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Hybridoma Technology

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DeepAptamer: Advancing high-affinity aptamer discovery with a hybrid deep learning model.

Xin Yang^1,2,3,4, Chi Ho Chan^1,2,3, Shanshan Yao^3,5

¹Institute of Integrated Bioinformedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong SAR, China.

Molecular Therapy. Nucleic Acids

|February 3, 2025

Summary

DeepAptamer, a novel hybrid neural network, identifies high-affinity oligonucleotide aptamers faster than traditional methods. This AI model significantly reduces the need for lengthy selection rounds, accelerating aptamer discovery.

Keywords:

AI DNA sequence DNA shape features MT: Oligonucleotides: Therapies and Applications SELEX aptamers drug discovery hybrid neural network

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Area of Science:

Biotechnology
Bioinformatics
Molecular Biology

Background:

Systematic evolution of ligands by exponential enrichment (SELEX) is a lengthy process for aptamer identification, often requiring 20-30 rounds.
SELEX can suffer from experimental biases and non-specific interactions, potentially excluding high-affinity aptamer candidates and leading to high failure rates.

Purpose of the Study:

To develop a computational method, DeepAptamer, for identifying high-affinity aptamer sequences from early, unenriched SELEX rounds.
To accelerate the aptamer discovery process and overcome limitations of traditional SELEX.

Main Methods:

DeepAptamer, a hybrid neural network model, integrates convolutional neural networks and bidirectional long short-term memory.
The model utilizes both sequence composition and structural features to predict aptamer binding affinities and identify binding motifs.
Trained on comprehensive SELEX data for enhanced predictive accuracy.

Main Results:

DeepAptamer demonstrated superior accuracy compared to existing models in predicting aptamer binding affinities.
The model successfully identified key nucleotides crucial for target binding.
Experimental validation confirmed DeepAptamer's ability to identify high-affinity aptamers efficiently.

Conclusions:

DeepAptamer significantly reduces the number of iterative selection rounds needed for aptamer identification, from 20-30 down to early stages.
This advancement streamlines aptamer discovery for various targets, offering broad applications in biotechnology and medicine.
DeepAptamer represents a leap forward in aptamer technology, enhancing efficiency and success rates in sequence identification.