Hybridization is a key molecular process in biology and biotechnology, but so far there is no predictive model for accurately determining hybridization rate constants based on sequence information. Here, we report a weighted neighbour voting (WNV) prediction algorithm, in which the hybridization rate constant of an unknown sequence is predicted based on similarity reactions with known rate constants. To construct this algorithm we first performed 210 fluorescence kinetics experiments to observe the hybridization kinetics of 100 different DNA target and probe pairs (36 nt sub-sequences of the CYCS and VEGF genes) at temperatures ranging from 28 to 55 °C. Automated feature selection and weighting optimization resulted in a final six-feature WNV model, which can predict hybridization rate constants of new sequences to within a factor of 3 with ∼91% accuracy, based on leave-one-out cross-validation. Accurate prediction of hybridization kinetics allows the design of efficient probe sequences for genomics research.
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The authors thank S.X. Chen for assistance with NGS sequence alignment. This work was funded by National Institutes of Health grant R01HG008752 to D.Y.Z.
There is a patent pending on the X-probes used in this work, and a patent pending on the WNV model of hybridization rate constant prediction.
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Zhang, J., Fang, J., Duan, W. et al. Predicting DNA hybridization kinetics from sequence. Nature Chem 10, 91–98 (2018). https://doi.org/10.1038/nchem.2877
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