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where the hypergeometric function gives the probability of finding exactly x of the H
gold standard positive residues in a set of M residues randomly chosen from the
population numbering D:
HYP(H,D,x,M) =
C M " % " D ( M%
$ ' C$ '
# x & # H ( x &
C D
.
" %
$ '
# H&
We will ! use the sensitivity, specificity, and p-value in our statistical evaluation. p-value
is a particularly useful quantity, since it compares directly to random picking. The three
quantities will be used to evaluate the three versions of FlexOracle and compare to GNM,
long a popular flexibility prediction algorithm.
Single-cut predictors and GNM
We begin our statistical evaluation with the TINKER and FoldX versions of the single-
cut predictor. We take as our test positives those residues identified as local minima
according to the algorithm described in the Methods section, then tabulate the various
statistical quantities per the above definitions. GNM requires a slightly different
treatment. To evaluate this predictor, we compute the absolute value of the first normal
mode displacements and normalize this quantity to range from 0 to 1. The nodes, or
points of zero displacement, are taken to correspond to the hinge location. Therefore we
take all residues with normalized displacement smaller than 0.02 to be test positives. The
results are shown in Table 3.1.
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