Chemoinformatic Database Building and in Silico Hit-Identification of Potential Multi-Targeting Bioactive Compounds Extracted from Mushroom Species
Abstract
:1. Introduction
2. Results and Discussion
2.1. Multi-Targeted Anticancer Compounds
2.2. Multi-Targeted Anti-Inflammatory Compounds
2.3. Multi-Targeted Compounds in Neurodegenerative Disease
2.4. Multi-Targeted Compounds in Metabolic Diseases
2.5. Non Selective MTAs
3. Materials and Methods
3.1. Database Building
3.2. Database Preparation
3.3. Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries
3.4. Targets Preparation and Clustering
3.5. Docking Simulation
3.6. Data Analysis
4. Conclusions
Supplementary Materials
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Sample Availability: Not Available. |
Name | Structure | Targets | G-Score (kcal/mol) |
---|---|---|---|
Inotopyrrole | c-Met | −8.05 | |
VEGFR2 | −8.08 | ||
EGFR | −8.22 | ||
MEK2 | −8.09 | ||
Marasmone 21 | c-Met | −8.34 | |
MEK1 | −8.02 | ||
Fascicularone A | c-Met | −8.51 | |
SIRT1 | −8.78 | ||
Enokipodin H | c-Met | −8.79 | |
GSK3β | −8.21 | ||
SIRT1 | −8.40 | ||
Confluentin | VEGFR2 | −10.16 | |
EGFR | −10.16 | ||
PDK1 | −8.97 | ||
SIRT1 | −8.75 | ||
Blennin C | c-Met | −10.16 | |
MEK1 | −8.97 | ||
SIRT1 | −8.75 | ||
Acromelic acid A | PDK1 | −8.50 | |
CA IX | −8.22 | ||
CA XII | −8.32 | ||
Ganomycin B | VEGFR2 | −8.44 | |
B-RAF V600E | −8.29 | ||
PDK1 | −8.74 | ||
SIRT1 | −8.19 | ||
Atromentic acid | c-Met | −8.51 | |
VEGFR2 | −9.09 | ||
B-RAF wt | −8.5 | ||
PDK1 | −8.14 | ||
1-O-Acetyl-3-epi-illudol | c-Met | −8.97 | |
MEK1 | −9.37 | ||
PDK1 | −8.25 | ||
GSK3β | −8.4 | ||
Gentianal | c-Met | −8.77 | |
VEGFR2 | −8.5 | ||
EGFR | −8.27 | ||
B-RAF V600E | −8.08 | ||
BMX | −8.02 | ||
Hericenone H | c-Met | −8.82 | |
EGFR | −8.48 | ||
Hericenol A | c-Met | −8.68 | |
MEK1 | −8.00 | ||
MEK2 | −8.17 | ||
SIRT1 | −8.46 | ||
Erinacerin P | MEK1 | −8.48 | |
SGK1 | −8.06 | ||
Erinacerin A | EGFR | −8.2 | |
B-RAF wt | −8.48 | ||
SIRT1 | −8.36 | ||
Hericenol B | c-Met | −9.52 | |
EGFR | −8.41 | ||
MEK1 | −8.66 | ||
MEK2 | −8.89 | ||
SIRT1 | −9.11 | ||
Genistin | VEGFR2 | −8.22 | |
B-RAF wt | −8.91 | ||
MEK1 | −9.52 | ||
SGK1 | −8.39 | ||
Illudinine | Aurora A kinase | −8.59 | |
c-Met | −8.89 | ||
VEGFR2 | −8.79 | ||
B-RAF V600E | −8.9 | ||
BMX | −8.13 | ||
GSK3β | −8.67 | ||
SIRT1 | −8.11 | ||
Daldinal | c-Met | −8.62 | |
VEGFR2 | −8.85 | ||
B-RAF V600E | −8.03 | ||
PI3Kγ | −8.34 | ||
Narigin | ERK1 | −8.42 | |
B-RAF wt | −9.89 | ||
B-RAF V600E | −9.64 | ||
MEK1 | −9.12 | ||
BMX | −10.04 | ||
SGK1 | −8.99 | ||
(R)-Torosachrysone | c-Met | −8.96 | |
VEGFR2 | −8.69 | ||
ERK1 | −8.29 | ||
MEK1 | −8.53 | ||
PI3Kα | −8.06 | ||
BMX | −8.22 | ||
GSK3β | −8.55 | ||
SIRT1 | −8.04 |
Name | Structure | Target | G-Score (kcal/mol) |
---|---|---|---|
