Datasets for Benchmarking RNA Design Algorithms.

Methods Mol Biol

Institute of Computing Science, Poznan University of Technology, Poznan, Poland.

Published: September 2024

AI Article Synopsis

  • RNA molecules are crucial for various biological processes like gene regulation and protein synthesis, and their specific structures are key to their functionality, which makes them useful in bioengineering therapeutics.
  • Recent advancements in computational tools have enhanced the ability to predict RNA sequences with specific folding properties, but there is still a need for standardized datasets to evaluate these tools.
  • This study introduces a comprehensive dataset of internal and multibranched loops from RNA structures, alongside benchmarking tests of popular RNA design algorithms using this dataset.

Article Abstract

RNA molecules play vital roles in many biological processes, such as gene regulation or protein synthesis. The adoption of a specific secondary and tertiary structure by RNA is essential to perform these diverse functions, making RNA a popular tool in bioengineering therapeutics. The field of RNA design responds to the need to develop novel RNA molecules that possess specific functional attributes. In recent years, computational tools for predicting RNA sequences with desired folding characteristics have improved and expanded. However, there is still a lack of well-defined and standardized datasets to assess these programs. Here, we present a large dataset of internal and multibranched loops extracted from PDB-deposited RNA structures that encompass a wide spectrum of design difficulties. Furthermore, we conducted benchmarking tests of widely utilized open-source RNA design algorithms employing this dataset.

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Source
http://dx.doi.org/10.1007/978-1-0716-4079-1_16DOI Listing

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