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

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 struct...

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Vydáno v:	Nature communications Ročník 16; číslo 1; s. 5572 - 19
Hlavní autoři:	Ke, Yuxi, Sharma, Eesha, Wayment-Steele, Hannah K., Becker, Winston R., Ho, Anthony, Marklund, Emil, Greenleaf, William J.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 01.07.2025 Nature Publishing Group Nature Portfolio
Témata:	45 45/47 45/56 45/77 49 49/23 631/114/1305 631/1647/2163 631/45/147 631/45/535 631/57/2272/951 64 Accuracy Base Pairing Binding sites Biological activity Biotechnology Deoxyribonucleic acid Design DNA DNA - chemistry DNA - genetics DNA probes Folding Graph neural networks High-Throughput Nucleotide Sequencing - methods Humanities and Social Sciences Hybridization Mathematical models multidisciplinary Nearest-neighbor Neural networks Neural Networks, Computer Nucleic Acid Conformation Nucleic Acid Denaturation Nucleotide sequence Science Science (multidisciplinary) Stability Temperature Thermodynamics
ISSN:	2041-1723, 2041-1723
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Popis
Shrnutí:	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. DNA thermodynamics underlies many biological and biotechnological applications but is challenging to accurately predict. Here, the authors develop a high-throughput DNA melting method and improve computational models for predicting DNA folding energies from sequence data.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2041-1723 2041-1723
DOI:	10.1038/s41467-025-60455-4