Acute lymphoblastic leukemia (ALL) is a cancer that immature white blood cells continuously overproduce in the bone marrow. These cells crowd out normal cells in the bone marrow bringing damage and death. Methotrexate (MTX) is a drug used in the treatment of various cancer and autoimmune diseases. In particular, for the treatment of childhood acute lymphoblastic leukemia, it had significant effect. MTX competitively inhibits dihydrofolate reductase (DHFR), an enzyme that participates in the tetrahydrofolate synthesis so as to inhibit purine synthesis. In addition, its downstream metabolite methotrexate polyglutamates (MTX-PGs) inhibit the thymidylate synthase (TS). Therefore, MTX can inhibit the synthesis of DNA. However, MTX has cytotoxicity and neurotoxin may cause multiple organ injury and is potentially lethal. Thus, the lower toxicity drugs are necessary to be developed. Recently, diseases treatments with Traditional Chinese Medicine (TCM) as complements are getting more and more attention. In this study, we attempted to discover the compounds with drug-like potential for ALL treatment from the components in TCM. We applied virtual screen and QSAR models based on structure-based and ligand-based studies to identify the potential TCM component compounds. Our results show that the TCM compounds adenosine triphosphate, manninotriose, raffinose, and stachyose could have potential to improve the side effects of MTX for ALL treatment.
Dihydrofolate reductase (DHFR) is essential in cellular metabolism and cell growth. It catalyzes the conversion of dihydrofolate into tetrahydrofolate which is a carrier for the methyl group. The methyl group carried by tetrahydrofolate is required for de novo synthesis of varieties of essential metabolites including amino acids, lipids, pyrimidines, and purines. Methotrexate (MTX), a folate antagonist, arrests cell growth by competitively binding to DHFR, thereby, blocking de novo synthesis of nucleotide precursors and inhibiting DNA synthesis [
MTX tightly binding on DHFR is one of the most widely used drugs in cancer treatment and is especially effective in the treatment of acute lymphocytic leukemia [
In the cells, MTX acts by inhibiting two enzymes. First, as an analog of folate, MTX is a powerful competitive inhibitor with 1000-fold more potent than the natural substrate of DHFR. DHFR is responsible for converting dihydrofolate (FH2) to their active form tetrahydrofolate (FH4), which is a substrate of thymidylate synthase (TS). Second, MTX is converted to active methotrexate polyglutamates (MTX-PGs) by folylpolyglutamate synthase [
The primary action of MTX is inhibition of the enzyme DHFR, which converts dihydrofolate (FH2) to tetrahydrofolate (FH4) [
Inhibition mechanism of MTX in DNA synthesis pathway. MTX: methotrexate; FPGS: folylpolyglutamate synthetase; MTX-PGs: methotrexate polyglutamates; DHFR: dihydrofolate reductase; TS: thymidylate synthase; FH4: tetrahydrofolate; FH2: dihydrofolate; Methylene-THF: 5,10-methylenetetrahydrofolate; Methyl-THF: 5-methyltetrahydrofolate; dUMP: deoxyurindine-5′-monophosphate; dTMP: deoxythymidine-5′-monophosphate; MTRR: methionine synthase reductase; SHMT: serine hydroxymethyltransferase.
