Aptamer-protein interaction prediction model based on transformer
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| Format: | Preprint |
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2025
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| _version_ | 1866909653045084160 |
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| author | Yan, Zhichao Kang, Yue Ma, Buyong |
| author_facet | Yan, Zhichao Kang, Yue Ma, Buyong |
| contents | Aptamers are single-stranded DNA/RNAs or short peptides with unique tertiary structures that selectively bind to specific targets. They have great potential in the detection and medical fields. Here, we present SelfTrans-Ensemble, a deep learning model that integrates sequence information models and structural information models to extract multi-scale features for predicting aptamer-protein interactions (APIs). The model employs two pre-trained models, ProtBert and RNA-FM, to encode protein and aptamer sequences, along with features generated from primary sequence and secondary structural information. To address the data imbalance in the aptamer dataset imbalance, we incorporated short RNA-protein interaction data in the training set. This resulted in a training accuracy of 98.9% and a test accuracy of 88.0%, demonstrating the model's effectiveness in accurately predicting APIs. Additionally, analysis using molecular simulation indicated that SelfTrans-Ensemble is sensitive to aptamer sequence mutations. We anticipate that SelfTrans-Ensemble can offer a more efficient and rapid process for aptamer screening. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_16084 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Aptamer-protein interaction prediction model based on transformer Yan, Zhichao Kang, Yue Ma, Buyong Biomolecules Aptamers are single-stranded DNA/RNAs or short peptides with unique tertiary structures that selectively bind to specific targets. They have great potential in the detection and medical fields. Here, we present SelfTrans-Ensemble, a deep learning model that integrates sequence information models and structural information models to extract multi-scale features for predicting aptamer-protein interactions (APIs). The model employs two pre-trained models, ProtBert and RNA-FM, to encode protein and aptamer sequences, along with features generated from primary sequence and secondary structural information. To address the data imbalance in the aptamer dataset imbalance, we incorporated short RNA-protein interaction data in the training set. This resulted in a training accuracy of 98.9% and a test accuracy of 88.0%, demonstrating the model's effectiveness in accurately predicting APIs. Additionally, analysis using molecular simulation indicated that SelfTrans-Ensemble is sensitive to aptamer sequence mutations. We anticipate that SelfTrans-Ensemble can offer a more efficient and rapid process for aptamer screening. |
| title | Aptamer-protein interaction prediction model based on transformer |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2506.16084 |