Aptamer-protein interaction prediction model based on transformer

TL;DR

This paper introduces SelfTrans-Ensemble, a deep learning model that combines sequence and structural features to accurately predict aptamer-protein interactions, aiding rapid aptamer screening.

Contribution

The study presents a novel deep learning framework integrating pre-trained models and structural data to improve API prediction accuracy.

Findings

Achieved 98.9% training accuracy and 88.0% test accuracy.

Incorporated structural features and data balancing for enhanced performance.

Sensitive to aptamer sequence mutations, indicating potential for mutation analysis.

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.