However, MTX may lead to acute renal cytotoxicity [
The receptors, human dihydrofolate reductase (DHFR) and human thymidylate synthase (TS) proteins were downloaded from Protein Data Bank of 1U72 (PDB ID: 1U72) [
In this study, 45 candidates (Figure
Experimental pIC50 values for DHFR inhibitors [
Name | R1 | R2 | X | R3 | pIC50 |
---|---|---|---|---|---|
1 | CH3 | CH3 | CH2 | H | 4.71 |
2 | CH3 | CH3 | CH2 | 4′-CH3 | 4.6091 |
3* | CH3 | CH3 | CH2 | 4′-OCH3 | 4.2306 |
4 | CH3 | CH3 | CH2 | 4′-F | 4.6615 |
5* | CH3 | CH3 | CH2 | 4′-Cl | 4.5243 |
6 | CH3 | CH3 | CH2 | 3′,4′-diCl | 4.8928 |
7* | CH3 | CH3 | –O-CH2– | H | 7.1612 |
8 | CH3 | C2H5 | –O-CH2– | H | 6.8097 |
9* | H | c-Pr | –O-CH2– | H | 6.2612 |
10 | –(CH2)3– | –O-CH2– | H | 6.8729 | |
11 | – (CH2)4– | –O-CH2– | H | 6.762 | |
12 | – (CH2)5– | –O-CH2– | H | 5.7471 | |
13 | – (CH2)6– | –O-CH2– | H | 5.2733 | |
14 | – (CH2)4– | –O-CH2CH2– | H | 7.5086 | |
15 | – (CH2)5– | –O-CH2CH2– | H | 8.0458 | |
16 | – (CH2)4– | –O-(CH2)3-O– | H | 7.699 | |
17 | – (CH2)5– | –O-(CH2)3-O– | H | 7.4949 | |
18 | CH3 | CH3 | –O-(CH2)3-O– | H | 8.2218 |
19 | CH3 | CH3 | –O-(CH2)4-O– | H | 7.5686 |
20 | CH3 | C2H5 | –O-(CH2)3-O– | H | 8.0969 |
21 | H | c-Pr | –O-(CH2)3-O– | H | 8.1549 |
22 | – (CH2)4– | –O-(CH2)3-O– | H | 8.699 | |
23* | – (CH2)4– | –O-(CH2)4-O– | H | 7.3768 | |
24 | – (CH2)5– | –O-(CH2)3-O– | H | 8.1549 | |
25 | – (CH2)5– | –O-(CH2)4-O– | H | 6.8069 | |
26 | –(CH2)6– | –O-(CH2)3-O– | H | 7.9586 | |
27 | –(CH2)5– | –O-(CH2)3-O– | F | 7.8239 | |
28 | –(CH2)5– | –O-(CH2)3-O– | Cl | 7.8539 | |
29 | –(CH2)5– | –O-(CH2)3-O– | NO2 | 7.8239 | |
30 | –(CH2)5– | –O-(CH2)3-O– | Me | 7.7447 | |
31 | –(CH2)5– | –O-(CH2)3-O– | t-Bu | 7.6576 | |
32 | –(CH2)5– | –O-(CH2)3-O– | OMe | 8.2218 | |
33* | –(CH2)5– | –O-(CH2)3-O– | CN | 8 | |
34 | –(CH2)5– | –O-(CH2)3-O– | COCH3 | 7.8861 | |
35 | –(CH2)5– | –O-(CH2)3-O– | SO2NH2 | 8.2218 | |
36* | –(CH2)4– | –O-(CH2)3-O– | F | 8 | |
37 | –(CH2)4– | –O-(CH2)3-O– | Cl | 8.1549 | |
38 | –(CH2)4– | –O-(CH2)3-O– | NO2 | 8.0969 | |
39* | –(CH2)4– | –O-(CH2)3-O– | Me | 8 | |
40 | –(CH2)4– | –O-(CH2)3-O– | t-Bu | 7.7696 | |
41 | –(CH2)4– | –O-(CH2)3-O– | OMe | 7.9586 | |
42 | –(CH2)4– | –O-(CH2)3-O– | CN | 8.0969 | |
43 | –(CH2)4– | –O-(CH2)3-O– | COCH3 | 8.0458 | |
44* | –(CH2)4– | –O-(CH2)3-N(Me)– | H | 7.3872 | |
45 | –(CH2)4– | –O-(CH2)3– | H | 7.4949 | |
MTX |
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8.5229 |
Chemical structure of DHFR inhibitors [
Multiple linear regression [
SVM implement classification or regression analysis with linear or nonlinear algorithms [
Cross-validation of the SVM model was also conducted following the default settings in LibSVM [
We used the Bayes Net Toolbox (BNT) in Matlab (
The Banjo (Bayesian network inference with Java objects) is software for structure learning of static Bayesian networks (BN) [
The square correlation coefficients (
Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed by Sybyl-X 1.1.1 (Tripos Inc., St. Louis, MO, USA) for DHFR inhibitors. Lennard-Jones potential and Coulomb potential were employed to calculate steric and electrostatic interaction energies. The two 3D-QSAR models were further evaluated by cross-validated correlation coefficient
The flowchart for the entire experimental procedure for TCM candidates screening is illustrated in Figure
The experimental flowchart.
