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Virtual Screening of TIPO Natural Compound Database on
HCV NS5B RNA Polymerase
虛擬篩選在智慧局天然小分子資料庫上進行C型肝炎RNA聚合酶NS5B抑制劑之篩選結果
Material and method
Crystal Structures of HCV NS5B with its inhibitor (1Z4U, 1YVF,
2FVC) were being used as the research protein. We first draw the
structure then do energy minimization in Sybyl® . Then we used
the generated compounds to go on docking by using GOLD® . We
used Catalyst® CoMFA, and CoMSIA to do hypothesis analysis.
For the database screening part, we used the same parameter
which we established and ran it with GOLD® , later we estimate the
inhibition ability of each potent molecule.
Virtual Screening is one methodology of CADD (computer-assisted drug design), it can not only increase the hit rate of screening result library, but
also decrease the manpower of laborious experiment, and hence it has become a powerful tool used by giant pharmaceutical companies. We have
established the 3D model of HCV NS5B RNA polymerase, validated by known inhibitors, run the virtual screening process on TIPO Natural Compound
Database consisted of around 40K molecules. Our result shows that on the top 200 hits, there are some compounds can be found on Taiwan native
species or common TCMs, including Agrimonia pilosa, Mallotus japonicus, Magnolia officinalis etc. Our future work is to establish the molecular level
assay system for NS5B and to collect those related plants to test the reliability of our model.
Result
1. Result & Model
2. Database Screening Results
Figure 8. (Left) CoMFA test set cross validation result. Training set non-cross
validation 0.993. Leave-one-out validation 0.755. Cross-validation validation
0.653.(data not shown). Test set cross validation qcv
2=0.6826.(Right) CoMSIA
test se cross validation result. Training set non-cross validation 0.991. Leave-
one-out validation 0.775. Cross-validation validation 0.663.(data not shown).
Test set cross validation qcv2=0.6917. Both component are 6, and group is 10.
Figure 9. Catalyst® contour map. Light blue contour indicating
hydrophobic interaction, white color contour indicating aromatic
planar, green color contour indicating hydrogen bond acceptor.
Table 1. Among the top 25 potential hits, 6 hits exist in Taiwan
native plants by reports, the following table shows the ID,
score, Name, MW and Formula. These Natural Compounds’
structure and the natural resources are also shown here.
Conclusion
1.In pharmacophore study, we defined several regions to have hydrogen bond
acceptor, and several region to have hydrophobic effect and steric effect. We
can refine structure in the future by CoMFA, CoMSIA model.
2.The CoMFA, CoMSIA, and Catalyst® models we generated here can aid us in
the estimation of unknown docked molecule, narrowing down the possible hits.
3.The possible natural compound hits selected by this methodology can help us
to accelerate our drug discovery progress, these hits need to be verified by
NS5B enzyme activity assay in the future.
Wei Lien1,2 、Thy-Hou Lin2 and Ting-Shou Chen1 (連偉、林志侯、陳廷碩)
Pharmaceutical Analysis Department, Pharmaceutical Technology Division, Biomedical Engineering Research Laboratories, ITRI1
Institute of Molecular Medicine, College of Life Sciences, National Tsing Hua University2
生醫所醫藥組藥物分析研究部1、清華大學生命科學學院分子醫學研究所2
Figure 1. Comparing the crystal ligand with the ligand generated from
GOLD method, 1Z4U(left) and 2FVC(right). The green ligand is from
the crystal structure, and the yellow ligand is generated by Gold
docking. We found that the RMSD values between the both ligands
are 0.357(left) and 0.576(right).
Figure 4. CoMFA contour map. Background protein
indicating lipophilicity. (Color Margin) Green: Steric favor.
Yellow: steric disfavor. Blue: Negative charge favor. Red:
Positive charge favor.
Figure 5. CoMSIA contour map. Background protein
indicating lipophilicity. Cyan color indicating
hydrophobic favor, white color indicating hydrophobic
disfavor.
Figure 7. CoMSIA contour map.
Background protein indicating hydrogen
donor favor. Purple color indicating
hydrogen bond acceptor favor.
Figure 2. Contour map of CoMFA generated by 2FVC
ligands docked into 1Z4U receptor. (Color Margin) Green:
Steric favor. Yellow: steric disfavor. Blue: Negative charge
favor. Red: Positive charge favor.
Figure 3. Contour map generated by 1Z4U+1YVF ligands
docked into 1Z4U receptor. (Color Margin) Green: Steric favor.
Yellow: steric disfavor. Blue: Negative charge favor. Red:
Positive charge favor.
Figure 6. CoMSIA contour map.
Background protein indicating cavity depth.
