Efficient local ligand-binding site search using landmark mds

In this work, we propose a new local binding site search system, called Fast Patch-Surfer, for extending previous work, Patch-Surfer. Patch-Surfer efficiently retrieves top-k similar proteins based on new representation of proteins capturing features of their local ligand-binding site and newly defined distance function. However, further speed up is needed since in practical setting of computing dissimilarity between proteins, there are possibilities for simultaneous multiple user access on the database. We address this need for further speed up in local ligand-binding site search by exploiting landmark MultiDimensional Scaling (MDS), which is an efficient version of MDS being popularly used for representing high-dimensional dataset. According to the result, using our method, the searching time is reduced up to 99%, and it retrieves almost 80% of exact top-k results.