Exploring actinomycetes natural products to identify potential multi-target inhibitors against Leishmania donovani

Abstract

Visceral leishmaniasis (VL) is a neglected tropical disease that mainly affects the poor population of the Indian, African, and South American subcontinent. The increasing resistance to antimonial and miltefosine and frequent toxicity of amphotericin B drives an urgent need to develop an anti-leishmanial drug with excellent efficacy and safety profile. In this study, three sequential docking protocols (HTVS, SP, and XP) were performed to screen the secondary metabolites (n = 6519) from the actinomycetes source against five key proteins involved in the metabolic pathway of Leishmania donovani. Those proteins were adenine phosphoribosyltransferase (PDB ID: 1QB7), trypanothione reductase (PDB ID: 2JK6), N-myristoyl transferase (PDB ID: 2WUU), pteridine reductase (PDB ID: 2XOX), and MAP kinase (PDB ID: 4QNY). Although the binding energy of top ligands was predicted using the MM-GBSA module of the Schrödinger suite. SP and XP docking mode resulted in 55 multi-targeted ligands against L donovani. MM-GBSA analysis selected the top 18 ligands with good-binding affinity and the binding-free energy for four proteins, as mentioned earlier, when compared with the miltefosine, paromomycin, and a reference ligand selected for each target. Finally, molecular dynamics simulation, post-MD-binding-free energy (MM-PBSA), and principal component analysis (PCA) proposed three best ligands (Adenosine pentaphosphate, Atetra P, and GDP-4-keto-6-deoxymannose) qualifying the above screening parameters and confirmed as a potential drug candidate to fight against Leishmania donovani parasites.

Keywords: Actinomycetes; HTVS; Leishmania donovani; MM-PBSA; PCA; Secondary metabolites; Visceral leishmaniasis infection.

Fig. 1

Schematic representation of workflow and hypothesis of bioinformatic (in-silico) approach used in virtual screening of actinomycetes derived secondary metabolites. HTVS High-throughput Virtual Screening, SP Special Precision, XP Extra Precision, APRT Adenosine phosphoribosyl transferase, TR Trypanothione reductase, NMT N-Myristoyl Transferase, PTR Pteridine reductase, MAPK MAP Kinase

Fig. 2

Ligand interaction diagram of Adenosine phosphoribosyl transferase with different proteins A Adenosine phosphoribosyl transferase, B MAP Kinase, C Pteridine reductase, and D Trypanothione reductase

Fig. 3

Interaction of Atetra P with A Adenosine phosphoribosyl transferase, B MAP Kinase, C Pteridine reductase, and D Trypanothione reductase

Fig. 4

GDP-4-keto-6-deoxymannose interacting with A Adenosine phosphoribosyl transferase, B MAP Kinase, C Pteridine reductase, and D Trypanothione reductase

Fig. 5

Graphical representation of Root-mean square deviation of complex A Adenosine pentaphosphate and MAP kinase, B Adenosine pentaphosphate and Trypanothione reductase, C Atetra P and MAP kinase, D Atetra P and Trypanothione reductase, E GDP-4-keto-6-deoxymannose and MAP kinase, F GDP-4-keto-6-deoxymannose and Trypanothione reductase

Fig. 6

Graphical representation of Root-mean square fluctuation of complex A Adenosine pentaphosphate and MAP kinase, B Adenosine pentaphosphate and Trypanothione reductase, C Atetra P and MAP kinase, D Atetra P and Trypanothione reductase, E GDP-4-keto-6-deoxymannose and MAP kinase, F GDP-4-keto-6-deoxymannose and Trypanothione reductase

Fig. 7

Illustration of the formation and breaking of hydrogen bonds over the course of a 100-ns molecular dynamics simulation. The following combinations: A Adenosine pentaphosphate and MAP kinase; B Adenosine pentaphosphate and Trypanothione reductase; C Atetra P and MAP kinase; D Atetra P and Trypanothione reductase; E GDP-4-keto-6-deoxymannose and MAP kinase; F GDP-4-keto-6-deoxymannose and Trypanothione reductase. Due to the larger size of Trypanothione reductase compared to MAP kinase, bond formation was higher for this enzyme

Fig. 8

Solvent accessibility surface area during the 100 ns of the molecular dynamics simulation is graphically illustrated. A Adenosine pentaphosphate and MAP kinase, B Adenosine pentaphosphate and Trypanothione reductase, C Atetra P and MAP kinase, D Atetra P and Trypanothione reductase, E GDP-4-keto-6-deoxymannose and MAP kinase, F GDP-4-keto-6-deoxymannose and Trypanothione reductase. SAS area for larger complexes is higher as in case of Trypanothione reductase

Fig. 9

Graphical representation of Radius of gyration (Rg) of complex A Adenosine pentaphosphate and MAP kinase, B Adenosine pentaphosphate and Trypanothione reductase, C Atetra P and MAP kinase, D Atetra P and Trypanothione reductase, E GDP-4-keto-6-deoxymannose and MAP kinase, F GDP-4-keto-6-deoxymannose and Trypanothione reductase

Fig. 10

Following a molecular dynamics simulation, a schematic representation of the hydrogen bond interaction between potential compounds and the therapeutic targets of Leishmania donovani is shown. Diagram was prepared by extracting the 1001 frame using the allframes.pdb file A Adenosine pentaphosphate and MAP kinase, B Adenosine pentaphosphate and Trypanothione reductase, C Atetra P and MAP kinase, D Atetra P and Trypanothione reductase, E GDP-4-keto-6-deoxymannose and MAP kinase, F GDP-4-keto-6-deoxymannose and Trypanothione reductase

Fig. 11

Principal component analysis (PCA) plotted for all of the complexes, the projection of eigenvector 1 vs. eigenvector 3 was illustrated as a 2D graph. AP-MAPK (black), AP-TR (red), ATETRA-MAPK (green), ATETRA-TR (blue), GDP-MAPK (yellow), and GDP-TR (nude pink)