D542 Nucleic Acids Research, 2012, Vol. 40, Database issue reasonable time. Our method ﬁrst encodes ligand-binding sites as feature vectors based on their physicochemical and geometric properties. Then similar sites are enumerated using a fast neighbor-search algorithm called SketchSort (18). For this study, we applied the proposed method to all-pair similarity searches for 1.8 million known and po- tential ligand-binding sites. Consequently, we discovered over 14 million pairs of similar binding sites, which is the largest-scale study of binding site comparison for the PDB entries ever reported. All the discovered similar pairs were compiled into a new database called Pocket Similarity Search using Multiple-sketches (PoSSuM). Similar binding sites have already been enumerated and stored in our database. Therefore, users can retrieve similar sites rapidly, within a few seconds, through our web interface. Because, all sites were annotated with in- formation of various types such as CATH (19), SCOP (20), EC numbers (21) and Gene Ontology (GO) terms (22), users can easily scrutinize similar binding sites between proteins with different folds or similar catalytic sites between enzymes with different EC numbers. In comparison with a well-known existing database, SitesBase (23), which includes 33 000 known ligand-binding sites, our new database stores a much larger number of up-to-date known binding sites de- posited in the PDB. In addition to them, our database includes pairs between known and potential ligand- binding regions predicted using a novel pocket detection program. Our database is expected to be useful for anno- tation of protein functions and rapid screening of target proteins in drug design. The PoSSuM database is freely available for use by researchers at http://possum.cbrc.jp/ PoSSuM/. PROCEDURE FOR DATABASE CONSTRUCTION 1.8 million binding site data set As the source data set, we concatenated the following two sets: 241 486 known ligand-binding sites obtained from protein–ligand complexes in the PDB and 1 588 329 putative ligand-binding sites predicted using a geometric- based pocket detection algorithm. In our study, the deﬁnition of a ligand is any HET molecule observed in the PDB (excluding water molecules, nucleic acids and peptides). The deﬁnition of a binding site is a set of amino acids around a ligand. We selected 47 562 protein entries (X-rays with resolution 4.0 A˚ and MODEL1 of all NMR) from the PDB (version January 2011). Of those, we found HET molecules fulﬁlling the deﬁnition of ligands given above. As a ligand-binding site, we extracted a set of all amino acids, each of which had at least one heavy atom lying within a distance of 5.0 A˚ , from at least one heavy atom of the ligand. A binding site consists of amino acids that are not necessar- ily continuous in the sequence and which need not be located in the same domain or chain [one example is Figure 12C in our earlier study (24)]. Consequently, we obtained 241 486 known ligand-binding sites: far more than those of SitesBase (Table 1). This difference is mainly attributable not only to the version of the PDB, Table 1. Data set of protein-binding sites included in SitesBase and PoSSuM PDB version Deﬁnition of a ligand Number of known ligand-binding sites (number of PDB entries) Number of putative sites (number of PDB entries) SitesBase June 2005 HET molecule with 6 atoms 33 168 (12 898) PoSSuM January 2011 All HET molecules 241 486 (47 562) None 1 588 329 (29 779) but also to the ligand deﬁnition: our data set includes small molecules such as metal ions. Potential binding sites were predicted using a novel binding pocket detection program: Ghecom (13). In this program, a potential ligand-binding pocket on the protein surface is detected as a region into which small probes can enter, but large probes cannot. Various shapes and sizes of pockets can be predicted by changing the radius of large probes (Rlarge). We prepared a non-redundant PDB subset (95% sequence identity cutoff and resolution 4.0 A˚ for X-rays) that contained 29 779 entries. For each, we iterated Ghecom four times while changing the values of Rlarge (3, 4, 5 and 6 A˚) and ﬁxing the value of Rsmall to 1.87 A˚ . The detected pockets are measured by the number of small probes ﬁlling up a pocket. To adjust the putative pocket size approximately to that of known ligand-binding sites, we chose pockets that were smaller than 200 probes. Then, a potential binding site was obtained by extracting a collection of all amino acids, each of which had at least one heavy atom within a distance of 5.0 A˚ from one of the probes. Consequently, 1 588 329 potential binding pockets were obtained (Table 1). In total, we collected 1 829 815 known and putative ligand-binding sites. Although the known sites were taken from almost all PDB entries, the putative ones account for about half of the PDB entries in the current version of PoSSuM. Annotation to binding sites To facilitate subsequent analyses, all binding sites were annotated to the greatest degree possible using CATH (version 3.4) codes, SCOP (version 1.75) domain classiﬁ- cation codes EC commission numbers, and three biologic- al domains of GO terms (molecular functions, biological processes and cellular components). Each site was annotated with four levels of CATH codes, i.e. Class, Architecture, Topology and Homology, and of SCOP codes, i.e. Class, Fold, Superfamily and Family, by matching the binding site residues against the domain region deﬁned by CATH or SCOP. One binding site can reside between multiple domains. In such a case, we found all domains involved with the binding site, and annotated the site with the multiple CATH and SCOP codes. Among those domains, many had not been