  Initial protein structural comparisons were sequence-based. Since amino acids
that are distant in the sequence can be close in the 3-dimensional (3D)
structure, 3D contact approaches can complement sequence approaches.
Traditional 3D contact approaches study 3D structures directly. Instead, 3D
structures can be modeled as protein structure networks (PSNs). Then, network
approaches can compare proteins by comparing their PSNs. Network approaches may
improve upon traditional 3D contact approaches. We cannot use existing PSN
approaches to test this, because: 1) They rely on naive measures of network
topology. 2) They are not robust to PSN size. They cannot integrate 3) multiple
PSN measures or 4) PSN data with sequence data, although this could help
because the different data types capture complementary biological knowledge. We
address these limitations by: 1) exploiting well-established graphlet measures
via a new network approach, 2) introducing normalized graphlet measures to
remove the bias of PSN size, 3) allowing for integrating multiple PSN measures,
and 4) using ordered graphlets to combine the complementary PSN data and
sequence data. We compare both synthetic networks and real-world PSNs more
accurately and faster than existing network, 3D contact, or sequence
approaches. Our approach finds PSN patterns that may be biochemically
interesting.
