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Structural computational modeling of RNA aptamers.

Xiaojun Xu1, David D Dickey2, Shi-Jie Chen1

  • 1Department of Physics, Department of Biochemistry, and Informatics Institute, University of Missouri-Columbia, Columbia, MO 65211, United States.

Methods (San Diego, Calif.)
|March 15, 2016
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Summary
This summary is machine-generated.

This study presents a computational method to determine RNA aptamer structures, aiding in their development for precision medicine. This approach successfully truncated a therapeutic aptamer, overcoming previous limitations.

Keywords:
A9 aptamerA9g aptamerNAALADase AssayProstate specific membrane antigen (PSMA)RNA aptamersRNA secondary and tertiary structureStructural algorithmsVfold2D modelVfold3D model

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Area of Science:

  • Biochemistry
  • Molecular Biology
  • Computational Biology

Background:

  • RNA aptamers are promising biologics for personalized medicine, but their clinical translation is hindered by limited structural information.
  • Understanding RNA secondary and tertiary structures is crucial for optimizing aptamers for therapeutic applications, including manufacturing cost reduction and improved drug properties.

Purpose of the Study:

  • To develop and validate a computational modeling methodology for determining RNA aptamer sequence and structural motifs.
  • To apply this methodology to facilitate the optimization of therapeutic RNA aptamers, specifically for truncation.

Main Methods:

  • A computational modeling methodology was developed and integrated with a standard functional assay.
  • This approach was applied to determine key sequence and structural features of RNA aptamers.
  • The methodology was tested on an aptamer targeting prostate specific membrane antigen (PSMA).

Main Results:

  • The computational methodology successfully identified key sequence and structural motifs of RNA aptamers.
  • This approach enabled the truncation of a PSMA-targeting aptamer, which had previously resisted truncation efforts.
  • The optimized aptamer demonstrated potential for targeted therapy.

Conclusions:

  • The described computational modeling methodology is effective for determining RNA aptamer structures and facilitating optimization.
  • This approach can accelerate the clinical translation of therapeutic aptamers by enabling crucial post-selection modifications.
  • The methodology holds potential for optimizing a wide range of aptamers for various therapeutic applications.