Deryusheva Eugenia, Machulin Andrey, Nemashkalova Ekaterina, Glyakina Anna, Galzitskaya Oxana
Laboratory of New Methods for Biology, Institute for Biological Instrumentation, Russian Academy of Sciences, Pushchino, Moscow Region, Russian Federation.
Laboratory of Cytology of Microorganisms, Skryabin Institute of Biochemistry, Institute of Biochemistry and Physiology of Microorganisms, Pushchino, Moscow Region, Russian Federation.
Protein Pept Lett. 2018;25(6):589-598. doi: 10.2174/0929866525666180621143957.
Unstructured regions in proteins can vary from several amino acid residues to a completely disordered sequence. Since such regions play an important role in the protein functioning, much attention is being paid to their prediction. Special different programs are available for this purpose; however, predictions obtained vary from protein to protein.
In this article, our motivation is the investigation of the high prediction accuracy of flexible loops in G-proteins family with FoldUnfold program due to crucial functions associated with these regions.
For prediction of loops in the G-proteins we used programs as RONN, DisEMBL, Glob- Plot2, IUPred, PONDR, FoldIndex, MobiDB and FoldUnfold. As a criterion of reliability of predicting disordered regions, we have chosen comparison with the regions known from the 3D structures. Collection, data analysis and statistical analysis were performed using Python 3.3. and R version 3.2.0.
For 23 G-proteins, the FoldUnfold program predicts loops with the average precision of 60-80%. It is seen that our program enables better prediction of loop positions than other programs. Statistically significant weak negative correlation exists between the average number of closed residues according to the FoldUnfold program and the Debye-Waller factors. Investigations of the G-proteins with the posttranslational modifications revealed additional flexible properties of the residues involved in the attachment of fatty acids.
Our research demonstrates additional possibilities and the high prediction accuracy of the FoldUnfold program for prediction of flexible regions and characteristics of individual residues in different protein family.
蛋白质中的非结构化区域长度从几个氨基酸残基到完全无序的序列不等。由于这些区域在蛋白质功能中发挥着重要作用,因此对其预测受到了广泛关注。为此有专门不同的程序;然而,不同蛋白质的预测结果各不相同。
在本文中,我们的动机是研究由于与这些区域相关的关键功能,使用FoldUnfold程序对G蛋白家族中柔性环的高预测准确性。
为了预测G蛋白中的环,我们使用了RONN、DisEMBL、GlobPlot2、IUPred、PONDR、FoldIndex、MobiDB和FoldUnfold等程序。作为预测无序区域可靠性的标准,我们选择与从三维结构中已知区域进行比较。使用Python 3.3和R版本3.2.0进行数据收集、分析和统计分析。
对于23种G蛋白,FoldUnfold程序预测环的平均精度为60%-80%。可以看出,我们的程序比其他程序能更好地预测环的位置。根据FoldUnfold程序,封闭残基的平均数量与德拜-瓦勒因子之间存在统计学上显著的弱负相关。对具有翻译后修饰的G蛋白的研究揭示了参与脂肪酸附着的残基的额外柔性特性。
我们的研究证明了FoldUnfold程序在预测不同蛋白质家族中柔性区域和单个残基特征方面具有更多可能性和高预测准确性。