The virtual screening was performed by the LigandFit Module of DS 2.5 in force field of CHARMm. The receptor binding sites were defined by the binding position of MTX on DHFR protein and by the binding position of MTX-PGs on TS protein. The compounds from our library were docked into the two receptors. In this protocol, the receptors were fixed, and the ligands that complement the binding sites were flexible in energy minimization process. The control compound used in this study was MTX which contains aromatic and heterocyclic rings (Figure
(a)-(b) The chemical scaffolds of MTX, MTX-PGs, and TCM candidates for acute lymphoblastic leukemia treatment.
The top eighteen results from DHFR docking score are tabulated in Table
DHFR and TS docking score of TCM candidates.
Index | TCM candidate | DHFR docking score | TS docking score |
---|---|---|---|
1 | Adenosine triphosphate | 226.6790 | 186.2170 |
2 | Methyl 6-O-digalloyl-beta-D-glucopyranoside (II) | 162.6260 | 154.1730 |
3 | Methyl 4,6-di-O-galloyl-beta-D-glucopyranoside | 153.7500 | 148.2880 |
4 | Methyl 6-O-digalloyl-beta-D-glucopyranoside | 151.7650 | 158.0350 |
5 | Manninotriose | 129.7870 | 114.6030 |
6 | Forsythiaside | 129.6030 | 27.9940 |
7 | Isoacteoside | 124.5900 | 30.6190 |
8 | Rehmannioside B | 119.9930 | 79.2920 |
9 | Rehmannioside A | 116.4330 | 71.3970 |
10 | Raffinose | 115.4940 | 134.2120 |
11 | Cistanoside C | 112.4270 | — |
12 | Methyl 3,3,6-tri-O-galloyl-beta-D-glucopyranoside | 109.9470 | 20.7830 |
13 | Stachyose | 107.0940 | 8.5760 |
14 | Chlorogenic acid | 103.8080 | — |
15 | Jionoside D | 103.5050 | 39.3430 |
16 | Isochlorogenic acid | 102.9470 | — |
17 | Jionoside C | 102.3940 | — |
18 | Rutin | 101.1310 | 78.816 |
* | MTX | 97.0960 | — |
** | MTX-PGs | — | 69.671 |
Predicted pharmacokinetic properties of TCM candidates and MTX.
Index | TCM candidate | Pharmacokinetic properties | |||
---|---|---|---|---|---|
Absorption | Solubility | Hepatotoxicity | PPB | ||
1 | Adenosine triphosphate | 3 | 2 | 1 | 0 |
2 | Chlorogenic acid | 3 | 4 | 1 | 0 |
3 | Cistanoside C | 3 | 2 | 1 | 2 |
4 | Forsythiaside | 3 | 2 | 1 | 0 |
5 | Isoacteoside | 3 | 2 | 1 | 0 |
6 | Isochlorogenic acid | 3 | 4 | 1 | 0 |
7 | Jionoside C | 3 | 3 | 1 | 2 |
8 | Jionoside D | 3 | 2 | 1 | 2 |
9 | Manninotriose | 3 | 3 | 0 | 0 |
10 | Methyl 4,6-di-O-galloyl-beta-D-glucopyranoside | 3 | 2 | 1 | 0 |
11 | Methyl 6-O-digalloyl-beta-D-glucopyranoside | 3 | 2 | 1 | 0 |
12 | Methyl 6-O-digalloyl-beta-D-glucopyranoside (II) | 3 | 2 | 1 | 0 |
13 | Methyl 3,3,6-tri-O-galloyl-beta-D-glucopyranoside | 3 | 0 | 1 | 0 |
14 | Raffinose | 3 | 3 | 0 | 0 |
15 | Rehmannioside A | 3 | 4 | 1 | 0 |
16 | Rehmannioside B | 3 | 4 | 1 | 0 |
17 | Rutin | 3 | 1 | 1 | 2 |
18 | Stachyose | 3 | 1 | 0 | 0 |
Control | MTX | 3 | 3 | 1 | 1 |
2Solubility, there are gour prediction levels: 0 (extremely low), 1 (very low, but possible), 2 (low), 3 (good), 4 (optimal), 5 (too soluble), 6 (warning).
