Methods for predicting DNA curvature have many possible applications. Dinucleotide step models describe DNA shape by characterization of helical twist, deflection angles and the direction of deflection for nearest neighbor base pairs. Liu and Beveridge have extended previous applications of dinucleotide step models with the development and qualitative validation of a predictive method for sequence-dependent DNA curvature (the LB model). We tested whether the LB model accurately predicts experimentally deduced curvature angles and helical repeat parameters for DNA sequences not in its training set, particularly when challenged with quantitative data and subtle sequence phasings. We examined a series of 17 well-characterized DNA sequences to compare electrophoretic and computational results. The LB model is superior to two other models in the prediction of helical repeat parameters. We observed a strong linear correlation between curvature magnitudes predicted using the LB model and those determined by electrophoretic ligation ladder experiments, although the LB model somewhat underestimated apparent curvature. With longer electrophoretic phasing probes the LB model slightly overestimated gel mobility anomalies, with modest deviations in predicted helical repeat parameters. Overall, our analyses suggest that the LB model provides reasonably accurate predictions for the electrophoretic behavior of DNA.
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