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Molecular docking studies of Traditional Chinese Medicinal compounds against known protein targets to treat non-small cell lung carcinomas.

Zhao GF, Huang ZA, Du XK, Yang ML, Huang DD, Zhang S - Mol Med Rep (2016)

Bottom Line: In silico drug design using virtual screening, absorption, distribution, metabolism and excretion (ADME)/Tox data analysis, automated docking and molecular dynamics simulations for the determination of lead compounds for further in vitro analysis is a cost effective strategy.The present study used this strategy to discover novel lead compounds from an in-house database of Traditional Chinese Medicinal (TCM) compounds against epithelial growth factor receptor (EGFR) protein for targeting non-small cell lung cancer (NSCLC).After virtual screening of an initial dataset of 2,242 TCM compounds, leads were identified based on binding energy and ADME/Tox data and subjected to automated docking followed by molecular dynamics simulation.

View Article: PubMed Central - PubMed

Affiliation: Department of Cardiothoracic Surgery, Ningbo No. 2 Hospital, Ningbo, Zhejiang 315010, P.R. China.

ABSTRACT
In silico drug design using virtual screening, absorption, distribution, metabolism and excretion (ADME)/Tox data analysis, automated docking and molecular dynamics simulations for the determination of lead compounds for further in vitro analysis is a cost effective strategy. The present study used this strategy to discover novel lead compounds from an in-house database of Traditional Chinese Medicinal (TCM) compounds against epithelial growth factor receptor (EGFR) protein for targeting non-small cell lung cancer (NSCLC). After virtual screening of an initial dataset of 2,242 TCM compounds, leads were identified based on binding energy and ADME/Tox data and subjected to automated docking followed by molecular dynamics simulation. Triptolide, a top compound identified by this vigorous in silico screening, was then tested in vitro on the H2347 cell line carrying wild-type EGFR, revealing an anti-proliferative potency similar to that of known drugs against NSCLC.

No MeSH data available.


Related in: MedlinePlus

(A) RMSD of the EGFR - triptolide complex. The top graph represents EGFR and the bottom graph represents triptolide - RMSD. (B) RMS fluctuation of the amino acids of the EGFR over time. RMSD, root-mean-square deviation; EGFR, epidermal growth factor receptor.
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f5-mmr-14-02-1132: (A) RMSD of the EGFR - triptolide complex. The top graph represents EGFR and the bottom graph represents triptolide - RMSD. (B) RMS fluctuation of the amino acids of the EGFR over time. RMSD, root-mean-square deviation; EGFR, epidermal growth factor receptor.

Mentions: The lead compound triptolide was selected based on the binding energy and number of hydrogen bonds. Its complex with EGFR was subjected to root-mean-square (RMS) simulation over 100 nsec. In a neutral aqueous environment, all trajectories converged within the first five nsec. All calculations were based on the backbone atoms of the protein. RMS deviation (RMSD), RMS fluctuation, radius of gyration (Rg) and hydrogen bond analysis of the complex were performed. The stability of the complex of EGFR with triptolide using RMSD calculations (Fig. 5A) revealed that the binding of the drug was stable. The fluctuations observed in the plot are the attributes of the changing position of the drug in the binding pocket. The fluctuations at each amino acid position were also calculated for the complex using the Grms tool, revealing that the fluctuation of the amino acids in the binding pocket of the complex was reduced, indicating a stable interaction of the drug with EGFR (Fig. 5B).


Molecular docking studies of Traditional Chinese Medicinal compounds against known protein targets to treat non-small cell lung carcinomas.

Zhao GF, Huang ZA, Du XK, Yang ML, Huang DD, Zhang S - Mol Med Rep (2016)

(A) RMSD of the EGFR - triptolide complex. The top graph represents EGFR and the bottom graph represents triptolide - RMSD. (B) RMS fluctuation of the amino acids of the EGFR over time. RMSD, root-mean-square deviation; EGFR, epidermal growth factor receptor.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4940108&req=5

f5-mmr-14-02-1132: (A) RMSD of the EGFR - triptolide complex. The top graph represents EGFR and the bottom graph represents triptolide - RMSD. (B) RMS fluctuation of the amino acids of the EGFR over time. RMSD, root-mean-square deviation; EGFR, epidermal growth factor receptor.
Mentions: The lead compound triptolide was selected based on the binding energy and number of hydrogen bonds. Its complex with EGFR was subjected to root-mean-square (RMS) simulation over 100 nsec. In a neutral aqueous environment, all trajectories converged within the first five nsec. All calculations were based on the backbone atoms of the protein. RMS deviation (RMSD), RMS fluctuation, radius of gyration (Rg) and hydrogen bond analysis of the complex were performed. The stability of the complex of EGFR with triptolide using RMSD calculations (Fig. 5A) revealed that the binding of the drug was stable. The fluctuations observed in the plot are the attributes of the changing position of the drug in the binding pocket. The fluctuations at each amino acid position were also calculated for the complex using the Grms tool, revealing that the fluctuation of the amino acids in the binding pocket of the complex was reduced, indicating a stable interaction of the drug with EGFR (Fig. 5B).

Bottom Line: In silico drug design using virtual screening, absorption, distribution, metabolism and excretion (ADME)/Tox data analysis, automated docking and molecular dynamics simulations for the determination of lead compounds for further in vitro analysis is a cost effective strategy.The present study used this strategy to discover novel lead compounds from an in-house database of Traditional Chinese Medicinal (TCM) compounds against epithelial growth factor receptor (EGFR) protein for targeting non-small cell lung cancer (NSCLC).After virtual screening of an initial dataset of 2,242 TCM compounds, leads were identified based on binding energy and ADME/Tox data and subjected to automated docking followed by molecular dynamics simulation.

View Article: PubMed Central - PubMed

Affiliation: Department of Cardiothoracic Surgery, Ningbo No. 2 Hospital, Ningbo, Zhejiang 315010, P.R. China.

ABSTRACT
In silico drug design using virtual screening, absorption, distribution, metabolism and excretion (ADME)/Tox data analysis, automated docking and molecular dynamics simulations for the determination of lead compounds for further in vitro analysis is a cost effective strategy. The present study used this strategy to discover novel lead compounds from an in-house database of Traditional Chinese Medicinal (TCM) compounds against epithelial growth factor receptor (EGFR) protein for targeting non-small cell lung cancer (NSCLC). After virtual screening of an initial dataset of 2,242 TCM compounds, leads were identified based on binding energy and ADME/Tox data and subjected to automated docking followed by molecular dynamics simulation. Triptolide, a top compound identified by this vigorous in silico screening, was then tested in vitro on the H2347 cell line carrying wild-type EGFR, revealing an anti-proliferative potency similar to that of known drugs against NSCLC.

No MeSH data available.


Related in: MedlinePlus