Ferreira Izabela, Jolley Elizabeth A, Znosko Brent M, Weber Gerald
Departamento de Física, Universidade Federal de Minas Gerais, 31270-901 Belo Horizonte, MG, Brazil.
Department of Chemistry, Saint Louis University, St. Louis, Missouri 63103, United States.
Chem Phys. 2019 May 1;521:69-76. doi: 10.1016/j.chemphys.2019.01.016. Epub 2019 Jan 17.
We calculate the nearest-neighbour enthalpies and entropies at 5 salt concentrations of 18 RNA sequences, each for at least 9 different species concentrations, totalling 757 melting temperatures, using a melting temperature optimization method. These new parameters do not need to be salt-corrected and are shown to provide overall improved melting temperature predictions. They show a marked quadratic dependence with salt concentrations which are compensated to form linear Gibbs free energies. Two different parameter schemes were tested, with fixed or variable initial parameters. We have found that using variable initial parameters provides better predictive results than using salt correction factors and that the prediction uncertainty is considerably reduced for a validation set of independent sequences. An interpolation scheme is introduced to generate model parameters for arbitrary salt concentrations which performs better against a validation set than predictions using salt corrections.
我们使用熔解温度优化方法,计算了18个RNA序列在5种盐浓度下的最近邻焓和熵,每种序列至少有9种不同的物种浓度,总共757个熔解温度。这些新参数无需进行盐校正,并且已证明能提供总体上改进的熔解温度预测。它们显示出与盐浓度有显著的二次依赖性,这种依赖性得到补偿以形成线性吉布斯自由能。测试了两种不同的参数方案,即固定初始参数或可变初始参数。我们发现,使用可变初始参数比使用盐校正因子能提供更好的预测结果,并且对于独立序列的验证集,预测不确定性显著降低。引入了一种插值方案来生成任意盐浓度下的模型参数,该方案在验证集上的表现优于使用盐校正的预测。