  Identifying protein functional sites (PFSs) and protein-ligand interactions
(PLIs) are critically important in understanding the protein function and the
involved biochemical reactions. As large amount of unknown proteins are quickly
accumulated in this post-genome era, an urgent task arises to predict PFSs and
PLIs at residual level. Nowadays many knowledge-based methods have been well
developed for prediction of PFSs, however, accurate methods for PLI prediction
are still lacking. In this study, we have presented a new method for prediction
of PLIs and PFSs based on sequence of the inquiry protein. The key of the
method hinges on a function- and interaction-annotated protein domain profile
database, called fiDPD, which was built from the Structural Classification of
Proteins (SCOP) database, using a hidden Markov model program. The method was
applied to 13 target proteins from the recent Critical Assessment of Structure
Prediction (CASP10/11). Our calculations gave a Matthews correlation
coefficient (MCC) value of 0.66 for prediction of PFSs, and an 80% recall in
prediction of PLIs. Our method reveals that PLIs are conserved during the
evolution of proteins, and they can be reliably predicted from fiDPD. fiDPD can
be used as a complement to existent bioinformatics tools for protein function
annotation.
