Dawson W, Suzuki K, Yamamoto K
Department of Bioactive Molecules, National Institute of Infectious Diseases, 1-23-1 Toyama, Shinjuku-ku, Tokyo, 162-8640, Japan.
J Theor Biol. 2001 Dec 7;213(3):359-86. doi: 10.1006/jtbi.2001.2436.
A global strategy for estimating the entropy of long sequences of RNA is proposed to help improve the predictive capacity of RNA secondary structure dynamic programming algorithm (DPA) free energy (FE) minimization methods. These DPA strategies only consider the effects that occur in the immediate (nearest neighbor) vicinity of a given base pair (bp) in a secondary structure plot. They are therefore defined as nearest-neighbor secondary structure (NNSS) strategies. The new approach utilizes the statistical properties of the Gaussian polymer chain model to introduce both local and global contributions to the entropy of a given secondary structure. These entropic contributions are primarily a function of the persistence length of the RNA. Limits on the domain size are strongly suggested by this model and these limits are a function of both the length and the percentage of bp enclosed within a given domain. The model generalizes the penalties found in the NNSS algorithms. The approach considers the importance of flexibility in the folding and stability of RNA by considering the role of the persistence length in a biopolymer structure. The theory also suggests that molecular machinery may also take advantage of this global entropic effect to bring about catalytic effects. The applications can also be extended to protein structure calculations with some additional considerations.
提出了一种估计长RNA序列熵的全局策略，以帮助提高RNA二级结构动态规划算法（DPA）自由能（FE）最小化方法的预测能力。这些DPA策略仅考虑二级结构图中给定碱基对（bp）紧邻（最近邻）区域内发生的影响。因此，它们被定义为近邻二级结构（NNSS）策略。新方法利用高斯聚合物链模型的统计特性，对给定二级结构的熵引入局部和全局贡献。这些熵贡献主要是RNA持久长度的函数。该模型强烈暗示了结构域大小的限制，这些限制是给定结构域内bp的长度和百分比的函数。该模型概括了NNSS算法中的惩罚项。该方法通过考虑持久长度在生物聚合物结构中的作用，考虑了RNA折叠和稳定性中灵活性的重要性。该理论还表明，分子机制也可能利用这种全局熵效应来产生催化作用。经过一些额外的考虑，这些应用也可以扩展到蛋白质结构计算。
