  A complex network approach to protein folding is proposed. The graph object
is the network of shortcut edges present in a native-state protein (SCN0).
Although SCN0s are found via an intuitive message passing algorithm (S.
Milgram, Psychology Today, May 1967 pp. 61-67), they are meaningful enough that
the logarithm form of their contact order (SCN0_lnCO) correlates significantly
with protein kinetic rates, regardless of protein size. Further, the clustering
coefficient of a SCN0 (CSCN0) can be used to combine protein segments
iteratively within the Restricted Binary Collision model to form the whole
native structure. This simple yet surprisingly effective strategy identified
reasonable folding pathways for 12 small single-domain two-state folders, and
three non-canonical proteins: ACBP (non-two-state), Top7 (non-cooperative) and
DHFR (non-single-domain, > 100 residues). For two-state folders, CSCN0 is
relatable to folding rates, transition-state placement and stability. The
influence of CSCN0 on folding extends to non-native structures. Moreover, SCN
analysis of non-native structures could suggest three fold success factors for
the fast folding Villin headpiece peptide. These results support the view of
protein folding as a bottom-up hierarchical process guided from above by
native-state topology, and could facilitate future constructive demonstrations
of this long held hypothesis for larger proteins.
