Aubailly Simon, Piazza Francesco
Université d'Orléans, Centre de Biophysique Moléculaire, CNRS-UPR4301, Rue C. Sadron, 45071, Orléans, France.
Sci Rep. 2015 Oct 8;5:14874. doi: 10.1038/srep14874.
Predicting function-related amino acids in proteins with unknown function or unknown allosteric binding sites in drug-targeted proteins is a task of paramount importance in molecular biomedicine. In this paper we introduce a simple, light and computationally inexpensive structure-based method to identify catalytic sites in enzymes. Our method, termed cutoff lensing, is a general procedure consisting in letting the cutoff used to build an elastic network model increase to large values. A validation of our method against a large database of annotated enzymes shows that optimal values of the cutoff exist such that three different structure-based indicators allow one to recover a maximum of the known catalytic sites. Interestingly, we find that the larger the structures the greater the predictive power afforded by our method. Possible ways to combine the three indicators into a single figure of merit and into a specific sequential analysis are suggested and discussed with reference to the classic case of HIV-protease. Our method could be used as a complement to other sequence- and/or structure-based methods to narrow the results of large-scale screenings.
预测功能未知的蛋白质中与功能相关的氨基酸，或药物靶向蛋白质中未知的变构结合位点，是分子生物医学中一项至关重要的任务。在本文中，我们介绍一种简单、轻便且计算成本低的基于结构的方法来识别酶中的催化位点。我们的方法称为截止透镜法，是一个通用程序，包括让用于构建弹性网络模型的截止值增加到较大值。针对一个大型注释酶数据库对我们的方法进行验证表明，存在截止值的最佳值，使得三种不同的基于结构的指标能够让人们最多地找回已知的催化位点。有趣的是，我们发现结构越大，我们的方法所提供的预测能力就越强。结合三种指标形成单一品质因数以及进行特定顺序分析的可能方法，以HIV蛋白酶的经典案例为例进行了建议和讨论。我们的方法可以作为其他基于序列和/或基于结构的方法的补充，以缩小大规模筛选的结果范围。
