Expanding Direct Coupling Analysis to Identify Heterodimeric Interfaces from Limited Protein Sequence Data

Direct
coupling analysis (DCA) is a global statistical approach
that uses information encoded in protein sequence data to predict
spatial contacts in a three-dimensional structure of a folded protein.
DCA has been widely used to predict the monomeric fold at amino acid
resolution and to identify biologically relevant interaction sites
within a folded protein. Going beyond single proteins, DCA has also
been used to identify spatial contacts that stabilize the interaction
in protein complex formation. However, extracting this higher order
information necessary to predict dimer contacts presents a significant
challenge. A DCA evolutionary signal is much stronger at the single
protein level (intraprotein contacts) than at the protein–protein
interface (interprotein contacts). Therefore, if DCA-derived information
is to be used to predict the structure of these complexes, there is
a need to identify statistically significant DCA predictions. We propose
a simple Z-score measure that can filter good predictions
despite noisy, limited data. This new methodology not only improves
our prediction ability but also provides a quantitative measure for
the validity of the prediction.