An alignment-free, three dimensional quantitative structure-activity relationship (3D-QSAR) analysis has been performed on a series of
The
Recently,
Previous data suggest that
The docking study was done using CDOCKER algorithm and the picture of the virtual receptor site was validated by investigation interactions between receptor and some inhibitors. CDOCKER is an implementation of a CHARMm-based docking tool using a rigid receptor that generates several prime random ligand orientations within the receptor active site followed by MD-based simulated annealing and final refinement by minimization [
This paper intended to get a better pharmacophoric pattern of these compounds and thus we obtained an alignment-independent 3D-QSAR model for the potency of 40 from total 47
Structures and potencies toward HepG2 tumor cell lines of compounds
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Comp. | Substituents | pIC50 (exp.) | pIC50 (cal.) | ||||
R1 | R2 | R3 | R7 | R9 | |||
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3,4,5-trimethoxyphenyl | — | CO2C2H5 | H | H | 3.78 | 3.97 |
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3,4,5-trimethoxyphenyl | — | CO2H | H |
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3.68 | 3.64 |
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3,4,5-trimethoxyphenyl | — | CONH(CH2)2OH | H |
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4.06 | 3.92 |
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H | — | CONH(CH2)2NH2 | H |
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4.08 | 4.24 |
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H | — | CONH(CH2)6NH2 | H |
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4.02 | 3.96 |
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CH3 | — | CONH(CH2)2OH | H |
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3.85 | 4.15 |
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CH3 | — | CONH(CH2)2NH2 | H |
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4.66 | 4.68 |
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CH3 | — | CONH(CH2)2NH2 | H | CH2C6H5 | 4.46 | 3.77 |
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H | — | CONH(CH2)2NH2 | H | CH2C6H5 | 3.98 | 3.9 |
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H | — | CONH(CH2)6NH2 | H | CH2C6H5 | 4.23 | 4.27 |
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CH3 | — | CH2OH | H |
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3.89 | 3.74 |
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CH3 | — | CHO | H |
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3.84 | 3.69 |
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CH3 | CH2C6H5 | H | H | H | 4.16 | 4.23 |
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CH3 | (CH2)3C6H5 | H | H | H | 4.48 | 4.56 |
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CH3 | CH2C6H5 | CO2C2H5 | H | H | 4.28 | 4.22 |
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CH3 | CH2C6H5 | H | OCH3 | H | 4.26 | 4.08 |
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H |
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H | H | H | 4.03 | 4.04 |
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H | CH2C6H5 | H | H | H | 4.11 | 4.22 |
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H | (CH2)3C6H5 | H | H | H | 4.35 | 4.42 |
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CH3 | — | H | OH | C2H5 | 3.87 | 3.89 |
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CH3 | — | H | OH |
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4.09 | 4.42 |
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CH3 | — | H | OH |
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3.94 | 4.03 |
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CH3 | — | H | OH | (CH2)3C6H5 | 4.55 | 4.52 |
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CH3 | — | H | OC2H5 | C2H5 | 4.16 | 4.21 |
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CH3 | — | H | OCH2C6F5 | C2H5 | 3.63 | 3.71 |
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CH3 | — | H | OC2H5 |
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4.36 | 4.38 |
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CH3 | — | H | OCH(CH3)2 |
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4.52 | 4.44 |
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CH3 | — | H | OC4H9 |
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4.81 | 4.74 |
