School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634, USA.
School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30308, USA.
Genes (Basel). 2021 Mar 25;12(4):469. doi: 10.3390/genes12040469.
Minimum free energy prediction of RNA secondary structures is based on the Nearest Neighbor Thermodynamics Model. While such predictions are typically good, the accuracy can vary widely even for short sequences, and the branching thermodynamics are an important factor in this variance. Recently, the simplest model for multiloop energetics-a linear function of the number of branches and unpaired nucleotides-was found to be the best. Subsequently, a parametric analysis demonstrated that per family accuracy can be improved by changing the weightings in this linear function. However, the extent of improvement was not known due to the ad hoc method used to find the new parameters. Here we develop a branch-and-bound algorithm that finds the set of optimal parameters with the highest average accuracy for a given set of sequences. Our analysis shows that the previous ad hoc parameters are nearly optimal for tRNA and 5S rRNA sequences on both training and testing sets. Moreover, cross-family improvement is possible but more difficult because competing parameter regions favor different families. The results also indicate that restricting the unpaired nucleotide penalty to small values is warranted. This reduction makes analyzing longer sequences using the present techniques more feasible.
RNA 二级结构的最小自由能预测基于最近邻热力学模型。虽然此类预测通常较为准确，但即使对于短序列，准确性也可能存在很大差异，而分支热力学是这种差异的重要因素。最近，人们发现多环能量的最简单模型是一个线性函数，与分支数和未配对核苷酸数成正比。随后，参数分析表明，通过改变该线性函数中的权重，可以提高每个家族的准确性。但是，由于使用了特定的方法来寻找新的参数，因此不知道改进的程度。在这里，我们开发了一种分支定界算法，可以为给定的序列集找到具有最高平均准确性的最佳参数集。我们的分析表明，对于 tRNA 和 5S rRNA 序列，以前的特定参数在训练集和测试集上都几乎是最优的。此外，跨家族的改进是可能的，但更困难，因为竞争的参数区域有利于不同的家族。结果还表明，将未配对核苷酸的罚分限制在较小的值是合理的。这种减少使得使用当前技术分析更长的序列变得更加可行。
