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Selection of Aptamers for Amyloid β-Protein, the Causative Agent of Alzheimer's Disease

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Machine Learning Directed Aptamer Search from Conserved Primary Sequences and Secondary Structures.

Javier Perez Tobia¹, Po-Jung Jimmy Huang², Yuzhe Ding²

¹Department of Computer Science, University of British Columbia, Kelowna, British Columbia V1V 1V7, Canada.

ACS Synthetic Biology

|January 3, 2023

Summary

This summary is machine-generated.

A new algorithm predicts aptamer sequences by analyzing conserved primary sequences and secondary structures. This method accurately identifies aptamers, even in complex libraries, accelerating aptamer discovery for biosensors and chemical biology.

Keywords:

SELEX aptamers artificial intelligence isothermal titration calorimetry machine learning

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Area of Science:

Biochemistry
Computational Biology
Molecular Biology

Background:

Traditional aptamer prediction relies on sequence alignment and motif comparison.
Aptamers often exhibit conserved secondary structures, like hairpins, despite sequence variability.
Existing methods struggle with complex aptamer selection libraries.

Purpose of the Study:

To develop a novel algorithm for aptamer sequence prediction incorporating secondary structure information.
To improve the accuracy and efficiency of aptamer identification from selection libraries.
To facilitate the discovery of aptamers for biosensor and chemical biology applications.

Main Methods:

Developed a new algorithm combining conserved primary sequences and secondary structure analysis.
Integrated three scoring metrics: sequence abundance, stability, and structure.
Applied the algorithm to aptamer selection libraries for caffeine and theophylline.

Main Results:

The algorithm accurately predicted abundant aptamer sequences in converged libraries.
Successfully identified and experimentally verified aptamer sequences in challenging, unconverged libraries using isothermal titration calorimetry.
Demonstrated superior performance over traditional methods in complex scenarios.

Conclusions:

The novel algorithm effectively predicts aptamer sequences by integrating primary and secondary structure information.
This approach offers a new paradigm for analyzing aptamer selection data, mimicking sequence evolution.
The algorithm has the potential to significantly reduce aptamer selection time and enhance aptamer applications.