Aptamer-protein interaction prediction model based on transformer

2025/06/19 by Yan, Zhichao, Kang, Yue, Ma, Buyong
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #Biomolecules (q-bio.BM) #FOS: Biological sciences #Machine Learning in Bioinformatics #RNA and protein synthesis mechanisms

paper · pdf · doi:10.48550/arxiv.2506.16084

openalex publication_date 2025/06/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions
• Measuring the Mixing of Contextual Information in the Transformer
• UniProt: the universal protein knowledgebase in 2021
• Attention Is All You Need
• CHARMM36 all-atom additive protein force field: Validation based on comparison to NMR data

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