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is reasonably successful in these cases. TLSMD is comparatively weak at distinguishing
domain from non-domain motion, but can find smaller scale motions not detected by FO
and hNMb, and its accuracy is unaffected by bound metals. Stonehinge lacks precision
but is good at finding the general region of the hinge. Altogether, only a few hinges
escaped detection by one or another of the methods. We found that FO1, FO1M,
HingeSeq, hNMa, and hNMd were of very little value compared to the others. The
combination of highly specific predictors which usually did well but were sometimes
somewhat off the mark, with predictors which identified broad swathes where the hinges
were likely to be, resulted in a predictor which assigned a hinge score to each residue
rather than a binary prediction. The output of HingeMaster strongly indicates the
pinpointed location of the hinge predicted by FOC and hNMb, but less dramatically
points out alternative locations. When interpreted by a critical eye, these results could
bring insight even when one or more of the predictors is incorrect. Some of this is
visible in the results for individual proteins as discussed.
Conclusions
We demonstrated the strengths and weaknesses of several predictors, including a set of
normal mode based tools. We show that for 29 of the 40 proteins in HAG at least one
predictor is completely successful under the loose criterion. For 10 of the remaining 11,
at least one predictor was partly successful. HingeMaster weighs the correlation of each
predictor to the HAG hinge annotations and presents the combined results in a visually
understandable way. This combined predictor is shown to robustly produce ROC curves
demonstrating high predictive power.
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