Yingkai Zhang's group and collaborators at Shandong University have published a study entitled, "Computational Strategy for Bound State Structure Prediction in Structure-Based Virtual Screening: A Case Study of Protein Tyrosine Phosphatase Receptor Type O Inhibitors," which is featured on the cover of the Journal of Chemical Information and Modelling. First author credit goes to Xuben Hou, and other NYU Chemistry authors include David Rooklin, Jianing Lu and Cheng Wang.
Abstract: Accurate protein structure in the ligand-bound state is a prerequisite for successful structure-based virtual screening (SBVS). Therefore, applications of SBVS against targets for which only an apo structure is available may be severely limited. To address this constraint, we developed a computational strategy to explore the ligand-bound state of a target protein, by combined use of molecular dynamics simulation, MM/GBSA binding energy calculation, and fragment-centric topographical mapping. Our computational strategy is validated against low-molecular weight protein tyrosine phosphatase (LMW-PTP) and then successfully employed in the SBVS against protein tyrosine phosphatase receptor type O (PTPRO), a potential therapeutic target for various diseases. The most potent hit compound GP03 showed an IC50 value of 2.89 μM for PTPRO and possessed a certain degree of selectivity toward other protein phosphatases. Importantly, we also found that neglecting the ligand energy penalty upon binding partially accounts for the false positive SBVS hits. The preliminary structure–activity relationships of GP03 analogs are also reported.