Fariselli Piero, Pazos Florencio, Valencia Alfonso, Casadio Rita
CIRB and Department of Biology, University of Bologna via Irnerio, Bologna, Italy.
Eur J Biochem. 2002 Mar;269(5):1356-61. doi: 10.1046/j.1432-1033.2002.02767.x.
In this paper we address the problem of extracting features relevant for predicting protein--protein interaction sites from the three-dimensional structures of protein complexes. Our approach is based on information about evolutionary conservation and surface disposition. We implement a neural network based system, which uses a cross validation procedure and allows the correct detection of 73% of the residues involved in protein interactions in a selected database comprising 226 heterodimers. Our analysis confirms that the chemico-physical properties of interacting surfaces are difficult to distinguish from those of the whole protein surface. However neural networks trained with a reduced representation of the interacting patch and sequence profile are sufficient to generalize over the different features of the contact patches and to predict whether a residue in the protein surface is or is not in contact. By using a blind test, we report the prediction of the surface interacting sites of three structural components of the Dnak molecular chaperone system, and find close agreement with previously published experimental results. We propose that the predictor can significantly complement results from structural and functional proteomics.
在本文中，我们探讨了从蛋白质复合物的三维结构中提取与预测蛋白质-蛋白质相互作用位点相关特征的问题。我们的方法基于进化保守性和表面布局的信息。我们实现了一个基于神经网络的系统，该系统使用交叉验证程序，在一个包含226个异源二聚体的选定数据库中，能够正确检测出参与蛋白质相互作用的73%的残基。我们的分析证实，相互作用表面的化学物理性质很难与整个蛋白质表面的性质区分开来。然而，用相互作用斑块和序列谱的简化表示训练的神经网络足以概括接触斑块的不同特征，并预测蛋白质表面的一个残基是否处于接触状态。通过使用盲测，我们报告了对Dnak分子伴侣系统三个结构组分的表面相互作用位点的预测，并发现与先前发表的实验结果高度一致。我们提出，该预测器可以显著补充结构和功能蛋白质组学的结果。
