Table 1 Functional site prediction tools included in the comparison analysis.

SitesIdentify

   Uniform charge method

CF

A uniform charge weighting is applied to each Cα atom on the protein and the electrostatic potential (Finite Difference-Poisson-Boltzmann calculation with no dielectric boundary) is sampled at points on a 2Ǻ grid across the protein volume. The peak potential indicates the position of the predicted active site.

Bate and Warwicker, (2004)

   Conservation method

SC, CF

As for the above method, except that the charge weightings applied across the protein are replaced with conservation weights derived from normalised sequence profile scores reflecting the amino acid diversity, the stereochemical diversity and the gap occurrence.

Greaves and Warwicker, (2005)

Consurf

SC

Consurf calculates the degree of evolutionary conservation for each residue in a structure and gives them an integer score from 1 to 9, with 9 being the most conserved residues. A graphical representation of the structure is then coloured according to these residue conservation scores, which allows visual identification of highly conserved patches that are predicted to be functional sites.

Landau et al. (2005)

Crescendo

SC

Predicts active sites by identifying clusters of residues that have higher than usual evolutionary restraint. Evolutionary constraint was identified by three measures: 1) whether there was a higher degree of evolutionary conservation than expected at a position, 2) whether environment specific substitution tables made weak predictions of the amino acid substitution patterns, and 3) residues that have spatially conserved positions when structures of proteins within the same family are superimposed.

Chelliah et al. (2004)

FOD

HP

The active site residues are predicted to be those with the highest hydrophobic deficiency score. This is the difference between the expected hydrophobicity and the observed hydrophobicity value for each residue. The expected hydrophobicity of a residue is determined by a residues relative position to the theoretically most hydrophobic point in the protein. The observed hydrophobicity is a combination of the hydrophobicity value of that residue and the effect on the residues position of other sidechains around it.

Brylinksi et al. (2007)

Q-SiteFinder

CF

Non-bonded interaction energies are calculated by placing a 3D grid over the whole protein and then evaluating the interaction energy between the protein and a methyl group at each point on the grid. The positions of the probes on the grid that gave the best interaction energies were then spatially clustered to identify groups of close probes. These clusters are then assigned a single interaction energy based on the energies of their member probes. The clusters are then ranked by their representative interaction energy and the highest ranked cluster is predicted as the active site.

Laurie and Jackson (2005)

PDBSiteScan

TM

PDBSiteScan takes 3D fragments of a protein structure and compares them to 3D structure fragments of known active sites. The known active sites structures are held in a collection called PDBSite that is formed from annotation in the PDB SITE field and also REMARK 800 fields. Results were discounted if they compared to annotation held for the test protein.

Ivanisenko et al. (2004)

PASS

CF

PASS (Putative Active Site Spheres) is essentially a geometric cleft-finding method. The shape, volume and depth of the cleft determine which clefts are predicted as active site clefts.

Brady and Stouten (2000)

Thematics

CP

Thematics identifies ionisable residues with unusually perturbed titrations curves. Active sites are predicted where two or more of these ionisable residues form a cluster in 3D space.

Wei et al. (2007)