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. 2018 Nov 26;58(11):2331-2342.
doi: 10.1021/acs.jcim.8b00548. Epub 2018 Oct 19.

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  1   2 , David Rooklin  2 , Duxiao Yang  3 , Xiao Liang  1 , Kangshuai Li  3 , Jianing Lu  2 , Cheng Wang  2 , Peng Xiao  3 , Yingkai Zhang  2   4 , Jin-Peng Sun  3 , Hao Fang  1
Affiliations

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 et al. J Chem Inf Model. .

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.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1.
Figure 1.
(A) Analysis of apo and holo structures for different classes of PTPs in the RCSB Protein Data Bank (version June 2018). (B) Computational strategy to predict protein bound state from apo state.
Figure 2.
Figure 2.
Comparison of the ligand binding pockets in apo (PDB: 1SUG, 3QCB and 3B7O) and holo (PDB: 1PH0, 3QCJ and 3O5X) crystal structures of PTP1B (A), PTPgama (B) and SHP2 (C).
Figure 3.
Figure 3.
Computational strategy validation using LMW-PTP. The binding pockets of LMW-PTP inhibitor in holo crystal structure (A), apo crystal structure (B) and representative MD snapshot (C) are calculated using AlphaSpace, . (D) RMSD of binding site residues (within 5Å of LMW-PTP inhibitor) from apo and holo crystal structures. (E) Comparison of ligand binding pocket space and score in holo crystal structure, apo crystal structure and representative MD snapshot. (F) Probability of ligand binding pocket space during MD simulation.
Figure 4.
Figure 4.
Prediction of the most favorable bound state structure of PTPRO. (A) Selected snapshots of compound 1 during 200ns MD simulation. (B) The RMSD values and calculated binding energies of compound 1 during MD simulation. (C) Binding pockets of compound 1 from initial docking result using crystal structure. (D) Binding pockets of compound 1 from representative MD snapshot. (E) Vina scores and occupied pocket space values of compound 1 in crystal structure and representative MD structure. Fragment-centric pocket analysis was performed using AlphaSpace, . Pockets are represented by spheres, which are colored by pocket classification: core pockets (green), auxiliary pockets (blue), and minor pockets (rosy brown).
Figure 5.
Figure 5.
Comparison of the inhibitor binding sites in PTPRO. Panel A and B illustrate the pockets of three inhibitor binding sites (Site 1, Site 2 and Site 3) in crystal structure and MD representative structure. Pockets are represented by spheres, which are colored by pocket classification: core pockets (green), auxiliary pockets (blue), and minor pockets (rosy brown). Panel C and D present the total pocket score and pocket space for three inhibitor binding sites, comparing crystal structure and MD representative structure.
Figure 6.
Figure 6.
(A) Predicted bound state of PTPRO is illustrated with two major inhibitor binding sites on the left and the workflow for virtual screening on the right. (B) Chemical structures and predicted binding modes of compound GP03, GP07 and GP17. The inhibitory activities against PTPRO as well as occupied pocket space values are illustrated for each compound.
Figure 7.
Figure 7.
Kinetic analysis of PTPRO inhibition by GP03 (A)and GP07 (B). The Lineweaver-Burk plot displays a characteristic pattern of intersecting lines that indicates competitive inhibition.
Figure 8.
Figure 8.
The ability to discriminate between binders and non-binders. (A) Docking scores of 20 virtual screening hits using Autodock Vina and Gold. (B) Energy difference and structure difference of each virtual screening hit between local minimum and global minimum. (C) Active and inactive hits are schematically represented by different energy wells on the ligand energy landscape, illustrating the magnitude of the conformational energy penalty upon binding.
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