3Hepatotoxicity, there are four prediction levels: 0 (nontoxic), 1 (toxic).
4PPB (Plasma protein binding), there are there prediction levels: 0 (binding is <90%), 1 (binding is >90%), 2 (binding >95%).
Ligand-receptor interactions during docking are shown in Figures
Docking pose of MTX and TCM candidates with DHFR for (a), (b), (c), (d), and (e). Docking pose of MTX-PGs with TS for (f), (g), (h), (i), and (j). TCM candidates are shown in cyan. The cofactors are shown in purple. In H-bond interactions, nitrogen atoms are shown in blue, hydrogen atoms are shown in gray, oxygen atoms are shown in magenta, hydrogen bonds are shown in red dotted line, pi bonds are shown in orange solid line. (a) MTX, (b) and (g) adenosine triphosphate, (c) and (h) manninotriose, (d) and (i) raffinose, (e) and (j) stachyose, and (f) MTX-PGs.
The Ligplot analysis of hydrophobic interactions between DHFR and TCM candidates and between TS and TCM candidates. (a) MTX with DHFR, (b) and (g) adenosine triphosphate with DHFR and TS, (c) and (h) manninotriose with DHFR and TS, (d) and (i) raffinose with DHFR and TS, (e) and (j) stachyose with DHFR and TS, and (f) MTX-PGs with DHFR and TS. Bonds: ligand bonds, nonligand bonds, hydrogen bonds, and hydrophobic are shown in purple, orange, olive green, and brick red, respectively. Atoms: nitrogen, oxygen, carbon, and sulfur are shown in blue, red, black, and yellow, respectively.
Analysis of hydrophobic interactions showed that MTX docking on DHFR was more stable than the TCM candidates. Comparing with chemical structures of the TCM candidates, it could be attributed to the larger size for MTX docking on DHFR (Figures
QSAR models were constructed using known DHFR inhibitors [
Our MLR model was as follows.
GFATempModel_1 = 31.623 + 2.5173
In CoMFA model, the steric fields were the primary contributing factor. In CoMSIA, various factors were considered and modeled. The optimum CoMSIA models were “EHA model” and “EHDA model” based on high
Partial Least Square (PLS) analysis for CoMFA and CoMSIA models.
