Abstract
The aim of the analysis of just 13 natural products of plants was to predict the most likely effective artificial mixtures of 2-3 most effective natural products on leukemia cells from over 364 possible mixtures. The natural product selected included resveratrol, honokiol, chrysin, limonene, cholecalciferol, cerulenin, aloe emodin, and salicin and had over 600 potential protein targets. Target profiling used the Ontomine set of tools for literature searches of potential binding proteins, binding constant predictions, binding site predictions, and pathway network pattern analysis. The analyses indicated that 6 of the 13 natural products predicted binding proteins which were important targets for established cancer treatments. Improvements in effectiveness were predicted for artificial combinations of 2 or 3 natural products. That effect might be attributed to drug synergism rather than increased numbers of binding proteins bound (dose effects). Among natural products, the combinations of aloe emodin with mevinolin and honokiol were predicted to be the most effective combination for AML-related predicted binding proteins. Therefore, plant extracts may in future provide more effective medicines than the single purified natural products of modern medicine, in some cases.
Highlights
Plant-derived secondary metabolites have been used to treat acute infections, health disorders, and chronic illness for tens of thousands of years
Ontomine searches were performed against large and manually curated databases. ey included (i) Literature searches based on experimentally determined properties from around 100.000 diverse small molecules, collected from databases, encyclopedias, and other literature followed by expert hand-curation; (ii) BioAssay Knowledgebase that was compiled from over 500 bioassay data found at NCBI-PubChem; (iii) Target Protein Knowledgebase that was compiled by curation among the ∼1500 proteins from DrugBank at NCBI-PubChem. (iv) Pathway Analysis; KEGG pathways were used as references ( p:// p.genome.jp/pub/kegg/); (v) Docking Algorithms were used to identify molecular binding sites and predict ligand binding constants
Multiple (4–8) natural products were predicted to interact with bioassays targets, guanine nucleotide binding protein, alpha activating polypeptide O, multidrug resistance protein 1, myeloid or lymphoid or mixed-lineage leukaemia protein, and runt-related transcription factor isoform 1 (AML1c)
Summary
Plant-derived secondary metabolites have been used to treat acute infections, health disorders, and chronic illness for tens of thousands of years. During the last 100 years have natural products been largely replaced by synthetic drugs [1]. Important anticancer agents have to be extracted from plants, due to their complex structures that o en contain several chiral centers. Some patients show resistance to known treatments [2]. Erefore, new treatments with different modes of action are constantly sought. Plants are an abundant source of new natural products. Estimates of 200,000 natural products in plant species have been revised upward as mass spectrometry techniques have developed [3]. Omics methods, and good practice standards are promising to deliver many new medicines based on plant natural products [4]
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