Limits...
In silico profiling for secondary metabolites from Lepidium meyenii (maca) by the pharmacophore and ligand-shape-based joint approach

View Article: PubMed Central - PubMed

ABSTRACT

Background: Lepidium meyenii Walpers (maca) is an herb known as a traditional nutritional supplement and widely used in Peru, North America, and Europe to enhance human fertility and treat osteoporosis. The secondary metabolites of maca, namely, maca alkaloids, macaenes, and macamides, are bioactive compounds, but their targets are undefined.

Methods: The pharmacophore-based PharmaDB targets database screening joint the ligand shape similarity-based WEGA validation approach is proposed to predict the targets of these unique constituents and was performed using Discovery Studio 4.5 and PharmaDB. A compounds–targets–diseases network was established using Cytoscape 3.2. These suitable targets and their genes were calculated and analyzed using ingenuity pathway analysis and GeneMANIA.

Results: Certain targets were identified in osteoporosis (8 targets), prostate cancer (9 targets), and kidney diseases (11 targets). This was the first study to identify the targets of these bioactive compounds in maca for cardiovascular diseases (29 targets). The compound with the most targets (46) was an amide alkaloid (MA-24).

Conclusion: In silico target fishing identified maca’s traditional effects on treatment and prevention of osteoporosis, prostate cancer, and kidney diseases, and its potential function of treating cardiovascular diseases, as the most important of this herb’s possible activities.

Electronic supplementary material: The online version of this article (doi:10.1186/s13020-016-0112-y) contains supplementary material, which is available to authorized users.

No MeSH data available.


Related in: MedlinePlus

The diversity of compounds analyzed by the scaffold-based classification approach (SCA). CID means compound class ID, categories by complexity; the SCA also outputs the following structural descriptor values: (1) Cyclicity side chain value; (2) AE average electronegativity; (3) HD number of H-bond donors; (3) HA number of H-bond acceptors; (4) AB number of aromatic bonds; (5) ATMS number of non-H atoms; (6) BNDS number of non-H-involved bonds; (7) SSSRS number of the smallest set of smallest rings; (8) AZ average atomic numbers; (9) RB number of rotating bonds; (10) MW molecular weight
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC5037646&req=5

Fig1: The diversity of compounds analyzed by the scaffold-based classification approach (SCA). CID means compound class ID, categories by complexity; the SCA also outputs the following structural descriptor values: (1) Cyclicity side chain value; (2) AE average electronegativity; (3) HD number of H-bond donors; (3) HA number of H-bond acceptors; (4) AB number of aromatic bonds; (5) ATMS number of non-H atoms; (6) BNDS number of non-H-involved bonds; (7) SSSRS number of the smallest set of smallest rings; (8) AZ average atomic numbers; (9) RB number of rotating bonds; (10) MW molecular weight

Mentions: In modern drug discovery, large compound libraries are compared, and the diversity of these libraries must be analyzed [42]. The constituents collected and synthesized from maca could be divided into eight compound classes (Fig. 1). The 40 compounds examined in this research were fished by targets (Fig. 2). The compounds with higher degree values were distributed across different categories, such as amide alkaloids (MA-24; 25), macaenes (MA-32; 33), and synthetic amides (MA-43; 44). Compounds that participate in more interactions than other components have a higher bioactivity value.Fig. 1


In silico profiling for secondary metabolites from Lepidium meyenii (maca) by the pharmacophore and ligand-shape-based joint approach
The diversity of compounds analyzed by the scaffold-based classification approach (SCA). CID means compound class ID, categories by complexity; the SCA also outputs the following structural descriptor values: (1) Cyclicity side chain value; (2) AE average electronegativity; (3) HD number of H-bond donors; (3) HA number of H-bond acceptors; (4) AB number of aromatic bonds; (5) ATMS number of non-H atoms; (6) BNDS number of non-H-involved bonds; (7) SSSRS number of the smallest set of smallest rings; (8) AZ average atomic numbers; (9) RB number of rotating bonds; (10) MW molecular weight
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5037646&req=5

Fig1: The diversity of compounds analyzed by the scaffold-based classification approach (SCA). CID means compound class ID, categories by complexity; the SCA also outputs the following structural descriptor values: (1) Cyclicity side chain value; (2) AE average electronegativity; (3) HD number of H-bond donors; (3) HA number of H-bond acceptors; (4) AB number of aromatic bonds; (5) ATMS number of non-H atoms; (6) BNDS number of non-H-involved bonds; (7) SSSRS number of the smallest set of smallest rings; (8) AZ average atomic numbers; (9) RB number of rotating bonds; (10) MW molecular weight
Mentions: In modern drug discovery, large compound libraries are compared, and the diversity of these libraries must be analyzed [42]. The constituents collected and synthesized from maca could be divided into eight compound classes (Fig. 1). The 40 compounds examined in this research were fished by targets (Fig. 2). The compounds with higher degree values were distributed across different categories, such as amide alkaloids (MA-24; 25), macaenes (MA-32; 33), and synthetic amides (MA-43; 44). Compounds that participate in more interactions than other components have a higher bioactivity value.Fig. 1

View Article: PubMed Central - PubMed

ABSTRACT

Background: Lepidium meyenii Walpers (maca) is an herb known as a traditional nutritional supplement and widely used in Peru, North America, and Europe to enhance human fertility and treat osteoporosis. The secondary metabolites of maca, namely, maca alkaloids, macaenes, and macamides, are bioactive compounds, but their targets are undefined.

Methods: The pharmacophore-based PharmaDB targets database screening joint the ligand shape similarity-based WEGA validation approach is proposed to predict the targets of these unique constituents and was performed using Discovery Studio 4.5 and PharmaDB. A compounds–targets–diseases network was established using Cytoscape 3.2. These suitable targets and their genes were calculated and analyzed using ingenuity pathway analysis and GeneMANIA.

Results: Certain targets were identified in osteoporosis (8 targets), prostate cancer (9 targets), and kidney diseases (11 targets). This was the first study to identify the targets of these bioactive compounds in maca for cardiovascular diseases (29 targets). The compound with the most targets (46) was an amide alkaloid (MA-24).

Conclusion: In silico target fishing identified maca’s traditional effects on treatment and prevention of osteoporosis, prostate cancer, and kidney diseases, and its potential function of treating cardiovascular diseases, as the most important of this herb’s possible activities.

Electronic supplementary material: The online version of this article (doi:10.1186/s13020-016-0112-y) contains supplementary material, which is available to authorized users.

No MeSH data available.


Related in: MedlinePlus