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COSMO: A dynamic programming algorithm for multicriteria codon optimization.

Akito Taneda1, Kiyoshi Asai2

  • 1Graduate School of Science and Technology, Hirosaki University, Hirosaki, Aomori 036-8561, Japan.

Computational and Structural Biotechnology Journal
|July 23, 2020
PubMed
Summary

COSMO, a new dynamic programming algorithm, finds all optimal codon optimization solutions, unlike evolutionary methods. It effectively excludes forbidden sequences and accelerates gene design with constraints, outperforming genetic algorithms in solution quality and quantity.

Keywords:
Codon adaptation indexCodon optimization/deoptimizationCodon pair biasConstraintsHidden stop codonMulti-objective genetic algorithm

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Area of Science:

  • Bioinformatics
  • Molecular Biology
  • Computational Biology

Background:

  • Codon optimization is crucial for heterologous gene expression.
  • Existing evolutionary algorithms struggle with multicriteria codon optimization and lack solution guarantees.
  • User-defined forbidden motifs must be excluded during optimization.

Purpose of the Study:

  • Introduce COSMO, a novel multicriteria dynamic programming algorithm for codon optimization.
  • Obtain all Pareto-optimal solutions for codon usage, codon context, and hidden stop codons.
  • Ensure rigorous exclusion of user-prescribed forbidden sequence motifs.

Main Methods:

  • Developed a multicriteria dynamic programming algorithm (COSMO).
  • Incorporated constraints for branch-and-bound processing to accelerate CDS design.
  • Benchmarked COSMO against a multi-objective genetic algorithm (MOGA) for run-time and solution generation.

Main Results:

  • COSMO generates all Pareto-optimal solutions, rigorously excluding forbidden motifs.
  • Constraints in COSMO significantly reduce run-time for CDS design.
  • COSMO identified more Pareto-optimal solutions with higher mean hypervolume values than MOGA.

Conclusions:

  • COSMO provides a guaranteed method for finding all Pareto-optimal solutions in multicriteria codon optimization.
  • COSMO outperforms MOGA in both the number and quality of Pareto-optimal solutions.
  • The developed constraints effectively accelerate the CDS design process using COSMO.