Combining empirical knowledge, in silico molecular docking and ADMET profiling to identify therapeutic phytochemicals from Brucea antidysentrica for acute myeloid leukemia

Abstract

Acute myeloid leukemia (AML) is one of the deadly cancers. Chemotherapy is the first-line treatment and the only curative intervention is stem cell transplantation which are intolerable for aged and comorbid patients. Therefore, finding complementary treatment is still an active research area. For this, empirical knowledge driven search for therapeutic agents have been carried out by long and arduous wet lab processes. Nonetheless, currently there is an accumulated bioinformatics data about natural products that enabled the use of efficient and cost effective in silico methods to find drug candidates. In this work, therefore, we set out to computationally investigate the phytochemicals from Brucea antidysentrica to identify therapeutic phytochemicals for AML. We performed in silico molecular docking of compounds against AML receptors IDH2, MCL1, FLT3 and BCL2. Phytochemicals were docked to AML receptors at the same site where small molecule drugs were bound and their binding affinities were examined. In addition, random compounds from PubChem were docked with AML targets and their docking score was compared with that of phytochemicals using statistical analysis. Then, non-covalent interactions between phytochemicals and receptors were identified and visualized using discovery studio and Protein-Ligand Interaction Profiler web tool (PLIP). From the statistical analysis, most of the phytochemicals exhibited significantly lower (p-value ≤ 0.05) binding energies compared with random compounds. Using cutoff binding energy of less than or equal to one standard deviation from the mean of the phytochemicals’ binding energies for each receptor, 12 phytochemicals showed considerable binding affinity. Especially, hydnocarpin (-8.9 kcal/mol) and yadanzioside P (-9.4 kcal/mol) exhibited lower binding energy than approved drugs AMG176 (-8.6 kcal/mol) and gilteritinib (-9.1 kcal/mol) to receptors MCL1 and FLT3 respectively, indicating their potential to be lead molecules. In addition, most of the phytochemicals possessed acceptable drug-likeness and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Based on the binding affinities as exhibited by the molecular docking studies supported by the statistical analysis, 12 phytochemicals from Brucea antidysentrica (1,11-dimethoxycanthin-6-one, 1-methoxycanthin-6-one, 2-methoxycanthin-6-one, beta-carboline-1-propionic acid, bruceanol A, bruceanol D, bruceanol F, bruceantarin, bruceantin, canthin-6-one, hydnocarpin, and yadanzioside P) can be considered as candidate compounds to prevent and manage AML. However, the phytochemicals should be further studied using in vivo & in vitro experiments on AML models. Therefore, this study concludes that combination of empirical knowledge, in silico molecular docking and ADMET profiling is useful to find natural product-based drug candidates. This technique can be applied to other natural products with known empirical efficacy.

Molecular docking of secondary metabolites of marine macroalgae Sargassum vulgare against exotoxin A

“…In silico molecular docking was applied to predict the interactions between ETA and phytochemical ligands based on their poses and binding free energies (kcal/mol). In this study, We employed AutoDock vina, which is the best bioinformatic tool for docking (Bultum et al, 2022). The prepared protein and ligands were imported to the PyRx software, where docking was performed by Vina mode (Garg et al, 2020).…”

“…LigPlot was used to review protein-ligand interactions in a schematic 2D picture (Mishra and Dey, 2019). Moreover, all interactions of the target protein with the ligand were visualized from the Protein-Ligand Interaction Pro ler (PLIP) webserver (Bultum et al, 2022).…”

“…For the docking, we rst prepared the ligands and target protein by OpenBabel online converter and UCSF Chimera 1.11, respectively (Adetayo and Anyasor, 2022). The SDF le of the ligands was converted to PDB format using open babel (Bultum et al, 2022). Hydrogen atoms were added, hydrogen bonding was optimized, and all the atomic clashes were removed through structure minimization.…”

“…We used PyMOL to visualize the docked protein and ligand complex binding pose and to convert the PDBQT format of the PyRx output to PDB format. Then, the PDB format of the docked target protein and ligand complex were analyzed using BIOVIA Discovery Studio, which showed the places of binding sites interactions between the receptor and the docked ligand (Bultum et al, 2022). LigPlot was used to review protein-ligand interactions in a schematic 2D picture (Mishra and Dey, 2019).…”