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<p>DNA folding thermodynamics are central to many biological processes and biotechnological applications involving base-pairing. Current methods for predicting stability from DNA sequence use nearest-neighbor models that struggle to accurately capture the diverse sequence dependence of secondary structural motifs&#xa0;beyond Watson-Crick base pairs, likely due to insufficient experimental data. In this work, we introduce a massively parallel method, Array Melt, that uses fluorescence-based quenching signals to measure the equilibrium stability of millions of DNA hairpins simultaneously on a repurposed Illumina sequencing flow cell. By leveraging this dataset of 27,732 sequences with two-state melting behaviors, we derive a NUPACK-compatible model (<i>dna24</i>), a rich parameter model that exhibits higher accuracy, and a graph neural network (GNN) model that identifies relevant interactions within DNA beyond nearest neighbors. All models show improved accuracy in predicting DNA folding thermodynamics, enabling more effective in silico design of qPCR primers, oligo hybridization probes, and DNA origami.</p>

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High-throughput DNA melt measurements enable improved models of DNA folding thermodynamics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Yuxi Ke,
  • Eesha Sharma,
  • Hannah K. Wayment-Steele,
  • Winston R. Becker,
  • Anthony Ho,
  • Emil Marklund,
  • William J. Greenleaf

摘要

DNA folding thermodynamics are central to many biological processes and biotechnological applications involving base-pairing. Current methods for predicting stability from DNA sequence use nearest-neighbor models that struggle to accurately capture the diverse sequence dependence of secondary structural motifs beyond Watson-Crick base pairs, likely due to insufficient experimental data. In this work, we introduce a massively parallel method, Array Melt, that uses fluorescence-based quenching signals to measure the equilibrium stability of millions of DNA hairpins simultaneously on a repurposed Illumina sequencing flow cell. By leveraging this dataset of 27,732 sequences with two-state melting behaviors, we derive a NUPACK-compatible model (dna24), a rich parameter model that exhibits higher accuracy, and a graph neural network (GNN) model that identifies relevant interactions within DNA beyond nearest neighbors. All models show improved accuracy in predicting DNA folding thermodynamics, enabling more effective in silico design of qPCR primers, oligo hybridization probes, and DNA origami.