  Protein-protein and protein nucleic acid interactions are vitally important
for a wide range of biological processes, including regulation of gene
expression, protein synthesis, and replication and assembly of many viruses. We
have developed machine learning approaches for predicting which amino acids of
a protein participate in its interactions with other proteins and/or nucleic
acids, using only the protein sequence as input. In this paper, we describe an
application of classifiers trained on datasets of well-characterized
protein-protein and protein-RNA complexes for which experimental structures are
available. We apply these classifiers to the problem of predicting protein and
RNA binding sites in the sequence of a clinically important protein for which
the structure is not known: the regulatory protein Rev, essential for the
replication of HIV-1 and other lentiviruses. We compare our predictions with
published biochemical, genetic and partial structural information for HIV-1 and
EIAV Rev and with our own published experimental mapping of RNA binding sites
in EIAV Rev. The predicted and experimentally determined binding sites are in
very good agreement. The ability to predict reliably the residues of a protein
that directly contribute to specific binding events - without the requirement
for structural information regarding either the protein or complexes in which
it participates - can potentially generate new disease intervention strategies.
