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The ROC curve is simply a plot of the true positive rate (same as sensitivity) vs. false
positive rate (1-specificity), for each value of the threshold, as the threshold is varied
from +1 to -1, a range which included all possible values of HingeMaster score, hNMa,
and the squared-normalized displacement of the higher modes described in the discussion
of hNMa. For a good predictor, the true positive rate will increase faster than the false
positive rate as the threshold is lowered, and the area under the curve will be significantly
greater than 0.5
Holm and Sander-like correlation matrix partitioning (hNMe)
As explained in the main text, Holm and Sander made a hinge predictor which analyzed
contact matrices. The basic idea is that for domain hinge bending proteins, two or more
submatrices lying on the diagonal correspond to continuous stretches of polypeptide
within a structural domain. Correspondingly, each such submatrix contains many
interconnected residues, represented by matrix elements set to “1”. If two such
submatrices correspond to different structural domains then the elements of the complete
matrix which connect the submatrices will be poor in contacts, and will largely be set to
“0”. It is possible to partition the complete matrix into such submatrices by iteratively
moving the boundaries of all possible submatrices, but in the most general case this
would be prohibitively expensive.
Holm and Sander instead extract the lowest-order nontrivial eigenvector of the contact
matrix, then sort the residues according the value assigned to each residue by that
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