Cross Validation | Non-cross Validtion | Fraction | ||||||||
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ONC |
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SEE |
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S | E | H | D |
| |
CoMFA | ||||||||||
7 | 0.5250 | 0.9630 | 0.2590 | 136.2760 | 0.7970 | 0.2030 | — | — | — | |
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CoMSIA | ||||||||||
S | 36 | 0.6350 | 0.9890 | 0.3040 | 19.6900 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
E | — | — | — | — | — | — | — | — | — | — |
H | 2 | 0.6130 | 0.7760 | 0.5940 | 72.7070 | 0.0000 | 0.0000 | 1.0000 | 0.0000 | 0.0000 |
D | 7 | 0.4180 | 0.7160 | 0.7130 | 13.3480 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | 0.0000 |
A | 1 | 0.0810 | 0.1600 | 1.1380 | 8.1640 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 |
SE | 37 | 0.6050 | 0.9890 | 0.3250 | 16.7260 | 0.9980 | 0.0200 | 0.0000 | 0.0000 | 0.0000 |
SH | 2 | 0.5970 | 0.7790 | 0.5910 | 73.9120 | 0.3880 | 0.0000 | 0.6120 | 0.0000 | 0.0000 |
SD | 36 | 0.6670 | 0.9890 | 0.3020 | 19.9580 | 0.6350 | 0.0000 | 0.0000 | 0.3650 | 0.0000 |
SA | 30 | 0.7020 | 0.9890 | 0.2340 | 39.7820 | 0.7480 | 0.0000 | 0.0000 | 0.0000 | 0.2520 |
EH | 7 | 0.6270 | 0.9540 | 0.2860 | 110.4680 | 0.0000 | 0.0500 | 0.9500 | 0.0000 | 0.0000 |
ED | 7 | 0.4130 | 0.7090 | 0.7210 | 12.9020 | 0.0000 | 0.0180 | 0.0000 | 0.9820 | 0.0000 |
EA | 2 | 0.0760 | 0.1830 | 1.1350 | 4.6860 | 0.0000 | 0.2000 | 0.0000 | 0.0000 | 0.8000 |
HD | 2 | 0.5780 | 0.7940 | 0.5690 | 81.1680 | 0.0000 | 0.0000 | 0.7220 | 0.2780 | 0.0000 |
HA | 2 | 0.5890 | 0.7910 | 0.5740 | 79.5410 | 0.0000 | 0.0000 | 0.7450 | 0.0000 | 0.2550 |
DA | 9 | 0.4300 | 0.7290 | 0.7160 | 10.4430 | 0.0000 | 0.0000 | 0.0000 | 0.7800 | 0.2200 |
SHE | 8 | 0.5850 | 0.9690 | 0.2400 | 139.5820 | 0.3570 | 0.0440 | 0.6000 | 0.0000 | 0.0000 |
SED | 38 | 0.6500 | 0.9890 | 0.3490 | 14.1810 | 0.6340 | 0.0010 | 0.0000 | 0.3650 | 0.0000 |
SEA | 31 | 0.7030 | 0.9880 | 0.2430 | 35.7980 | 0.7420 | 0.0110 | 0.0000 | 0.0000 | 0.2470 |
SHD | 22 | 0.5780 | 0.9890 | 0.1830 | 89.3410 | 0.3070 | 0.0000 | 0.4490 | 0.2430 | 0.0000 |
SHA | 2 | 0.5800 | 0.7950 | 0.5680 | 81.4850 | 0.3130 | 0.0000 | 0.4980 | 0.0000 | 0.1900 |
SDA | 30 | 0.7170 | 0.9890 | 0.2320 | 40.3890 | 0.5640 | 0.0000 | 0.0000 | 0.2910 | 0.1450 |
EDA | 11 | 0.4240 | 0.7380 | 0.7250 | 8.4650 | 0.0000 | 0.0200 | 0.0000 | 0.7640 | 0.2150 |
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HAD | 2 | 0.5550 | 0.8020 | 0.5580 | 85.2150 | 0.0000 | 0.0000 | 0.6150 | 0.2080 | 0.1770 |
SEHD | 23 | 0.5970 | 0.9890 | 0.1870 | 81.1730 | 0.2940 | 0.0230 | 0.4520 | 0.2310 | 0.0000 |
SEHA | 23 | 0.5970 | 0.9800 | 0.1880 | 80.4080 | 0.3000 | 0.0420 | 0.4620 | 0.0000 | 0.1960 |
SEDA | 31 | 0.7110 | 0.9890 | 0.2420 | 36.1870 | 0.5640 | 0.0050 | 0.0000 | 0.2840 | 0.1470 |
SHDA | 5 | 0.5630 | 0.9290 | 0.3470 | 102.3970 | 0.2600 | 0.0000 | 0.3980 | 0.1920 | 0.1510 |
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SEHDA | 23 | 0.6120 | 0.9890 | 0.1880 | 80.3300 | 0.2690 | 0.0340 | 0.4020 | 0.1630 | 0.1330 |
OCN: Optimal number of components.
