Nicosia Giuseppe, Stracquadanio Giovanni
Department of Mathematics and Computer Science, University of Catania, Catania, Italy.
Biophys J. 2008 Nov 15;95(10):4988-99. doi: 10.1529/biophysj.107.124016. Epub 2008 May 16.
Finding the near-native structure of a protein is one of the most important open problems in structural biology and biological physics. The problem becomes dramatically more difficult when a given protein has no regular secondary structure or it does not show a fold similar to structures already known. This situation occurs frequently when we need to predict the tertiary structure of small molecules, called peptides. In this research work, we propose a new ab initio algorithm, the generalized pattern search algorithm, based on the well-known class of Search-and-Poll algorithms. We performed an extensive set of simulations over a well-known set of 44 peptides to investigate the robustness and reliability of the proposed algorithm, and we compared the peptide conformation with a state-of-the-art algorithm for peptide structure prediction known as PEPstr. In particular, we tested the algorithm on the instances proposed by the originators of PEPstr, to validate the proposed algorithm; the experimental results confirm that the generalized pattern search algorithm outperforms PEPstr by 21.17% in terms of average root mean-square deviation, RMSD C(alpha).
找到蛋白质的近天然结构是结构生物学和生物物理学中最重要的开放性问题之一。当给定的蛋白质没有规则的二级结构，或者它没有显示出与已知结构相似的折叠时，这个问题会变得极其困难。当我们需要预测称为肽的小分子的三级结构时，这种情况经常发生。在这项研究工作中，我们基于著名的搜索与探测算法类，提出了一种新的从头算算法，即广义模式搜索算法。我们对一组著名的44种肽进行了广泛的模拟，以研究所提出算法的稳健性和可靠性，并将肽的构象与一种称为PEPstr的肽结构预测的最新算法进行了比较。特别是，我们在PEPstr的发起者提出的实例上测试了该算法，以验证所提出的算法；实验结果证实，广义模式搜索算法在平均均方根偏差RMSD C(α)方面比PEPstr性能优21.17%。
