Browsing by Author "Duan, Wei"

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    Method, apparatus, and computer-readable medium for predicting a hybridization rate constant of a first sequence
    (2022-10-18) Zhang, Xuemeng; Fang, Zheng; Wu, Ruojia; Duan, Wei; Zhang, David; Rice University; William Marsh Rice University; United States Patent and Trademark Office
    Embodiments of methods, systems, and tangible non-transitory computer readable medium having instructions are presented. A method includes calculating a plurality of feature values for a number of bioinformatic features of the desired hybridization reaction; and calculating distances between the plurality of feature values and corresponding database rate constant values stored in a database, the database comprising a plurality of hybridization reactions having known rate constants. The method additionally includes calculating a weighted average of a logarithm of the database rate constant values, with larger weights assigned to value instances having values lower in distance to the plurality of feature values of the desired hybridization reaction; and providing the weighted average as a predicted logarithm of the rate constant of the desired hybridization reaction.
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    Predicting DNA hybridization kinetics from sequence
    (Springer Nature, 2018) Zhang, Jinny X.; Fang, John Z.; Duan, Wei; Wu, Lucia R.; Zhang, Angela W.; Dalchau, Neil; Yordanov, Boyan; Petersen, Rasmus; Phillips, Andrew; Zhang, David Yu
    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.