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. 2021 Apr 7;26(8):2114.
doi: 10.3390/molecules26082114.

Identification of Flavonoids as Putative ROS-1 Kinase Inhibitors Using Pharmacophore Modeling for NSCLC Therapeutics

Shraddha Parate  1 , Vikas Kumar  2 , Jong Chan Hong  1 , Keun Woo Lee  2
Affiliations

Identification of Flavonoids as Putative ROS-1 Kinase Inhibitors Using Pharmacophore Modeling for NSCLC Therapeutics

Shraddha Parate et al. Molecules. .

Abstract

Non-small cell lung cancer (NSCLC) is a lethal non-immunogenic malignancy and proto-oncogene ROS-1 tyrosine kinase is one of its clinically relevant oncogenic markers. The ROS-1 inhibitor, crizotinib, demonstrated resistance due to the Gly2032Arg mutation. To curtail this resistance, researchers developed lorlatinib against the mutated kinase. In the present study, a receptor-ligand pharmacophore model exploiting the key features of lorlatinib binding with ROS-1 was exploited to identify inhibitors against the wild-type (WT) and the mutant (MT) kinase domain. The developed model was utilized to virtually screen the TimTec flavonoids database and the retrieved drug-like hits were subjected for docking with the WT and MT ROS-1 kinase. A total of 10 flavonoids displayed higher docking scores than lorlatinib. Subsequent molecular dynamics simulations of the acquired flavonoids with WT and MT ROS-1 revealed no steric clashes with the Arg2032 (MT ROS-1). The binding free energy calculations computed via molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) demonstrated one flavonoid (Hit) with better energy than lorlatinib in binding with WT and MT ROS-1. The Hit compound was observed to bind in the ROS-1 selectivity pocket comprised of residues from the β-3 sheet and DFG-motif. The identified Hit from this investigation could act as a potent WT and MT ROS-1 inhibitor.

Keywords: MM/PBSA; NSCLC; ROS-1 kinase; drug resistance; flavonoids; molecular docking; molecular dynamics simulations; structure-based pharmacophore; virtual screening.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Receptor-ligand pharmacophore model- Pharmacophore_01. (A) Pharmacophore model generated at the catalytic site of c-ros oncogene1 (ROS-1) complexed with its co-crystallized ligand, lorlatinib. (B) ROS-1 selective inhibitor, lorlatinib mapping with essential residues of the ROS-1 active site via key pharmacophoric features- HBD, HBA and Hy. (C) Interfeature distance between the generated features of the pharmacophore. HBD (hydrogen bond donor); HBA (hydrogen bond acceptor); Hy (hydrophobic).
Figure 2
Figure 2
Representation of the steps involved in retrieving drug-like flavonoids for molecular docking, from the TimTec database using the receptor-ligand pharmacophore model.
Figure 3
Figure 3
Backbone root-mean-square deviation (RMSD) analysis of (A) wild-type (WT) and (B) mutated (RT) ROS-1 systems.
Figure 4
Figure 4
Binding mode of co-crystallized inhibitor, Lorlatinib (reference) and identified flavonoids within the catalytic pocket of (A) WT and (B) MT ROS-1 kinase.
Figure 5
Figure 5
Binding mode of (A) lorlatinib and (B) Hit compound in the wild type (WT) ROS-1 catalytic pocket and molecular interactions with key residues.
Figure 6
Figure 6
Binding mode of (A) lorlatinib and (B) Hit compound in the mutated (MT) ROS-1 catalytic pocket and molecular interactions with key residues.
Figure 7
Figure 7
The 2D structures of lorlatinib and Hit flavonoid compound of ROS-1 tyrosine kinase.
See this image and copyright information in PMC

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