Illudacetalic Acid | COX-2 | −8.42 | |
Adenosine A2A R | −8.09 | ||
Pterulinic Acid | COX-1 | −8.97 | |
Glucocorticoid R | −8.73 | ||
Orellanine | COX-1 | −8.64 | |
COX-2 | −8.85 | ||
Glucocorticoid R | −8.39 | ||
Bisnoryangonin | COX-2 | −8.17 | |
Glucocorticoid R | −8.34 | ||
1-Naphthylacetic acid | COX-1 | −9.33 | |
COX-2 | −8.33 | ||
Pulvinic acid | COX-2 | −8.56 | |
Glucocorticoid R | −8.23 |
Name | Structure | Targets | G-Score (kcal/mol) |
---|---|---|---|
Erinacerin O | AChE | −8.70 | |
COMT | −8.00 | ||
Cordysinin A | GSK3β | −8.13 | |
AChE | −8.47 | ||
COX-2 | −8.34 | ||
Hesperidin | AChE | −10.67 | |
COMT | −9.18 | ||
AdenosineA2A R | −9.63 | ||
Vitexin | AChE | −8.93 | |
COMT | −8.09 | ||
Pterulone | AChE | −8.38 | |
MAO-B | −8.16 | ||
COX-2 | −8.54 | ||
GSK3β | −8.09 |
Name | Structure | Targets | G-Score (kcal/mol) |
---|---|---|---|
Hericenone A | Insulin R | −8.62 | |
IGFR | −8.23 | ||
PPAR-α | −8.49 | ||
PPAR-γ | −8.81 | ||
GSK3β | −8.01 | ||
O-xylotocopherol | PPAR-α | −9.00 | |
PPAR-γ | −9.20 | ||
3,4-Dihydro-2-methyl-2-(2-oxo-4-methyl-3-pentenyl)-5-methoxy-2H-furo[3,4-h][1]benzopyran-9(7H)-one | PPAR-α | −8.05 | |
PPAR-γ | −8.27 | ||
7-Acetyl-4-methylazulene-1-carboxylic acid | Insulin R | −8.12 | |
PPAR-α | −8.43 | ||
4,6-Dihydroxyisobenzofuran-1,3-dione | PKA C-α | −8.84 | |
PPAR-γ | −8.49 | ||
GSK3β | −8.10 |
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Maruca, A.; Moraca, F.; Rocca, R.; Molisani, F.; Alcaro, F.; Gidaro, M.C.; Alcaro, S.; Costa, G.; Ortuso, F. Chemoinformatic Database Building and in Silico Hit-Identification of Potential Multi-Targeting Bioactive Compounds Extracted from Mushroom Species. Molecules 2017, 22, 1571. https://doi.org/10.3390/molecules22091571
Maruca A, Moraca F, Rocca R, Molisani F, Alcaro F, Gidaro MC, Alcaro S, Costa G, Ortuso F. Chemoinformatic Database Building and in Silico Hit-Identification of Potential Multi-Targeting Bioactive Compounds Extracted from Mushroom Species. Molecules. 2017; 22(9):1571. https://doi.org/10.3390/molecules22091571
Chicago/Turabian StyleMaruca, Annalisa, Federica Moraca, Roberta Rocca, Fulvia Molisani, Francesca Alcaro, Maria Concetta Gidaro, Stefano Alcaro, Giosuè Costa, and Francesco Ortuso. 2017. "Chemoinformatic Database Building and in Silico Hit-Identification of Potential Multi-Targeting Bioactive Compounds Extracted from Mushroom Species" Molecules 22, no. 9: 1571. https://doi.org/10.3390/molecules22091571
APA StyleMaruca, A., Moraca, F., Rocca, R., Molisani, F., Alcaro, F., Gidaro, M. C., Alcaro, S., Costa, G., & Ortuso, F. (2017). Chemoinformatic Database Building and in Silico Hit-Identification of Potential Multi-Targeting Bioactive Compounds Extracted from Mushroom Species. Molecules, 22(9), 1571. https://doi.org/10.3390/molecules22091571