  We developed a novel method based on the Fourier analysis of protein
molecular surfaces to speed up the analysis of the vast structural data
generated in the post-genomic era. This method computes the power spectrum of
surfaces of the molecular electrostatic potential, whose three-dimensional
coordinates have been either experimentally or theoretically determined. Thus
we achieve a reduction of the initial three-dimensional information on the
molecular surface to the one-dimensional information on pairs of points at a
fixed scale apart. Consequently, the similarity search in our method is
computationally less demanding and significantly faster than shape comparison
methods. As proof of principle, we applied our method to a training set of
viral proteins that are involved in major diseases such as Hepatitis C, Dengue
fever, Yellow fever, Bovine viral diarrhea and West Nile fever. The training
set contains proteins of four different protein families, as well as a
mammalian representative enzyme. We found that the power spectrum successfully
assigns a unique signature to each protein included in our training set, thus
providing a direct probe of functional similarity among proteins. The results
agree with established biological data from conventional structural
biochemistry analyses.
