Searching glycolate oxidase inhibitors based on QSAR, molecular docking, and molecular dynamic simulation approaches

Abstract

Primary hyperoxaluria type 1 (PHT1) treatment is mainly focused on inhibiting the enzyme glycolate oxidase, which plays a pivotal role in the production of glyoxylate, which undergoes oxidation to produce oxalate. When the renal secretion capacity exceeds, calcium oxalate forms stones that accumulate in the kidneys. In this respect, detailed QSAR analysis, molecular docking, and dynamics simulations of a series of inhibitors containing glycolic, glyoxylic, and salicylic acid groups have been performed employing different regression machine learning techniques. Three robust models with less than 9 descriptors-based on a tenfold cross (Q² _CV) and external (Q² _EXT) validation-were found i.e., MLR1 (Q² _CV = 0.893, Q² _EXT = 0.897), RF1 (Q² _CV = 0.889, Q² _EXT = 0.907), and IBK1 (Q² _CV = 0.891, Q² _EXT = 0.907). An ensemble model was built by averaging the predicted pIC₅₀ of the three models, obtaining a Q² _EXT = 0.933. Physicochemical properties such as charge, electronegativity, hardness, softness, van der Waals volume, and polarizability were considered as attributes to build the models. To get more insight into the potential biological activity of the compouds studied herein, docking and dynamic analysis were carried out, finding the hydrophobic and polar residues show important interactions with the ligands. A screening of the DrugBank database V.5.1.7 was performed, leading to the proposal of seven commercial drugs within the applicability domain of the models, that can be suggested as possible PHT1 treatment.

Figure 1

Schematic representation of the production of oxalate from glycolate. Detail flow: A pharmacological treatment that inhibit GO and GI interaction may reduce the production of Ox that forms insoluble kidney stones.

Figure 2

Training and test set splitting employing the K-means technique (25% test and 75% training).

Figure 3

Experimental versus predicted pIC₅₀ values obtained by models MLR1, RF1 and IBK1.

Figure 4

Graphical representation of the maximum and minimum values of Pearson correlation coefficients (r) for each descriptor vs the rest in MLR1, RF1 and IBK1 models. Values for the corresponding correlation matrix are presented in Tables S4, S5 and S6.

Figure 5

(a) Schematic representation for the ensemble construction procedure. (b) pIC₅₀ experimental vs. pIC₅₀ predicted the training and test sets obtained with the ensemble model.

Figure 6

Glyoxylate conformation comparison between experimental (yellow), 2RDT docked (green), and 2RDopt (turquoise).

Figure 7

2D and 3D representation of the Interaction of Gl with FMN and GO.

Figure 8

RMSD of GO during the 200 ns simulation.

Figure 9

Root Mean Square Fluctuation (RMSF) of compounds 82, 116, and Gly. RC amino acids are presented inside the red circle.

Figure 10

(a) RMSD of FMN during the 200 ns simulation. (b) 2D representation of the interaction between FMN and GO. (c) RMSD of the ligands during the 200 ns simulation. (d) Comparison of the structure after 200 ns simulation vs. experimental CDST (yellow).

Figure 11

(a) HBs during the 200 ns simulation. (b) HBs occupancy of the studied compounds.

Figure 12

Chemical structure of the best molecules derived from the drug bank screening.

Figure 13

RMSD of GO (a), FCN (b), and the ligands (c) of the DB screening during the 200 ns simulation.

Figure 14

Boiled egg of (a) all the compounds in the database and (b) 7 best compounds from the screening showing probability of human intestinal absorption (HIA) and blood–brain barrier (BBB) permeation of the studied molecules.

Figure 15

Flow diagram for the applied methodology in this work.