High-throughput DNA melt measurements enable improved models of DNA folding thermodynamics
Abstract 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 seconda...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60455-4 |
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| author | Yuxi Ke Eesha Sharma Hannah K. Wayment-Steele Winston R. Becker Anthony Ho Emil Marklund William J. Greenleaf |
| author_facet | Yuxi Ke Eesha Sharma Hannah K. Wayment-Steele Winston R. Becker Anthony Ho Emil Marklund William J. Greenleaf |
| author_sort | Yuxi Ke |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-253d2984646241a88efeefd4d7c960b6 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-253d2984646241a88efeefd4d7c960b62025-08-20T03:45:31ZengNature PortfolioNature Communications2041-17232025-07-0116111910.1038/s41467-025-60455-4High-throughput DNA melt measurements enable improved models of DNA folding thermodynamicsYuxi Ke0Eesha Sharma1Hannah K. Wayment-Steele2Winston R. Becker3Anthony Ho4Emil Marklund5William J. Greenleaf6Department of Bioengineering, Stanford UniversityDepartment of Genetics, Stanford University School of MedicineDepartment of Chemistry, Stanford UniversityProgram in Biophysics, Stanford UniversityDepartment of Genetics, Stanford University School of MedicineDepartment of Genetics, Stanford University School of MedicineDepartment of Genetics, Stanford University School of MedicineAbstract 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.https://doi.org/10.1038/s41467-025-60455-4 |
| spellingShingle | Yuxi Ke Eesha Sharma Hannah K. Wayment-Steele Winston R. Becker Anthony Ho Emil Marklund William J. Greenleaf High-throughput DNA melt measurements enable improved models of DNA folding thermodynamics Nature Communications |
| title | High-throughput DNA melt measurements enable improved models of DNA folding thermodynamics |
| title_full | High-throughput DNA melt measurements enable improved models of DNA folding thermodynamics |
| title_fullStr | High-throughput DNA melt measurements enable improved models of DNA folding thermodynamics |
| title_full_unstemmed | High-throughput DNA melt measurements enable improved models of DNA folding thermodynamics |
| title_short | High-throughput DNA melt measurements enable improved models of DNA folding thermodynamics |
| title_sort | high throughput dna melt measurements enable improved models of dna folding thermodynamics |
| url | https://doi.org/10.1038/s41467-025-60455-4 |
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