Machine learning-powered, high-affinity modification strategies for aptamers
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 s...
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Compuscript Ltd
2025-01-01
|
| Series: | Acta Materia Medica |
| Online Access: | https://www.scienceopen.com/hosted-document?doi=10.15212/AMM-2024-0065 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832577772948553728 |
|---|---|
| author | Gubu Amu Xin Yang Hang Luo Sifan Yu Huarui Zhang Yuan Tian Yuanyuan Yu Shijian Ding Yufei Pan Zefeng Chen Yixin He Yuan Ma Baoting Zhang Ge Zhang |
| author_facet | Gubu Amu Xin Yang Hang Luo Sifan Yu Huarui Zhang Yuan Tian Yuanyuan Yu Shijian Ding Yufei Pan Zefeng Chen Yixin He Yuan Ma Baoting Zhang Ge Zhang |
| author_sort | Gubu Amu |
| collection | DOAJ |
| description | 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. Our 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 antifcial 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 K D value) and a 3.2-folds greater Wnt-signal re-activation effect compared to naturally unmodified aptamers. This approach harnessed the power of machine learning to predict the most promising high-affinity modification strategy for aptamers. |
| format | Article |
| id | doaj-art-d4577f2d9f6940879a1c88af113fcf57 |
| institution | Kabale University |
| issn | 2737-7946 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Compuscript Ltd |
| record_format | Article |
| series | Acta Materia Medica |
| spelling | doaj-art-d4577f2d9f6940879a1c88af113fcf572025-01-30T17:00:11ZengCompuscript LtdActa Materia Medica2737-79462025-01-014112213610.15212/AMM-2024-0065Machine learning-powered, high-affinity modification strategies for aptamersGubu AmuXin YangHang LuoSifan YuHuarui ZhangYuan TianYuanyuan YuShijian DingYufei PanZefeng ChenYixin HeYuan MaBaoting ZhangGe ZhangThe 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. Our 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 antifcial 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 K D value) and a 3.2-folds greater Wnt-signal re-activation effect compared to naturally unmodified aptamers. This approach harnessed the power of machine learning to predict the most promising high-affinity modification strategy for aptamers.https://www.scienceopen.com/hosted-document?doi=10.15212/AMM-2024-0065 |
| spellingShingle | Gubu Amu Xin Yang Hang Luo Sifan Yu Huarui Zhang Yuan Tian Yuanyuan Yu Shijian Ding Yufei Pan Zefeng Chen Yixin He Yuan Ma Baoting Zhang Ge Zhang Machine learning-powered, high-affinity modification strategies for aptamers Acta Materia Medica |
| title | Machine learning-powered, high-affinity modification strategies for aptamers |
| title_full | Machine learning-powered, high-affinity modification strategies for aptamers |
| title_fullStr | Machine learning-powered, high-affinity modification strategies for aptamers |
| title_full_unstemmed | Machine learning-powered, high-affinity modification strategies for aptamers |
| title_short | Machine learning-powered, high-affinity modification strategies for aptamers |
| title_sort | machine learning powered high affinity modification strategies for aptamers |
| url | https://www.scienceopen.com/hosted-document?doi=10.15212/AMM-2024-0065 |
| work_keys_str_mv | AT gubuamu machinelearningpoweredhighaffinitymodificationstrategiesforaptamers AT xinyang machinelearningpoweredhighaffinitymodificationstrategiesforaptamers AT hangluo machinelearningpoweredhighaffinitymodificationstrategiesforaptamers AT sifanyu machinelearningpoweredhighaffinitymodificationstrategiesforaptamers AT huaruizhang machinelearningpoweredhighaffinitymodificationstrategiesforaptamers AT yuantian machinelearningpoweredhighaffinitymodificationstrategiesforaptamers AT yuanyuanyu machinelearningpoweredhighaffinitymodificationstrategiesforaptamers AT shijianding machinelearningpoweredhighaffinitymodificationstrategiesforaptamers AT yufeipan machinelearningpoweredhighaffinitymodificationstrategiesforaptamers AT zefengchen machinelearningpoweredhighaffinitymodificationstrategiesforaptamers AT yixinhe machinelearningpoweredhighaffinitymodificationstrategiesforaptamers AT yuanma machinelearningpoweredhighaffinitymodificationstrategiesforaptamers AT baotingzhang machinelearningpoweredhighaffinitymodificationstrategiesforaptamers AT gezhang machinelearningpoweredhighaffinitymodificationstrategiesforaptamers |