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hinge, it is usually predicted by at least one of the other predictors. Notably,
experimentally defined hinges virtually always occur within the lower 10% of the hNMa
atomic displacement function. Accordingly, the predictive confidence of HingeMaster is
strong when at least 3 of these methods predicts a hinge at a given site (or within a few
residues of it). As with computational hinge detection, experimentally-defined hinges
can be identified based on different criteria. For instance, one researcher might identify
them based on the observation of large, localized, non-compensatory changes in main-
chain dihedral angles, whereas another might identify hinges based on large main-chain
B-values (which can reflect rigid-body motions of a well-ordered structure in one case,
but a lack of well-defined local structure in another). Thus, there is a distinct possibility
that the various hinge predictors are identifying hinges that represent different
mechanisms of motion, especially with regard to how localized or disseminated that
motion is. By defining different hinge mechanisms and algorithms for detecting them,
we hope to ultimately clarify the kinds of motion that occur in proteins, and provide tools
that will aid in annotating experimental structures.
Complementarity of methods
Since the various predictors are based on very different information (Table 4.1), their
strengths and weaknesses are complementary. In particular, FO and hNMb are limited to
single- and double-stranded hinges. hNMd, however, has no such limitation and may be
more successful in these cases (supplementary Table 4.5). Similarly, FO is susceptible to
bound metals that play a significant role in stabilizing the protein, but hNMb and hNMd
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