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Related Experiment Video

Updated: Jun 1, 2026

Phthalic Acid Ester-Binding DNA Aptamer Selection, Characterization, and Application to an Electrochemical Aptasensor
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Phthalic Acid Ester-Binding DNA Aptamer Selection, Characterization, and Application to an Electrochemical Aptasensor

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Selection of thrombin-binding aptamers by using computational approach for aptasensor application.

Alessandra Bini1, Marcello Mascini, Marco Mascini

  • 1Dipartimento di Chimica, Università degli Studi di Firenze, Via della Lastruccia 3, 50019 Sesto Fiorentino, Italy.

Biosensors & Bioelectronics
|June 4, 2011
PubMed
Summary
This summary is machine-generated.

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This study introduces a computational method to improve aptamer selection. Shape-based scoring accurately predicted aptamer-protein binding, streamlining the discovery of new aptamers.

Area of Science:

  • Biochemistry
  • Computational Biology
  • Molecular Biology

Background:

  • Aptamers are short DNA or RNA molecules that bind to specific targets.
  • Traditional aptamer screening is time-consuming and resource-intensive.
  • Computational methods offer a potential solution for faster aptamer discovery.

Purpose of the Study:

  • To evaluate a computational approach for studying aptamer-protein interactions.
  • To streamline the screening and selection process for new aptamers.
  • To compare computational predictions with experimental binding data.

Main Methods:

  • Generated a library of 994 mutated DNA sequences based on the thrombin binding aptamer (TBA).
  • Utilized the shape-based scoring function, ShapeGauss (OpenEye), for computational screening.

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Last Updated: Jun 1, 2026

Phthalic Acid Ester-Binding DNA Aptamer Selection, Characterization, and Application to an Electrochemical Aptasensor
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  • Incorporated selected aptamers into surface plasmon resonance (SPR) biosensors for experimental validation.
  • Compared analytical performances (signal amplitude, sensitivity, linearity, reproducibility) under reduced ionic strength.

    • Main Results:

    • Computational binding scores from ShapeGauss correlated with experimental findings.
    • SPR analysis confirmed the binding affinities of selected aptamers.
    • Reduced ionic strength conditions in experiments mimicked simulation parameters.
    • Experimental results aligned with the computational predictions.

    Conclusions:

    • Computational assisted methods, like shape-based scoring, can effectively streamline aptamer screening and selection.
    • The study validates the use of computational tools in predicting aptamer-protein interactions.
    • This approach accelerates the discovery of high-affinity aptamers for various applications.