AptaTrans: a deep neural network for predicting aptamer-protein interaction using pretrained encoders
Abstract Background Aptamers, which are biomaterials comprised of single-stranded DNA/RNA that form tertiary structures, have significant potential as next-generation materials, particularly for drug discovery. The systematic evolution of ligands by exponential enrichment (SELEX) method is a critica...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-11-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-023-05577-6 |
| _version_ | 1797413795056844800 |
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| author | Incheol Shin Keumseok Kang Juseong Kim Sanghun Sel Jeonghoon Choi Jae-Wook Lee Ho Young Kang Giltae Song |
| author_facet | Incheol Shin Keumseok Kang Juseong Kim Sanghun Sel Jeonghoon Choi Jae-Wook Lee Ho Young Kang Giltae Song |
| author_sort | Incheol Shin |
| collection | DOAJ |
| description | Abstract Background Aptamers, which are biomaterials comprised of single-stranded DNA/RNA that form tertiary structures, have significant potential as next-generation materials, particularly for drug discovery. The systematic evolution of ligands by exponential enrichment (SELEX) method is a critical in vitro technique employed to identify aptamers that bind specifically to target proteins. While advanced SELEX-based methods such as Cell- and HT-SELEX are available, they often encounter issues such as extended time consumption and suboptimal accuracy. Several In silico aptamer discovery methods have been proposed to address these challenges. These methods are specifically designed to predict aptamer-protein interaction (API) using benchmark datasets. However, these methods often fail to consider the physicochemical interactions between aptamers and proteins within tertiary structures. Results In this study, we propose AptaTrans, a pipeline for predicting API using deep learning techniques. AptaTrans uses transformer-based encoders to handle aptamer and protein sequences at the monomer level. Furthermore, pretrained encoders are utilized for the structural representation. After validation with a benchmark dataset, AptaTrans has been integrated into a comprehensive toolset. This pipeline synergistically combines with Apta-MCTS, a generative algorithm for recommending aptamer candidates. Conclusion The results show that AptaTrans outperforms existing models for predicting API, and the efficacy of the AptaTrans pipeline has been confirmed through various experimental tools. We expect AptaTrans will enhance the cost-effectiveness and efficiency of SELEX in drug discovery. The source code and benchmark dataset for AptaTrans are available at https://github.com/pnumlb/AptaTrans . |
| first_indexed | 2024-03-09T05:24:02Z |
| format | Article |
| id | doaj.art-2a42a0ea0aa746b1b187a360af037ae2 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-09T05:24:02Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-2a42a0ea0aa746b1b187a360af037ae22023-12-03T12:38:17ZengBMCBMC Bioinformatics1471-21052023-11-0124112010.1186/s12859-023-05577-6AptaTrans: a deep neural network for predicting aptamer-protein interaction using pretrained encodersIncheol Shin0Keumseok Kang1Juseong Kim2Sanghun Sel3Jeonghoon Choi4Jae-Wook Lee5Ho Young Kang6Giltae Song7Division of Artificial Intelligence, Pusan National UniversityDivision of Artificial Intelligence, Pusan National UniversityDivision of Artificial Intelligence, Pusan National UniversityDivision of Artificial Intelligence, Pusan National UniversityDivision of Artificial Intelligence, Pusan National UniversityResearch & DevelopmentResearch & DevelopmentDivision of Artificial Intelligence, Pusan National UniversityAbstract Background Aptamers, which are biomaterials comprised of single-stranded DNA/RNA that form tertiary structures, have significant potential as next-generation materials, particularly for drug discovery. The systematic evolution of ligands by exponential enrichment (SELEX) method is a critical in vitro technique employed to identify aptamers that bind specifically to target proteins. While advanced SELEX-based methods such as Cell- and HT-SELEX are available, they often encounter issues such as extended time consumption and suboptimal accuracy. Several In silico aptamer discovery methods have been proposed to address these challenges. These methods are specifically designed to predict aptamer-protein interaction (API) using benchmark datasets. However, these methods often fail to consider the physicochemical interactions between aptamers and proteins within tertiary structures. Results In this study, we propose AptaTrans, a pipeline for predicting API using deep learning techniques. AptaTrans uses transformer-based encoders to handle aptamer and protein sequences at the monomer level. Furthermore, pretrained encoders are utilized for the structural representation. After validation with a benchmark dataset, AptaTrans has been integrated into a comprehensive toolset. This pipeline synergistically combines with Apta-MCTS, a generative algorithm for recommending aptamer candidates. Conclusion The results show that AptaTrans outperforms existing models for predicting API, and the efficacy of the AptaTrans pipeline has been confirmed through various experimental tools. We expect AptaTrans will enhance the cost-effectiveness and efficiency of SELEX in drug discovery. The source code and benchmark dataset for AptaTrans are available at https://github.com/pnumlb/AptaTrans .https://doi.org/10.1186/s12859-023-05577-6Aptamer protein interactionTransformerPretraingStructural representationSELEX |
| spellingShingle | Incheol Shin Keumseok Kang Juseong Kim Sanghun Sel Jeonghoon Choi Jae-Wook Lee Ho Young Kang Giltae Song AptaTrans: a deep neural network for predicting aptamer-protein interaction using pretrained encoders BMC Bioinformatics Aptamer protein interaction Transformer Pretraing Structural representation SELEX |
| title | AptaTrans: a deep neural network for predicting aptamer-protein interaction using pretrained encoders |
| title_full | AptaTrans: a deep neural network for predicting aptamer-protein interaction using pretrained encoders |
| title_fullStr | AptaTrans: a deep neural network for predicting aptamer-protein interaction using pretrained encoders |
| title_full_unstemmed | AptaTrans: a deep neural network for predicting aptamer-protein interaction using pretrained encoders |
| title_short | AptaTrans: a deep neural network for predicting aptamer-protein interaction using pretrained encoders |
| title_sort | aptatrans a deep neural network for predicting aptamer protein interaction using pretrained encoders |
| topic | Aptamer protein interaction Transformer Pretraing Structural representation SELEX |
| url | https://doi.org/10.1186/s12859-023-05577-6 |
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