{"id":1378,"date":"2025-02-05T12:49:27","date_gmt":"2025-02-05T12:49:27","guid":{"rendered":"https:\/\/amm-journal.org\/?p=1378"},"modified":"2025-02-05T12:49:27","modified_gmt":"2025-02-05T12:49:27","slug":"machine-learning-strategies-for-aptamer","status":"publish","type":"post","link":"https:\/\/amm-journal.org\/index.php\/2025\/02\/05\/machine-learning-strategies-for-aptamer\/","title":{"rendered":"Machine learning-powered, high-affinity modification strategies for aptamers"},"content":{"rendered":"

Announcing a new publication for Acta Materia Medica<\/em> journal. The binding affinity of aptamers to targets has a crucial role in the pharmaceutical and biosensing effects. Despite diverse post-systematic evolution of ligands by exponential enrichment (post-SELEX) modifications explored in aptamer optimization, accurate prediction of high-affinity modification strategies remains challenging. Sclerostin, which antagonizes the Wnt signaling pathway, negatively regulates bone formation. The screened sclerostin aptamer was previously shown to exert bone anabolic potential. In the current study, an interactive methodology involving the exchange of mutual information between experimental endeavors and machine learning was initially proposed to design a high-affinity post-SELEX modification strategy for aptamers. After four rounds of interactive training (a total of 422 modified aptamer-target affinity datasets with diverse modification types and sites), an artificial intelligence model with high predictive accuracy with a correlation coefficient of 0.82 between the predicted and actual binding affinities was obtained. Notably, the machine learning-powered modified aptamer selected from this work exhibited 105-fold higher affinity (picomole level KD<\/sub> value) and a 3.2-folds greater Wnt-signal re-activation effect compared to naturally unmodified aptamers.<\/p>\n

This approach harnessed the power of machine learning to predict the most promising high-affinity modification strategy for aptamers.<\/p>\n

Read full open-access article: https:\/\/www.scienceopen.com\/hosted-document?doi=10.15212\/AMM-2024-0065 \u00a0<\/a><\/p>\n

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Gubu Amu, Xin Yang and Hang Luo et al. Machine learning-powered, high-affinity modification strategies for aptamers.\u00a0Acta Materia Medica.\u00a0<\/em>2025. Vol. 4(1):122-136. DOI: 10.15212\/AMM-2024-0065<\/p>\n","protected":false},"excerpt":{"rendered":"

Announcing a new publication for Acta Materia Medica journal. The binding affinity of aptamers to targets has a crucial role in the pharmaceutical and biosensing effects. Despite diverse post-systematic evolution of ligands by exponential enrichment (post-SELEX) modifications explored in aptamer optimization, accurate prediction of high-affinity modification strategies remains challenging. Sclerostin, which antagonizes the Wnt signaling […]<\/p>\n","protected":false},"author":5,"featured_media":1379,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[404,405,403,402,406,407],"class_list":["post-1378","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-and-events","tag-aptamer","tag-high-affinity","tag-machine-learning","tag-machine-learning-aided-aptamer-optimization","tag-post-selex","tag-sclerostin"],"yoast_head":"\nMachine learning-powered, high-affinity modification strategies for aptamers<\/title>\n<meta name=\"description\" content=\"Explore machine learning strategies for aptamer modification that boost binding affinity 105-fold and enhance Wnt signaling for improved bone regeneration.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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