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CH3 | — | H | OC10H21 |
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3.90 | 3.71 |
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CH3 | — | H | OC4H9 |
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4.92 | 4.94 |
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CH3 | — | H | OCH2C6H5 |
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4.65 | 4.66 |
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CH3 | — | H | OCH(CH3)2 | (CH2)3C6H5 | 4.84 | 4.5 |
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CH3 | — | H | OC8H17 | (CH2)3C6H5 | 3.98 | 4.12 |
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CH3 | — | H | OCH2C6H5 | (CH2)3C6H5 | 4.80 | 4.44 |
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CH3 | — | H | OCH2C6F5 | (CH2)3C6H5 | 3.83 | 4.05 |
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CH3 | CH2C6H5 | H | OC2H5 | C2H5 | 4.84 | 4.76 |
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CH3 | CH2C6H5 | H | OCH2C6F5 | C2H5 | 5.80 | 5.65 |
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CH3 | CH2C6H5 | H | OC4H9 |
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5.74 | 5.6 |
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CH3 | CH2C6H5 | H | OCH2C6F5 |
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5.72 | 5.33 |
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CH3 | CH2C6H5 | H | OC8H17 | (CH2)3C6H5 | 5.41 | 5.63 |
A new class of molecular descriptors called GRid-INdependent descriptors has been developed by Pastor et al. [
With the aim of getting a virtual molecular interaction fields (MIFs) pattern of the regions (fingerprint of receptor) so-called virtual receptor sites (VRS) to reveal the most common structural groups in the active compounds, first we should compute the MIFs of nodes. Therefore for the derivation of MIFs we used the four most recommended probes. To represent steric and hydrophobic interaction, hydrogen bond acceptor groups, and hydrogen bond donor groups, we use DRY (hydrophobic probe), O (carbonyl oxygen), and N1 (amide nitrogen), respectively. These probes represent strong noncovalent interactions between ligands and receptor. In addition, to consider molecular shape effects in the receptor-ligand interaction process, and as complementary to point-based interaction information, we used a supplementary probe, called TIP (shape probe), that extracts each ligand’s isosurface at 1 kcal/mol from the field of a normal GRID calculation. This method has been described elsewhere in more details [
In the absence of binding site knowledge it is possible to explore the process of ligand-receptor interactions with the help of the MIFs [
The dataset was adopted from the work of Cao et al. [
The dataset was partitioned into training and test set using most descriptive compound (MDC) method in which the compounds are weighted according to their population density [
The
ALMOND-PLS results of the model developed for the series of
LV |
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SDEC | SDEP |
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1 | 0.63 | 0.46 | 0.45 | 0.34 | 0.41 |
2 | 0.94 | 0.81 | 0.77 | 0.14 | 0.24 |
3 | 0.96 | 0.85 | 0.82 | 0.12 | 0.22 |
4 | 0.97 | 0.80 | 0.81 | 0.09 | 0.23 |
Plot of predicted versus experimental pIC50 values for ALMOND model.
Molecular docking studies were carried out by using CDOCKER (CHARMm-based DOCKER) [
The CDOCKER score (-CDOCKER ENERGY) as a negative value includes receptor-ligand interaction energy and internal ligand strain energy [
PLS coefficients for the model are calculated using the DRY, O, N1, and TIP probes [
PLS coefficient plots for 3D-QSAR model. Direct and reverse correlation with the activity are indicated with positive and negative PLS coefficients, respectively. Bars with the most intensive height in the PLS plots have the most profound impact on the model obtained.
Variables 11–24 and 11–25 which have the strongest positive impact on antitumor potency are within the block of DRY-DRY node pairs. The variable 24 represents a distance of 9.6 Å between hydrophobic nodes due to ring A and phenyl ring in 2-benzyl substituent in the two compounds
Association of structural fragments with GRIND variables. Nodes were selected using ALMOND filtering for DRY and TIP probes. Distances in red are favorable whereas distances in blue are not favorable for antitumor potency.
