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We also sought to determine whether there existed a bias towards particular protein
classes, in either the Hinge Atlas or the nonredundant set of MolMovDB morphs from
which it was compiled. To do this, we first counted the number of times each top-level
Gene Ontology (GO) term under the “molecular function” ontology was associated with a
protein in the Hinge Atlas. Where the annotation was given for deeper levels, we traced
up the hierarchical tree to retrieve the corresponding top level term in the ontology. Thus
we found, for example, that 14 proteins in the Hinge Atlas were associated with the term
“nucleic acid binding.” We repeated this procedure for the PDB as a whole as well as for
the non-redundant set of 1508 morphs in MolMovDB from which the Hinge Atlas was
compiled. The results for the 10 most frequently encountered GO terms are shown in
Table 2.9.
To compare the Hinge Atlas counts to the PDB counts in an overall fashion, we used the
chi-square distribution with 162 degrees of freedom (from 163 GO terms and 2 datasets)
and obtained a chi-square value of 121.1. This corresponds to a p-value of 0.9931, so
there is no statistically significant difference in the distribution of these terms in the
Hinge Atlas vs. the entire Protein Data Bank.
Statistical comparison of datasets
The Hinge Atlas and computer annotated sets were compiled differently, therefore one
might suspect that the hinges from one set might comprise a statistically different
population from the hinges of the other set. If this were the case, then one of the two sets
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