[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118894-en":3,"doc-seo-118894-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118894,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Beyond Nature’s Base Pairs - Machine Learning-Enabled Design of DNA-Stabilized Silver Nanoclusters","Sequence-encoded biomolecules such as DNA and peptides function as programmable building blocks for nanomaterials, with properties governed by nucleic acid and amino acid sequence-driven interactions. Non-natural interactions, including metal-ion coordination to nucleic acids and amino acids, expand this design space but remain difficult to predict because sequence-to-structure rules are not fully understood. This feature article reviews how data mining and machine learning address these challenges for DNA-stabilized silver nanoclusters (AgN-DNAs), covering high-throughput synthesis, characterization, and machine learning-guided DNA design for fluorescence-tuned biophotonics applications.","UC Irvine  \nUC Irvine Previously Published Works  \nTitle  \nBeyond nature's base pairs: machine learning-enabled design of DNA-stabilized silver nanoclusters  \nPermalink  \n[https://escholarship.org/uc/item/8098f7q5](https://escholarship.org/uc/item/8098f7q5)  \nJournal  \nChemical Communications, 59(69)  \nISSN  \n1359-7345  \nAuthors  \nMastracco, Peter  \nCopp, Stacy M  \nPublication Date  \n2023-08-24  \nDOI  \n10.1039/d3cc02890a  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons AttributionNonCommercial-NoDerivatives License, available at [https://creativecommons.org/licenses/by](https://creativecommons.org/licenses/by)nc-nd/4 .0/  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nBeyond Nature’s base pairs: machine learning-enabled design of DNA-stabilized silver nanoclusters†  \nPeter Mastraccoa and Stacy M. Copp‡a,b,c  \nABSTRACT: Sequence-encoded biomolecules such as DNA and peptides are powerful programmable building blocks for nanomaterials. This paradigm is enabled by decades of prior research into how nucleic acid and amino acid sequences dictate biomolecular interactions. The properties of biomolecular materials can be significantly expanded with non-natural interactions, including metal ion coordination of nucleic acids and amino acids. However, these approaches present design challenges because it is often not well-understood how biomolecular sequence dictates such non-natural interactions. This Feature Article presents a case study in overcoming challenges in biomolecular materials with emerging approaches in data mining and machine learning for chemical design. We review progress in this area for a specific class of DNA-templated metal nanomaterials with complex sequence-to-property relationships: DNA-stabilized silver nanoclusters (AgN-DNAs) with bright, sequence-tuned fluorescence colors and promise for biophotonics applications. A brief overview of machine learning concepts is presented, and high-throughput experimental synthesis and characterization of AgN-DNAs are discussed. Then, recent progress in machine learning-guided design of DNA sequences that select for specific AgN-DNA fluorescence properties is reviewed. We conclude with emerging opportunities in machine learning-guided design and discovery of AgN-DNAsand other sequence-encoded biomolecular nanomaterials.  \n1 Introduction  \nNature’s nucleic acids are powerful molecular tools for nanotechnologies. Since Ned Seeman’s seminal paper in 1982 presented the concept that oligomeric DNA and RNA can be used to build static junctions and networks, 1 researchers have become  \na Department of Materials Science and Engineering, University of California, Irvine, California 92697, United States  \nb Department of Physics and Astronomy, University of California, Irvine, California 92697, United States  \nc Department of Chemical and Biomolecular Engineering, University of California, Irvine, California 92697, United States  \n‡ [stacy.copp@uci.edu](stacy.copp@uci.edu)  \nincreasingly adept at harnessing the sequence-programmed rules of Watson-Crick-Franklin base pairs to assemble DNA nanostructures, 2 organize colloids into nanoscale architectures, 3 and create dynamic machines and computers. 4,5 (This article refers to canonical hydrogen-bonded base pairing of the natural nucleobases adenine (A), cytosine (C), guanine (G), and thymine (T) as Watson-Crick-Franklin base pairs, 6 as is becoming commonly adopted by the scientific community. 7,8 ) The diverse functionalities of DNA nanotechnologies are enabled by the degree to which scientists now understand the nature of Watson-Crick-Franklin base pairing, an understanding that has been built by decades of intense prior research on the structure and formation of the DNA duplex within the biochemistry community.  \nMore recently, nucleic acid nanotechnologies are expanding beyond the confines of the Watson-Crick-Frankli","cbCaipiNG7Yi4x5O","https://ap.wps.com/l/cbCaipiNG7Yi4x5O","pdf",4180583,1,16,"English","en",105,"# Introduction\n## Metal–nucleic acid interactions and design challenges\n## DNA-stabilized silver nanoclusters (AgN-DNAs)\n## Machine learning concepts for chemical design\n## High-throughput synthesis and characterization\n## Machine learning-guided DNA sequence design and future opportunities","[{\"question\":\"What design challenge does the article address for metal-nucleic acid interactions?\",\"answer\":\"It highlights that metal-mediated interactions are less understood than Watson-Crick-Franklin base pairs, making it difficult to achieve predictive control from biomolecular sequence to molecular conformation and properties.\"},{\"question\":\"What are DNA-stabilized silver nanoclusters (AgN-DNAs) and why are they important?\",\"answer\":\"AgN-DNAs are DNA-templated silver nanoclusters whose fluorescence colors can be tuned by sequence, with potential for biophotonics applications.\"},{\"question\":\"How do data mining and machine learning contribute to designing AgN-DNAs?\",\"answer\":\"The article describes using machine learning to model complex sequence-to-property relationships and to guide the design of DNA sequences that select for specific AgN-DNA fluorescence properties.\"}]","Beyond Nature’s Base Pairs - Machine Learning-Enabled Design of DNA-Stabilized Silver Nanoclusters | PDF",1785720822,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"beyond-natures-base-pairs-machine-learning-enabled-design-of-dna-stabilized-silver-nanoclusters","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/beyond-natures-base-pairs-machine-learning-enabled-design-of-dna-stabilized-silver-nanoclusters/118894/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What design challenge does the article address for metal-nucleic acid interactions?","Question",{"text":76,"@type":77},"It highlights that metal-mediated interactions are less understood than Watson-Crick-Franklin base pairs, making it difficult to achieve predictive control from biomolecular sequence to molecular conformation and properties.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What are DNA-stabilized silver nanoclusters (AgN-DNAs) and why are they important?",{"text":81,"@type":77},"AgN-DNAs are DNA-templated silver nanoclusters whose fluorescence colors can be tuned by sequence, with potential for biophotonics applications.",{"name":83,"@type":74,"acceptedAnswer":84},"How do data mining and machine learning contribute to designing AgN-DNAs?",{"text":85,"@type":77},"The article describes using machine learning to model complex sequence-to-property relationships and to guide the design of DNA sequences that select for specific AgN-DNA fluorescence properties.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":29,"slug":119},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]