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Factorization machine with quadratic-optimization annealing for RNA inverse folding and evaluation of binary-integer

Shuta Kikuchi1,2, Shu Tanaka3,4,5,6

  • 1Graduate School of Science and Technology, Keio University, Yokohama, Kanagawa, 223-8522, Japan. kikuchi.shuta@keio.jp.

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Summary
This summary is machine-generated.

This study introduces a new method for RNA inverse folding using factorization machines with quadratic-optimization annealing (FMQA). Domain-wall encoding, particularly with guanine and cytosine at boundaries, yields more stable RNA structures with fewer sequence evaluations.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Biophysics

Background:

  • The RNA inverse folding problem seeks nucleotide sequences for specific secondary structures.
  • Existing methods often require extensive sequence evaluations, limiting practical application.
  • Optimization techniques are crucial for efficient RNA inverse folding.

Purpose of the Study:

  • To develop a novel FMQA framework for RNA inverse folding.
  • To analyze the impact of integer-to-nucleotide assignments and binary-integer encoding on FMQA performance.
  • To identify optimal encoding strategies for improved RNA secondary structure prediction.

Main Methods:

  • Implemented a factorization machine with quadratic-optimization annealing (FMQA) for RNA inverse folding.
  • Evaluated all 24 possible nucleotide-to-integer assignments (0-3).
  • Compared four binary-integer encoding methods: one-hot, domain-wall, binary, and unary.

Main Results:

  • Domain-wall and one-hot encodings outperformed binary and unary encodings based on normalized ensemble defect value.
  • Domain-wall encoding showed higher frequency for nucleotides at boundary integers (0 and 3).
  • Assigning guanine and cytosine to boundary integers in domain-wall encoding resulted in more stable RNA structures.

Conclusions:

  • The choice of encoding significantly impacts FMQA performance in RNA inverse folding.
  • Domain-wall encoding with specific nucleotide assignments offers a promising approach for designing stable RNA structures.
  • This framework reduces the need for extensive sequence evaluations in RNA design.