Computational Strategy for Bound State Structure Prediction in Structure-Based Virtual Screening: A Case Study of Protein Tyrosine Phosphatase Receptor Type O Inhibitors

Xuben Hou, David Rooklin, Duxiao Yang, Xiao Liang, Kangshuai Li, Jianing Lu, Cheng Wang, Peng Xiao, Yingkai Zhang, Jin Peng Sun, Hao Fang

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
JournalJournal of Chemical Information and Modeling
DOIs
StateAccepted/In press - Jan 1 2018

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Class 3 Receptor-Like Protein Tyrosine Phosphatases
Phosphatases
Screening
Proteins
Ligands
Class 2 Receptor-Like Protein Tyrosine Phosphatases
Protein Tyrosine Phosphatases
Phosphoprotein Phosphatases
Binding energy
energy
Molecular dynamics
activity structure
Molecular weight
penalty
Computer simulation
Disease
simulation

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Computer Science Applications
  • Library and Information Sciences

Cite this

Computational Strategy for Bound State Structure Prediction in Structure-Based Virtual Screening : A Case Study of Protein Tyrosine Phosphatase Receptor Type O Inhibitors. / Hou, Xuben; Rooklin, David; Yang, Duxiao; Liang, Xiao; Li, Kangshuai; Lu, Jianing; Wang, Cheng; Xiao, Peng; Zhang, Yingkai; Sun, Jin Peng; Fang, Hao.

In: Journal of Chemical Information and Modeling, 01.01.2018.

Research output: Contribution to journalArticle

Hou, Xuben ; Rooklin, David ; Yang, Duxiao ; Liang, Xiao ; Li, Kangshuai ; Lu, Jianing ; Wang, Cheng ; Xiao, Peng ; Zhang, Yingkai ; Sun, Jin Peng ; Fang, Hao. / Computational Strategy for Bound State Structure Prediction in Structure-Based Virtual Screening : A Case Study of Protein Tyrosine Phosphatase Receptor Type O Inhibitors. In: Journal of Chemical Information and Modeling. 2018.
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