Green color indicating steric favor, yellow
color indicating steric disfavor.
qcv
2
Actual IC50
Predicted IC50
qcv
2
Actual IC50
Predicted IC50
124898027
Mallotus japonicus
121737157
Agrimonia pilosa
55620649
Agrimonia pilosa
55620627
Agrimonia pilosa
55829581
Agrimonia pilosa
138635110
Magnolia officinalis

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Biotechnology Poster Session. William Lien

  • 1. Virtual Screening of TIPO Natural Compound Database on HCV NS5B RNA Polymerase 虛擬篩選在智慧局天然小分子資料庫上進行C型肝炎RNA聚合酶NS5B抑制劑之篩選結果 Material and method Crystal Structures of HCV NS5B with its inhibitor (1Z4U, 1YVF, 2FVC) were being used as the research protein. We first draw the structure then do energy minimization in Sybyl® . Then we used the generated compounds to go on docking by using GOLD® . We used Catalyst® CoMFA, and CoMSIA to do hypothesis analysis. For the database screening part, we used the same parameter which we established and ran it with GOLD® , later we estimate the inhibition ability of each potent molecule. Virtual Screening is one methodology of CADD (computer-assisted drug design), it can not only increase the hit rate of screening result library, but also decrease the manpower of laborious experiment, and hence it has become a powerful tool used by giant pharmaceutical companies. We have established the 3D model of HCV NS5B RNA polymerase, validated by known inhibitors, run the virtual screening process on TIPO Natural Compound Database consisted of around 40K molecules. Our result shows that on the top 200 hits, there are some compounds can be found on Taiwan native species or common TCMs, including Agrimonia pilosa, Mallotus japonicus, Magnolia officinalis etc. Our future work is to establish the molecular level assay system for NS5B and to collect those related plants to test the reliability of our model. Result 1. Result & Model 2. Database Screening Results Figure 8. (Left) CoMFA test set cross validation result. Training set non-cross validation 0.993. Leave-one-out validation 0.755. Cross-validation validation 0.653.(data not shown). Test set cross validation qcv 2=0.6826.(Right) CoMSIA test se cross validation result. Training set non-cross validation 0.991. Leave- one-out validation 0.775. Cross-validation validation 0.663.(data not shown). Test set cross validation qcv2=0.6917. Both component are 6, and group is 10. Figure 9. Catalyst® contour map. Light blue contour indicating hydrophobic interaction, white color contour indicating aromatic planar, green color contour indicating hydrogen bond acceptor. Table 1. Among the top 25 potential hits, 6 hits exist in Taiwan native plants by reports, the following table shows the ID, score, Name, MW and Formula. These Natural Compounds’ structure and the natural resources are also shown here. Conclusion 1.In pharmacophore study, we defined several regions to have hydrogen bond acceptor, and several region to have hydrophobic effect and steric effect. We can refine structure in the future by CoMFA, CoMSIA model. 2.The CoMFA, CoMSIA, and Catalyst® models we generated here can aid us in the estimation of unknown docked molecule, narrowing down the possible hits. 3.The possible natural compound hits selected by this methodology can help us to accelerate our drug discovery progress, these hits need to be verified by NS5B enzyme activity assay in the future. Wei Lien1,2 、Thy-Hou Lin2 and Ting-Shou Chen1 (連偉、林志侯、陳廷碩) Pharmaceutical Analysis Department, Pharmaceutical Technology Division, Biomedical Engineering Research Laboratories, ITRI1 Institute of Molecular Medicine, College of Life Sciences, National Tsing Hua University2 生醫所醫藥組藥物分析研究部1、清華大學生命科學學院分子醫學研究所2 Figure 1. Comparing the crystal ligand with the ligand generated from GOLD method, 1Z4U(left) and 2FVC(right). The green ligand is from the crystal structure, and the yellow ligand is generated by Gold docking. We found that the RMSD values between the both ligands are 0.357(left) and 0.576(right). Figure 4. CoMFA contour map. Background protein indicating lipophilicity. (Color Margin) Green: Steric favor. Yellow: steric disfavor. Blue: Negative charge favor. Red: Positive charge favor. Figure 5. CoMSIA contour map. Background protein indicating lipophilicity. Cyan color indicating hydrophobic favor, white color indicating hydrophobic disfavor. Figure 7. CoMSIA contour map. Background protein indicating hydrogen donor favor. Purple color indicating hydrogen bond acceptor favor. Figure 2. Contour map of CoMFA generated by 2FVC ligands docked into 1Z4U receptor. (Color Margin) Green: Steric favor. Yellow: steric disfavor. Blue: Negative charge favor. Red: Positive charge favor. Figure 3. Contour map generated by 1Z4U+1YVF ligands docked into 1Z4U receptor. (Color Margin) Green: Steric favor. Yellow: steric disfavor. Blue: Negative charge favor. Red: Positive charge favor. Figure 6. CoMSIA contour map. Background protein indicating cavity depth. Green color indicating steric favor, yellow color indicating steric disfavor. qcv 2 Actual IC50 Predicted IC50 qcv 2 Actual IC50 Predicted IC50 124898027 Mallotus japonicus 121737157 Agrimonia pilosa 55620649 Agrimonia pilosa 55620627 Agrimonia pilosa 55829581 Agrimonia pilosa 138635110 Magnolia officinalis