140,146,152,158,165,172,178],{"sort":4,"item":141},{"page_title":142,"slug":143,"menu_description":144,"menu_icon":145},"Benchtop Solutions","benchtop-crystal","Space-Saving, High-Impact","breakfast_dining",{"sort":123,"item":147},{"page_title":148,"slug":149,"menu_description":150,"menu_icon":151},"Configurable Solutions","configurable-solutions","Tailored for Your Workflow","edit_square",{"sort":101,"item":153},{"page_title":154,"slug":155,"menu_description":156,"menu_icon":157},"NMR Automation Portfolio","bruker-nmr-automation","E2E automated NMR workflows","location_automation",{"sort":159,"item":160},4,{"page_title":161,"slug":162,"menu_description":163,"menu_icon":164},"XRD Automation Portfolio","bruker-xrd-automation","E2E automated XRD Workflows","api",{"sort":166,"item":167},5,{"page_title":168,"slug":169,"menu_description":170,"menu_icon":171},"Automated \"Make\" and IR & Raman Workflows (Optics)","automated-ir-and-raman-workflows","Spectroscopy Automation","escalator",{"sort":111,"item":173},{"page_title":174,"slug":175,"menu_description":176,"menu_icon":177},"QC Packages","qc-packages","Quality Control Solutions","verified_user",{"sort":120,"item":179},{"page_title":180,"slug":181,"menu_description":182,"menu_icon":183},"Software","software-and-digitalization","Connect. 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Key processes, including knowledge accumulation for predictive design and experimental refinement and validation, are traditionally labor-insensitive and timeconsuming, making it difficult to balance accuracy and efficiency.\u003C/p>","2026-05-12",[494,496,499],{"id":495,"news_id":487,"industry_id":159},1229,{"id":497,"news_id":487,"industry_id":498},1230,10,{"id":500,"news_id":487,"industry_id":166},1231,[502,504,506,508,510],{"id":503,"news_id":487,"workflow_id":4},1187,{"id":505,"news_id":487,"workflow_id":123},1188,{"id":507,"news_id":487,"workflow_id":101},1189,{"id":509,"news_id":487,"workflow_id":120},1190,{"id":511,"news_id":487,"workflow_id":159},1191,{"id":513,"featured_news":488,"news_type":46,"title":514,"url":515,"preview":516,"target_date":517,"picture":49,"industry":518,"workflow":558},14012,"Localization, inspection, and reasoning (LIRA) module for autonomous workflows in self-driving laboratories","localization-inspection-and-reasoning-lira-module-for-autonomous-workflows-in-self-driving-laboratories","\u003Cp>Self-driving labs (SDLs) combine robotic automation with artificial intelligence (AI) to allow autonomous, high-throughput experimentation. 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