Limits...
Predicting in silico which mixtures of the natural products of plants might most effectively kill human leukemia cells?

El-Shemy HA, Aboul-Enein KM, Lightfoot DA - Evid Based Complement Alternat Med (2013)

Bottom Line: Improvements in effectiveness were predicted for artificial combinations of 2 or 3 natural products.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.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Agriculture Research Park (FARP) and Department of Biochemistry, Faculty of Agriculture, Cairo University, Giza 12513, Egypt.

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.

No MeSH data available.


Related in: MedlinePlus

Subnetwork dysregulated in AML versus normal white blood cells. Interaction-type annotations from KEGG were shown as the letters above the arrows where; E was enzymatic; T was transcription with subscript + showing activation and − showing inhibition; B was protein-to-protein binding. Subscripts for the predicted protein-to-protein interactions were c: for compound interactions, +: activation, −: inhibition, i: an indirect effect, s: a state change, p+: phosphorylation, p−: dephosphorylation, m: methylation, u: ubiquitination, g: glycosylation and “none” for missing information.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3569894&req=5

fig1: Subnetwork dysregulated in AML versus normal white blood cells. Interaction-type annotations from KEGG were shown as the letters above the arrows where; E was enzymatic; T was transcription with subscript + showing activation and − showing inhibition; B was protein-to-protein binding. Subscripts for the predicted protein-to-protein interactions were c: for compound interactions, +: activation, −: inhibition, i: an indirect effect, s: a state change, p+: phosphorylation, p−: dephosphorylation, m: methylation, u: ubiquitination, g: glycosylation and “none” for missing information.

Mentions: The reference databases and software of Ontomine were used for predictive analysis. Ontomine was chosen because it provided an innovative chemoinformatics prediction tool based on the presence or absence of chemical group(s) of a set of related natural products. Ontomine searches were performed against large and manually curated databases. They 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 (details given in Figure 1). (iv) Pathway Analysis; KEGG pathways were used as references (ftp://ftp.genome.jp/pub/kegg/); (v) Docking Algorithms were used to identify molecular binding sites and predict ligand binding constants. Ontomine databases and tools are among those used widely in this field [4].


Predicting in silico which mixtures of the natural products of plants might most effectively kill human leukemia cells?

El-Shemy HA, Aboul-Enein KM, Lightfoot DA - Evid Based Complement Alternat Med (2013)

Subnetwork dysregulated in AML versus normal white blood cells. Interaction-type annotations from KEGG were shown as the letters above the arrows where; E was enzymatic; T was transcription with subscript + showing activation and − showing inhibition; B was protein-to-protein binding. Subscripts for the predicted protein-to-protein interactions were c: for compound interactions, +: activation, −: inhibition, i: an indirect effect, s: a state change, p+: phosphorylation, p−: dephosphorylation, m: methylation, u: ubiquitination, g: glycosylation and “none” for missing information.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3569894&req=5

fig1: Subnetwork dysregulated in AML versus normal white blood cells. Interaction-type annotations from KEGG were shown as the letters above the arrows where; E was enzymatic; T was transcription with subscript + showing activation and − showing inhibition; B was protein-to-protein binding. Subscripts for the predicted protein-to-protein interactions were c: for compound interactions, +: activation, −: inhibition, i: an indirect effect, s: a state change, p+: phosphorylation, p−: dephosphorylation, m: methylation, u: ubiquitination, g: glycosylation and “none” for missing information.
Mentions: The reference databases and software of Ontomine were used for predictive analysis. Ontomine was chosen because it provided an innovative chemoinformatics prediction tool based on the presence or absence of chemical group(s) of a set of related natural products. Ontomine searches were performed against large and manually curated databases. They 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 (details given in Figure 1). (iv) Pathway Analysis; KEGG pathways were used as references (ftp://ftp.genome.jp/pub/kegg/); (v) Docking Algorithms were used to identify molecular binding sites and predict ligand binding constants. Ontomine databases and tools are among those used widely in this field [4].

Bottom Line: Improvements in effectiveness were predicted for artificial combinations of 2 or 3 natural products.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.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Agriculture Research Park (FARP) and Department of Biochemistry, Faculty of Agriculture, Cairo University, Giza 12513, Egypt.

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.

No MeSH data available.


Related in: MedlinePlus