LMDS-based approach for efficient top-k local ligand-binding site search

Abstract

In this work, we propose a LMDS-based binding-site search for improving the search speed of the Patch-Surfer method. Patch-Surfer is efficient in recognition of protein-ligand binding partners, further speedup is necessary to address multiple-user access. Futher speedup is realised by exploiting Landmark Multi-Dimensional Scaling (LMDS). It computes embedding coordinates for data points based on their distances from landmark points. When selecting the landmark points, we adopt two approaches random and greedy selection. Our method approximately retrieves top-k results and the accuracy increases as we exploit more landmark points. Although two landmark selection approaches show comparable results, the greedy selection shows the best performance when the number of landmark points is large. Using our method, the searching time is reduced up to 99% and it retrieves almost 80% of exact top-k results. Additionally, LMDS-based binding-site search+ improves the retrieval accuracy from 80% to 95% while sacrificing the speedup ratio from 99% to 90% compared to Patch-Surfer.