deﬁned in CATH or SCOP. In our study, if the number of binding site residues that were overlapped with undeﬁned domains was >70% of all binding site residues, then we regarded the binding site as an undeﬁned one and assigned ‘0.0.0.0’ to it. Furthermore, we assigned EC numbers and GO terms to binding sites. Because, an EC number and/or a GO term was assigned to each protein chain, we detected the largest overlapped protein chain for the binding site, and assigned the corresponding EC number/GO term to the site. Fast method for enumerating similar binding sites To enumerate similar pairs, we applied our ultrafast method to the 1.8 million binding sites. Our method encodes each binding site as a feature vector based on their physicochemical and geometric properties. Then, the similarity between two sites is measured as the cosine similarity of two vectors, ranging from 1.0 to 1.0. To compare sites, an earlier often-used method, FuzCav (15), which uses similar descriptors of binding sites to those that we use, employed a special similarity metric counting of exact matches of frequencies in the descrip- tors. However, this is too sensitive to small frequency changes of sites. Instead, we use cosine similarity, which is more robust against such changes, in our method (24). The cosine value drops according to the difference between two vectors, i.e. two sites. For brute-force pairwise cosine calculation, similar sites are enumerated using a fast neighbor-search algorithm called SketchSort (18), in which feature vectors are converted into bit strings and where neighbor pairs are enumerated by multiple masked sorting (25). The source code of SketchSort is available on our website (http://sites.google.com/site/ tabeiyasuo/). A crucial point is that the sorting operation can be performed as approximately O(n), where n repre- sents the number of binding sites. It is much faster than a brute-force pairwise comparison whose time complexity is O(n2). The computation time to enumerate all similar pairs with cosine similarity 0.77 from the 1.8 million sites was 7 days or less on a single-core processor (Xeon 2.93 GHz; Intel Corp.). From the enumerated pairs, we eliminated the pairs that comprised only two putative sites because the pairs without a ligand label are less in- formative for ﬁnding ligand analogs that bind to a similar region, and for predicting the bound ligands of putative sites. We speciﬁcally examined the remaining pairs (over 277 million) consisting of at least one known ligand-binding site and performed structural alignment of all of them. To obtain the 3D superpositions for all pairs, we simply employed TM align (26). Although this program was developed to obtain 3D alignment of protein global structures, it can also be applied to two sets of discontinuous amino acids such as those of the ligand-binding site [details presented in our accepted paper (24)]. An important concern is that biologically meaningful pairs might be ﬁltered out during this proced- ure because TM align can only ﬁnd 3D superposition with sequence-order dependent alignment. However, we selected it mainly for the computation time because no al- gorithm that can perform over 277 million sequence-order Nucleic Acids Research, 2012, Vol. 40, Database issue D543 independent alignments within a reasonable time has ever been reported. Eventually, we obtained 14 556 057 pairs whose binding sites were aligned with six or more residues. Statistics of obtained pairs Of the similar pairs, 66% (9 625 132 pairs) were known– known pairs comprising only known ligand-binding sites (Figure 1A). The remaining 34% (4 930 925) were known–putative pairs consisting of a known ligand- binding site and a putative site. These pairs, which are unique to our database, are particularly interesting because they are useful for speculating on the type of bound ligand and for assigning functions to the un-characterized proteins. Meanwhile, 51% of detected pairs consisted of two binding sites, both of which were annotated with CATH code(s). We classiﬁed them into two categories: homolo- gous pairs comprise two binding sites with identical CATH codes; all remaining pairs are analogous pairs. A binding site can have multiple CATH codes, as described earlier. In such a situation, if at least one pair of the CATH codes exactly matches at four levels, then the pair is regarded as homologous; otherwise it is an analo- gous pair. Analogous pairs were 1 973 495 (13%); homolo- gous pairs were 5 540 245 (38%) (Figure 1B), suggesting that our method can detect similar binding sites, not only between homologous proteins but also between proteins with different folds or sequences. All of the obtained 14 million pairs were compiled into a relational database, POSSUM, along with their corres- ponding annotations such as CATH, SCOP, EC and GO. THE WEB INTERFACE PoSSuM is implemented using PHP, MySQL and JavaScript on a machine with eight 2.93-GHz processors (core i7; Intel Corp.). One advantage of the PoSSuM database is that similar pairs have already been enumerated using our method and have been stored in the MySQL database. Consequently, similar sites can be retrieved rapidly. PoSSuM provides search modes of two types. (i) SearchK: given a known ligand-binding site as a query, this search mode provides two applications. The users can retrieve various ligands whose binding sites are similar to the query site if the target data set is set to known ligand-binding sites. By searching against the putative data set, users can detect putative pockets that might bind the ligand of the query site, which might be useful for detecting apo-forms. (ii) SearchP: post a known protein structure as a query, and search for similar sites for the predicted bind- ing pockets on the query structure against known binding sites. This search mode is helpful for inferring the binding ligand of a structure of interest.