Figure 3.

ROC curve analysis. ROC curve as an assay of discriminative power of a profile Hidden Markov models (pHMMs) that models a sub-region within the second intracellular loop of GPCRs that are known to couple with G-proteins of the G_s subfamily. The space under the ROC curve is a measure of the discriminative power of the model, while the distance of each point from the diagonal of the chart y = x is a measure of combined specificity and sensitivity of the model. The e-value that corresponds to the maximal distance from the diagonal spot is set as the threshold that discriminates positive from negative predictions in an hmmpfam run, regarding the selected pHMM. Similar charts were applied to optimize e-value cutoffs of all HMMs in the refined final library.