Reliable Identification of Significant Differences in Differential Hydrogen Exchange-Mass Spectrometry Measurements Using a Hybrid Significance Testing Approach

Differential hydrogen exchange-mass spectrometry (HX-MS) measurements are valuable for identification of differences in the higher order structures of proteins. Typically, the data sets are large with many differential HX values corresponding to many peptides monitored at several labeling times. To eliminate subjectivity and reliably identify significant differences in HX-MS measurements, a statistical analysis approach is needed. In this work, we performed null HX-MS measurements (i.e., no meaningful differences) on maltose binding protein and infliximab, a monoclonal antibody, to evaluate the reliability of different statistical analysis approaches. Null measurements are useful for directly evaluating the risk (i.e., falsely classifying a difference as significant) and power (i.e., failing to classify a true difference as significant) associated with different statistical analysis approaches. With null measurements, we identified weaknesses in the approaches commonly used. Individual tests of significance were prone to false positives due to the problem of multiple comparisons. Incorporation of Bonferroni correction led to unacceptably large limits of detection, severely decreasing the power. Analysis methods using a globally estimated significance limit also led to an overestimation of the limit of detection, leading to a loss of power. Here, we demonstrate a hybrid statistical analysis, based on volcano plots, that combines individual significance testing with an estimated global significance limit, that simultaneously decreased the risk of false positives and retained superior power. Furthermore, we highlight the utility of null HX-MS measurements to explicitly evaluate the criteria used to classify a difference in HX as significant.