In metaPocket 1.0 (MPK1), there are four element predictors: LIGSITEcs, PASS, Q-SiteFinder, SURFNET, In metaPocket 2.0 (MPK2), we added four new methodsFpocket, GHECOM, ConCavity and POCASA, and the success rate has been raised a lot. Plase see the comparison of MPK1 and MPK2 in table 1:
Table 1: Success rate comparison between MPK1 and MPK2 in different datasets.
In MPK1, functional residues are simple identified by calculating the distances between ligand binding sites and all the protein residues and if the distance is within a threshold value (5.0 Angstrom was used), the residue is judged to be a functional residue. This may cause many problems especially the ligand binding site is very deep, and the bottom residues will missing. In MPK2, we use a synthetically way to do it. For the detailed description please refer to: algorithm page. An example result of residue mapping of MPK2 is illustrated in Figure 1.
Figure 1: The result of residue mapping of MPK2. Image generated from PyMOL 1.2. PDB id: 1J3P. Ligand binding sites are illustracted in red ball, probe points are in blue, potential binding atoms are in green balls, functional residues are in cyan mesh, other parts of the protein are in red lines.
Users can structually visualize their prediction results dicrectly in this server by using our web visualization system built based on Jmol. The sample result visualization is shown in Figure 2, for the usage of the web visualization system please go to our help page.
Figure 2: Structural visualization of prediction result. PDB id: 1J3P.
Zengming Zhang, Yu Li, Biaoyang Lin, Michael Schroeder and Bingding Huang (2011), Identification of cavities on protein surface using multiple computational approaches for drug binding site prediction. Bioinformatics, 27 (15): 2083-2088. link
If you find some bugs of this server, please help us improve metaPocket by reporting bugs to Zengming Zhang, any help from you will be greatly appreciated!
Funding from Klaus Tschira Foundation, MOST China (grant no: 2008DFA11320) and EU 7th Framework Marie Curie Actions IRSES project (grant no: 247097) is kindly acknowledged!