Bernauer Julie, Poupon Anne, Azé Jérôme, Janin Joël
Yeast Structural Genomics Laboratory, IBBMC UMR CNRS 8619, Bâtiment 430, Université Paris-Sud, 91405-Orsay, France.
Phys Biol. 2005 Jun;2(2):S17-23. doi: 10.1088/1478-3975/2/2/S02.
We describe protein-protein recognition within the frame of the random energy model of statistical physics. We simulate, by docking the component proteins, the process of association of two proteins that form a complex. We obtain the energy spectrum of a set of protein-protein complexes of known three-dimensional structure by performing docking in random orientations and scoring the models thus generated. We use a coarse protein representation where each amino acid residue is replaced by its Voronoï cell, and derive a scoring function by applying the evolutionary learning program ROGER to a set of parameters measured on that representation. Taking the scores of the docking models to be interaction energies, we obtain energy spectra for the complexes and fit them to a Gaussian distribution, from which we derive physical parameters such as a glass transition temperature and a specificity transition temperature.
我们在统计物理学的随机能量模型框架内描述蛋白质-蛋白质识别。通过对接组成蛋白质，我们模拟了形成复合物的两种蛋白质的缔合过程。我们通过以随机取向进行对接并对由此生成的模型进行评分，获得了一组已知三维结构的蛋白质-蛋白质复合物的能谱。我们使用一种粗略的蛋白质表示法，其中每个氨基酸残基被其沃罗诺伊胞代替，并通过将进化学习程序ROGER应用于在该表示法上测量的一组参数来推导评分函数。将对接模型的分数视为相互作用能，我们获得了复合物的能谱并将它们拟合为高斯分布，从中我们推导出诸如玻璃化转变温度和特异性转变温度等物理参数。