SEE: Standard error of estimate.
S: Steric.
H: Hydrophobic.
D: Hydrogen bond donor.
A: Hydrogen bone acceptor.
E: Electrostatic.
Experimental and predicted pIC50 values of 45 DHFR inhibitors using CoMFA and CoMSIA models are shown in Table
Experimental and predicted pIC50 values of 45 DHFR inhibitors using the constructed CoMFA and CoMSIA models.
DHFR inhibitors no. | Experimental pIC50 | CoMFA | CoMSIA_EHDA | CoMSIA_EHA | |||
---|---|---|---|---|---|---|---|
Predicted | Residual | Predicted | Residual | Predicted | Residual | ||
1 | 4.710 | 4.652 | 0.0580 | 4.481 | 0.229 | 4.532 | 0.178 |
2 | 4.609 | 4.606 | 0.0031 | 4.635 | −0.026 | 4.662 | −0.053 |
3* | 4.231 | 4.576 | −0.3454 | 4.333 | −0.102 | 4.407 | −0.176 |
4 | 4.662 | 5.027 | −0.3655 | 4.698 | −0.037 | 4.701 | −0.039 |
5* | 4.524 | 4.571 | −0.0467 | 4.807 | −0.283 | 4.797 | −0.273 |
6 | 4.893 | 4.476 | 0.4168 | 4.723 | 0.170 | 4.651 | 0.242 |
7* | 7.161 | 6.810 | 0.3512 | 7.287 | −0.126 | 7.359 | −0.198 |
8 | 6.810 | 6.529 | 0.2807 | 6.722 | 0.088 | 6.723 | 0.087 |
9* | 6.261 | 6.495 | −0.2338 | 6.270 | −0.009 | 6.240 | 0.021 |
10 | 6.873 | 6.648 | 0.2249 | 6.808 | 0.065 | 6.832 | 0.041 |
11 | 6.762 | 6.793 | −0.0310 | 6.686 | 0.076 | 6.645 | 0.117 |
12 | 5.747 | 5.749 | −0.0019 | 5.767 | −0.020 | 5.705 | 0.042 |
13 | 5.273 | 5.346 | −0.0727 | 5.245 | 0.028 | 5.279 | −0.006 |
14 | 7.509 | 7.454 | 0.0546 | 7.494 | 0.015 | 7.522 | −0.013 |
15 | 8.046 | 8.322 | −0.2762 | 8.056 | −0.010 | 8.052 | −0.006 |
16 | 7.699 | 8.127 | −0.4280 | 8.130 | −0.431 | 8.110 | −0.411 |
17 | 7.495 | 7.670 | −0.1751 | 7.871 | −0.376 | 7.820 | −0.325 |
18 | 8.222 | 8.079 | 0.1428 | 8.130 | 0.092 | 8.105 | 0.117 |
19 | 7.569 | 7.561 | 0.0076 | 7.581 | −0.012 | 7.609 | −0.040 |
20 | 8.097 | 8.207 | −0.1101 | 8.105 | −0.008 | 8.240 | −0.143 |
21 | 8.155 | 8.007 | 0.1479 | 8.242 | −0.087 | 8.215 | −0.060 |
22 | 8.699 | 8.127 | 0.5720 | 8.130 | 0.569 | 8.110 | 0.589 |
23* | 7.377 | 7.636 | −0.2592 | 7.325 | 0.052 | 7.318 | 0.059 |
24 | 8.155 | 7.670 | 0.4849 | 7.871 | 0.284 | 7.820 | 0.335 |
25 | 6.807 | 7.113 | −0.3061 | 6.902 | −0.095 | 6.824 | −0.017 |
26 | 7.959 | 7.987 | −0.0284 | 7.887 | 0.072 | 7.975 | −0.016 |
27 | 7.824 | 7.763 | 0.0609 | 7.981 | −0.157 | 7.955 | −0.131 |
28 | 7.854 | 7.839 | 0.0149 | 7.906 | −0.052 | 7.850 | 0.004 |