According to Figure
The results of molecular docking
Docked binding mode of compound
Compound
Within the block of TIP-TIP node pairs, the largest positive impact on antitumor potency is attributed to variables
Visual inspection of variables to identify the most discriminative ones. Red and blue boxes include active and inactive compounds, respectively. The numbers written in red represent the variables which have a high positive impact on antitumor activity and the variables with a significant negative impact are written in blue. If a variable is present (
Within the N1-N1 block in PLS coefficient plot, it is worth mentioning that the negatively correlated bars are located on the left side (representing smaller node-node distances) and the positively correlated variables are positioned on larger node-node distances, that is, on the right side. It is due to the fact that structural elements which exert negative impact on potency are located closer to each other in molecules than those having positive impact. According to the N1-N1 autocorrelogram, regions favorable for hydrogen bond acceptor groups separated by 3.2 Å (i.e., variable 33–8) seem to be characteristic of inactive compounds. They are associated to compounds
Figure
Molecular docking between compound
Also there is a similarity in node-node energy products between compounds
According to the docking results there are two hydrogen bonds in the –C(O)NHCH2CH2OH moiety. One is between oxygen of CONH and alcoholic hydrogen within the molecule itself, and the other is between alcoholic oxygen at the end of the moiety and LYS61 amino acid.
Overall, the hydrogen bond acceptor group in the side chain of ring A at position 7 forms a hydrogen bond with LYS82 or ASP194, such as oxygen of ether group in compounds
The interpretation of all relevant peaks is summarized in Table
The variables with the highest impact (positive or negative) in the final GRIND model and the structural elements of the most active (
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Node pair | GRIND | Interpretation | |
Variable no. |
Impact (coefficient) | ||
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TIP-TIP | 42, 43 |
Inverse |
Shape of ring A and one of the methoxy substituents in 3,4,5-trimethoxyphenyl at position 1 for |
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TIP-TIP | 52 |
Direct |
Shape of pentafluorobenzoxyl at position 7 and N2-arylated substituent for |
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DRY-DRY | 24 |
Direct |
Hydrophobic properties of ring A and phenyl ring in 2-benzyl substituent for |
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N1-N1 | 8 |
Inverse |
Interaction of carboxyl at position 3 with the probe N1 for |
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TIP-TIP | 44 |
Inverse |
Shape of ring A and one of the methoxy substituents in 3,4,5-trimethoxyphenyl at position 1 for |
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N1-TIP | 10 |
Inverse |
Shape of 3-carboxyl and the interaction of ring C with probe N1 for |
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DRY-DRY | 25 |
Direct |
Hydrophobic properties of ring A and phenyl ring in 2-benzyl substituent for |
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N1-TIP | 24 |
Direct |
Shape of 2-benzyl ring (TIP) and nitrogen of ring C (N1) for |
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N1-TIP | 7 |
Inverse |
Shape of 3-carboxyl and the interaction of ring C with probe N1 for |
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TIP-TIP | 56 |
Direct |
Shape of pentafluorobenzoxyl at position 7 and N2-arylated substituent for |
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N1-TIP | 23 |
Direct |
Shape of N2-benzyl and the interaction of oxygen in pentafluorobenzoxyl group with probe N1 for |
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N1-N1 | 28 |
Direct |
Interaction of nitrogen in ring C and O in pentafluorobenzoxyl group with probe N1 for |
At a deeper insight, in order to determine the most relevant descriptors which can properly distinguish between active and inactive compounds, 6 compounds of training set (
The final results of analysis of 3D-QSAR models and docking studies are summarized in Figure
Schematic representation of the results revealed by the present study. (a) As variables of 3D-QSAR model. Yellow, green, and blue circles represent hydrophobic (DRY), shape (TIP), and hydrogen bond acceptor (N1) regions, respectively. Red and blue lines represent the variables with positive and negative impact, respectively. (b) As structural requirements of
Within this paper we aimed at development and validation of a ligand-based 3D-QSAR model in order to get a deeper insight into molecular structure-antitumor potency relationship in a series of
The hydrophobicity (associated with the DRY probe), shape effects (associated with TIP probe), and hydrogen bond acceptor-donor interactions (associated with N1 probe) are the main factors that determine antitumor potency toward HepG2 cell lines, within studied set. In addition, molecular docking is carried out to map the binding pocket of the PLK1 and its characteristics. And the interactions described with docking studies are similar to those described through analysis of 3D-QSAR model. Results obtained in the present study can be used as guidance for design of novel drugs.
The authors declare that there is no conflict of interests regarding the publication of this paper.