{"title":"Homogeneity tests for Michaelis-Menten curves with application to fluorescence resonance energy transfer data","authors":"Amparo Ba'illo, Laura Mart'inez-Munoz, M. Mellado","doi":"10.1142/S0218339013500174","DOIUrl":null,"url":null,"abstract":"Resonance energy transfer methods are in wide use for evaluating protein-protein interactions and protein conformational changes in living cells. Fluorescence resonance energy transfer (FRET) measures energy transfer as a function of the acceptor:donor ratio, generating FRET saturation curves. Modeling these curves by Michaelis-Menten kinetics allows characterization by two parameters, which serve to evaluate apparent affinity between two proteins and to compare this affinity in different experimental conditions. To reduce the effect of sampling variability, several statistical samples of the saturation curve are generated in the same biological conditions. Here we study three procedures to determine whether statistical samples in a collection are homogeneous, in the sense that they are extracted from the same regression model. From the hypothesis testing viewpoint, we considered an F test and a procedure based on bootstrap resampling. The third method analyzed the problem from the model selection viewpoint, and used the Akaike information criterion (AIC). Although we only considered the Michaelis-Menten model, all statistical procedures would be applicable to any other nonlinear regression model. We compared the performance of the homogeneity testing methods in a Monte Carlo study and through analysis in living cells of FRET saturation curves for dimeric complexes of CXCR4, a seven-transmembrane receptor of the G protein-coupled receptor family. We show that the F test, the bootstrap procedure and the model selection method lead in general to similar conclusions, although AIC gave the best results when sample sizes were small, whereas the F test and the bootstrap method were more appropriate for large samples. In practice, all three methods are easy to use simultaneously and show consistency, facilitating conclusions on sample homogeneity.","PeriodicalId":8447,"journal":{"name":"arXiv: Biomolecules","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2011-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv: Biomolecules","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/S0218339013500174","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Resonance energy transfer methods are in wide use for evaluating protein-protein interactions and protein conformational changes in living cells. Fluorescence resonance energy transfer (FRET) measures energy transfer as a function of the acceptor:donor ratio, generating FRET saturation curves. Modeling these curves by Michaelis-Menten kinetics allows characterization by two parameters, which serve to evaluate apparent affinity between two proteins and to compare this affinity in different experimental conditions. To reduce the effect of sampling variability, several statistical samples of the saturation curve are generated in the same biological conditions. Here we study three procedures to determine whether statistical samples in a collection are homogeneous, in the sense that they are extracted from the same regression model. From the hypothesis testing viewpoint, we considered an F test and a procedure based on bootstrap resampling. The third method analyzed the problem from the model selection viewpoint, and used the Akaike information criterion (AIC). Although we only considered the Michaelis-Menten model, all statistical procedures would be applicable to any other nonlinear regression model. We compared the performance of the homogeneity testing methods in a Monte Carlo study and through analysis in living cells of FRET saturation curves for dimeric complexes of CXCR4, a seven-transmembrane receptor of the G protein-coupled receptor family. We show that the F test, the bootstrap procedure and the model selection method lead in general to similar conclusions, although AIC gave the best results when sample sizes were small, whereas the F test and the bootstrap method were more appropriate for large samples. In practice, all three methods are easy to use simultaneously and show consistency, facilitating conclusions on sample homogeneity.
共振能量转移方法被广泛用于评估活细胞中蛋白质相互作用和蛋白质构象变化。荧光共振能量转移(FRET)测量能量转移作为受体:供体比例的函数,产生FRET饱和曲线。通过Michaelis-Menten动力学对这些曲线进行建模,可以通过两个参数进行表征,这两个参数用于评估两种蛋白质之间的表观亲和力,并在不同的实验条件下比较这种亲和力。为了减少采样变异性的影响,在相同的生物条件下,生成了饱和度曲线的多个统计样本。在这里,我们研究了三个程序来确定统计样本是否在一个集合是同质的,在某种意义上说,他们是从相同的回归模型中提取的。从假设检验的观点来看,我们考虑了一个F检验和一个基于自举重采样的过程。第三种方法从模型选择的角度分析问题,采用了赤池信息准则(Akaike information criterion, AIC)。虽然我们只考虑Michaelis-Menten模型,但所有的统计过程都适用于任何其他非线性回归模型。我们通过蒙特卡罗研究和活细胞中CXCR4二聚体复合物的FRET饱和曲线分析比较了均匀性测试方法的性能,CXCR4是G蛋白偶联受体家族的七跨膜受体。我们发现,F检验、bootstrap过程和模型选择方法通常会得出类似的结论,尽管AIC在样本量较小时给出了最好的结果,而F检验和bootstrap方法更适合于大样本。在实际应用中,三种方法易于同时使用,且具有一致性,便于得出样品均匀性结论。