Structure Based Docking and Molecular Dynamic Studies of Plasmodial Cysteine Proteases against a South African Natural Compound and its Analogs

Abstract

Identification of potential drug targets as well as development of novel antimalarial chemotherapies with unique mode of actions due to drug resistance by Plasmodium parasites are inevitable. Falcipains (falcipain-2 and falcipain-3) of Plasmodium falciparum, which catalyse the haemoglobin degradation process, are validated drug targets. Previous attempts to develop peptide based drugs against these enzymes have been futile due to the poor pharmacological profiles and susceptibility to degradation by host enzymes. This study aimed to identify potential non-peptide inhibitors against falcipains and their homologs from other Plasmodium species. Structure based virtual docking approach was used to screen a small non-peptidic library of natural compounds from South Africa against 11 proteins. A potential hit, 5α-Pregna-1,20-dien-3-one (5PGA), with inhibitory activity against plasmodial proteases and selectivity on human cathepsins was identified. A 3D similarity search on the ZINC database using 5PGA identified five potential hits based on their docking energies. The key interacting residues of proteins with compounds were identified via molecular dynamics and free binding energy calculations. Overall, this study provides a basis for further chemical design for more effective derivatives of these compounds. Interestingly, as these compounds have cholesterol-like nuclei, they and their derivatives might be well tolerated in humans.

Figure 1. Graphical representation of the different approaches used in this study.

Figure 2. The drug-like properties, molecular weight (Mol. Wt.), hydrogen bond acceptors (HbA), hydrogen bond donors (HbD), number of rotational bonds (nRB), partition coefficient (LogP) and the 2-Dimensional (2D) structures of all compounds used in this study.

Marked with asterisk is the South African hit used for structure similarity search on the ZINC database.

Figure 3. AutoDock binding energies.

A heatmap showing the interaction energies of the SA subset of natural compounds and selected ZINC hits when docked against human cathepsins and plasmodial cysteine proteases. Shown by the dotted box are the energy profiles of the interaction between 5PGA and the corresponding protease. The energy code shows regions with interaction energy ranging from low (yellow) to high (black).

Figure 4. 5PGA-protein predicted inhibitor constants as determined by AutoDock software.

Figure 5. Binding poses of 5PGA (green), ZINC03869631 (magenta), ZINC04532950 (blue) and ZINC05247724 (cyan) in relation to the various subsites of cysteine proteases.

S1 is shown in pale yellow, S2 in brick red, S3 in green while S1’ in orange.

Figure 6. Conformational stability of the different protein complexes with 5PGA and the selected ZINC hits during the last 12 ns of MD simulations with GROMACS.

The RMSD of (a) apo structure (b) holo system (c) ligand only and (d) radius of gyration. Standard deviations are shown by the error bars.

Figure 7

The average number of intermolecular H-bonds of (a) Cat k and (b) Cat L in complex with 5PGA, ZINC03869631, ZINC04532950 and ZINC05247724 during a 20 ns MD simulation.

Figure 8

The average number of intermolecular H-bonds of (a) FP-2 and (b) FP-3 in complex with 5PGA, ZINC03869631, ZINC04532950 and ZINC05247724 during a 20 ns MD simulation.

Figure 9. Box plots showing the distribution of the various interaction energies of different ligands.

Figure 10. Binding pocket amino acid residue interactions patterns of bound 5PGA, ZINC03869631, ZINC04532950, and ZINC05247724 with Cat L (blue), FP-2 (yellow) and FP-3 (magenta).

Hydrogen bonds are depicted by a yellow dotted line.

Figure 11

Per-residue decomposition analysis of 5PGA and the selected ZINC compounds when in complex with (a) Cat K, (b) Cat L, (c) FP-2 and (d) FP-3. Amino acids with a positive energy value impair the binding and vice versa.