Identification of mIDH1 R132C/S280F Inhibitors from Natural Products by Integrated Molecular Docking, Pharmacophore Modeling and Molecular Dynamics Simulations

Abstract

Mutant isocitrate dehydrogenase 1 (mIDH1) is a common driving factor in acute myeloid leukemia (AML), with the R132 mutation accounting for a high proportion. The U.S. Food and Drug Administration (FDA) approved Ivosidenib, a molecular entity that targets IDH1 with R132 mutations, as a promising therapeutic option for AML with mIDH1 in 2018. It was of concern that the occurrence of disease resistance or recurrence, attributed to the IDH1 R132C/S280F second site mutation, was observed in certain patients treated with Ivosidenib within the same year. Furthermore, it should be noted that most mIDH1 inhibitors demonstrated limited efficacy against mutations at this specific site. Therefore, there is an urgent need to investigate novel inhibitors targeting mIDH1 for combating resistance caused by IDH1 R132C/S280F mutations in AML. This study aimed to identify novel mIDH1 R132C/S280F inhibitors through an integrated strategy of combining virtual screening and dynamics simulations. First, 2000 hits were obtained through structure-based virtual screening of the COCONUT database, and hits with better scores than -10.67 kcal/mol were obtained through molecular docking. A total of 12 potential small molecule inhibitors were identified through pharmacophore modeling screening and Prime MM-GBSA. Dynamics simulations were used to study the binding modes between the positive drug and the first three hits and IDH1 carrying the R132C/S280F mutation. RMSD showed that the four dynamics simulation systems remained stable, and RMSF and Rg showed that the screened molecules have similar local flexibility and tightness to the positive drug. Finally, the lowest energy conformation, hydrogen bond analysis, and free energy decomposition results indicate that in the entire system the key residues LEU120, TRP124, TRP267, and VAL281 mainly contribute van der Waals forces to the interaction, while the key residues VAL276 and CYS379 mainly contribute electrostatic forces.

Keywords: ADMET; IDH1 R132C/S280F mutations; molecular docking; molecular dynamics simulations; pharmacophore modeling.

Figure 1

The flowchart of virtual screening and dynamics simulations.

Figure 2

Chemical structures of the 12 compounds identified by structure-based virtual screening and pharmacophore analysis.

Figure 3

Pharmacophore model of the three molecules and DS-1001b. The pharmacophore models of DS-1001b, CNP0119040, CNP0243438, and CNP0449118 are depicted in panel (A–D) respectively. The pharmacophore features include acceptor (Color: Brown), donor (Color: Sky blue), hydrophobic (Color: Forest green), negative ionic (Color: Firebrick red), aromatic ring (Color: Orange), and positive ionic (Color: Deep blue).

Figure 4

Fluctuation of RMSD values for DS-1001b and three molecules during 500 ns MD simulations. The fluctuation of RMSD values of DS-1001b, CNP0119040, CNP0243438, and CNP0449118 are depicted in panels (A–D), respectively. The black, blue, and cyan fluctuations corresponded to the ligand, backbone, and active pocket, respectively.

Figure 5

RMSF values of mIDH1 residue backbone and visualization. The fluctuation of RMSF values of DS-1001b, CNP0119040, CNP0243438, and CNP0449118 are depicted in panels (A–D), respectively. The RMSF fluctuations on the crystal structure are depicted in (E). Residues 104–124 are illustrated in green, while residues 205–220 are shown in blue. The positions of residues 265 to 290 are indicated by cyan, and red indicates residues 125 to 185.

Figure 6

The Rg values of the DS-1001b, CNP0119040, CNP0243438, and CNP0449118 (frame interval = 10).

Figure 7

DCCM of the three molecules and DS-1001b. The DCCM of DS-1001b, CNP0119040, CNP0243438, and CNP0449118 are depicted in panels (A–D) respectively.

Figure 8

Free energy landscapes of the DS-1001b (A), CNP0119040 (B), CNP0243438 (C), and CNP0449118 (D). In the figure, (Aa,Ab,Ba,Bb,Ca,Cb,Da,Db) correspond to the lowest conformations a and b of A, B, C and D in the free energy landscape, respectively.

Figure 9

Decomposed binding energy of four inhibitors. The DCCM of DS-1001b, CNP0119040, CNP0243438, and CNP0449118 are depicted in panels (A (a), B (b), C (c), and D (d)) respectively.