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. 2023 Mar 2;28(5):2315.
doi: 10.3390/molecules28052315.

Rational Computational Approaches in Drug Discovery: Potential Inhibitors for Allosteric Regulation of Mutant Isocitrate Dehydrogenase-1 Enzyme in Cancers

Masthan Thamim  1 , Ashish Kumar Agrahari  2 , Pawan Gupta  3 , Krishnan Thirumoorthy  1
Affiliations

Rational Computational Approaches in Drug Discovery: Potential Inhibitors for Allosteric Regulation of Mutant Isocitrate Dehydrogenase-1 Enzyme in Cancers

Masthan Thamim et al. Molecules. .

Abstract

Mutations in homodimeric isocitrate dehydrogenase (IDH) enzymes at specific arginine residues result in the abnormal activity to overproduce D-2 hydroxyglutarate (D-2HG), which is often projected as solid oncometabolite in cancers and other disorders. As a result, depicting the potential inhibitor for D-2HG formation in mutant IDH enzymes is a challenging task in cancer research. The mutation in the cytosolic IDH1 enzyme at R132H, especially, may be associated with higher frequency of all types of cancers. So, the present work specifically focuses on the design and screening of allosteric site binders to the cytosolic mutant IDH1 enzyme. The 62 reported drug molecules were screened along with biological activity to identify the small molecular inhibitors using computer-aided drug design strategies. The designed molecules proposed in this work show better binding affinity, biological activity, bioavailability, and potency toward the inhibition of D-2HG formation compare to the reported drugs in the in silico approach.

Keywords: 2-Hydroxyglutarate; 3D-QSAR; ADME; CADD; cancers; chirality; drug discovery; epigenetics; inhibitors; molecular docking; molecular dynamics simulation; oncometabolite.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Scheme 1
Scheme 1
The mechanistic pathway of the wild-type and mutant-type IDH1/2 reaction in oncometabolite formation (Blue—allowed reaction, Red—forbidden reaction).
Figure 1
Figure 1
The aligned structures of (A) 62 reported ligands using Tanimoto similarities and the FB-3D-QSAR modeled results of favorable and unfavorable contour maps for (B) steric (Green—Positive, Yellow—Negative), (C) electrostatic (Blue—Positive, Red—Negative), (D) hydrophobic (Green—Positive, Red—Negative), (E) hydrogen bond acceptor (Cyan—Positive, Pink—Negative) and (F) donor (Blue—Positive, Red—Negative) properties.
Figure 2
Figure 2
The steric (S and S′) and hydrophobic (H and H′) modification of favorable and non-favorable functional points in reference compound (RC-01). The green represents the oxazolidine moiety, pink represents the pyrimidine moiety, and cyan represents the imidazole moiety.
Figure 3
Figure 3
The cumulative picture representation of reference compound (stick model) and designed compounds (ball and stick model).
Figure 4
Figure 4
The overall RMSD analysis of protein and ligands compounds for 50 ns.
Figure 5
Figure 5
The overall RMSF analysis of designed and reference compounds.
Figure 6
Figure 6
The overall Rg plot for designed and reference compounds for 50 ns.
Figure 7
Figure 7
The HB interaction plot of the designed and reference compounds for 50 ns.
Figure 8
Figure 8
The receptor–ligand 3D interaction of reference compound (A) (RC-01), (B) (RC-02), and the designed compounds (C) (DC-01), (D) (DC-02), (E) (DC-03) and (F) (DC-04) after the simulation at the final frame (50 ns). Green—Classical and non-classical hydrogen bond interactions, Cyan—halogen induced hydrogen bond interactions, Lavender—alkyl-alkyl and pi-alkyl interactions, Magenta—pi-pi interactions.
Figure 8
Figure 8
The receptor–ligand 3D interaction of reference compound (A) (RC-01), (B) (RC-02), and the designed compounds (C) (DC-01), (D) (DC-02), (E) (DC-03) and (F) (DC-04) after the simulation at the final frame (50 ns). Green—Classical and non-classical hydrogen bond interactions, Cyan—halogen induced hydrogen bond interactions, Lavender—alkyl-alkyl and pi-alkyl interactions, Magenta—pi-pi interactions.
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