29 | 7.824 | 7.843 | −0.0191 | 7.824 | 0.000 | 7.827 | −0.003 |
30 | 7.745 | 7.914 | −0.1693 | 7.736 | 0.009 | 7.733 | 0.012 |
31 | 7.658 | 8.069 | −0.4114 | 7.665 | −0.007 | 7.654 | 0.004 |
32 | 8.222 | 8.005 | 0.2168 | 7.848 | 0.374 | 7.814 | 0.408 |
33* | 8.000 | 8.100 | −0.1000 | 7.978 | 0.022 | 8.010 | −0.010 |
34 | 7.886 | 7.455 | 0.4311 | 7.947 | −0.061 | 7.811 | 0.075 |
35 | 8.222 | 7.981 | 0.2408 | 8.208 | 0.014 | 8.237 | −0.015 |
36* | 8.000 | 8.173 | −0.1730 | 8.130 | −0.130 | 8.139 | −0.139 |
37 | 8.155 | 8.180 | −0.0251 | 8.170 | −0.015 | 8.187 | −0.032 |
38 | 8.097 | 8.122 | −0.0251 | 8.097 | 0.000 | 8.097 | 0.000 |
39* | 8.000 | 7.990 | 0.0100 | 8.007 | −0.007 | 8.054 | −0.054 |
40 | 7.770 | 7.683 | 0.0866 | 7.832 | −0.062 | 7.697 | 0.073 |
41 | 7.959 | 8.223 | −0.2644 | 7.883 | 0.076 | 7.907 | 0.052 |
42 | 8.097 | 7.974 | 0.1229 | 8.040 | 0.057 | 8.150 | −0.053 |
43 | 8.046 | 7.996 | 0.0498 | 8.052 | −0.006 | 8.061 | −0.015 |
44* | 7.387 | 7.542 | −0.1548 | 7.567 | −0.180 | 7.590 | −0.203 |
45 | 7.495 | 7.449 | 0.0459 | 7.484 | 0.011 | 7.516 | −0.021 |
The correlations between the predicted and actual bioactivity for DHFR inhibitors are shown in Figure
Predicted bioactivity (pIC50) of MTX and TCM candidates using MLR, Bayesian, SVM, CoMFA and CoMSIA models.
Name | MLR | Bayesian | SVM | CoMFA | CoMSIA_EHDA* | CoMSIA_EHA** |
---|---|---|---|---|---|---|
Adenosine triphosphate | 6.4559 | 5.8145 | 8.7175 | 7.9640 | 7.8600 | 7.8350 |
Methyl 6-O-digalloyl-beta-D-glucopyranoside (II) | 27.5044 | 5.1810 | 8.0157 | 6.9800 | 6.6030 | 5.5170 |
Methyl 4,6-di-O-galloyl-beta-D-glucopyranoside | 27.7317 | 5.4868 | 8.4131 | 7.5490 | 6.5980 | 5.9300 |
Methyl 6-O-digalloyl-beta-D-glucopyranoside | 26.7188 | 5.2477 | 7.8936 | 6.8980 | 6.6620 | 5.6840 |
Manninotriose | 29.1034 | 5.1934 | 5.9247 | 7.6470 | 6.2450 | 5.3700 |
Forsythiaside | 29.9821 | 5.3595 | 8.5713 | 7.7140 | 8.0830 | 7.8950 |
Isoacteoside | 27.6319 | 6.3265 | 8.1255 | 7.6550 | 7.7990 | 7.5430 |
Rehmannioside B | 26.7291 | 4.3032 | 7.3293 | 6.9990 | 6.8000 | 5.8300 |
Rehmannioside A | 30.3632 | 4.4182 | 9.3324 | 6.7480 | 5.8070 | 4.6750 |
Raffinose | 32.8592 | 5.1647 | 8.4766 | 6.9350 | 5.9620 | 4.2830 |
Cistanoside C | 26.1802 | 5.7174 | 8.2029 | 7.6060 | 8.0200 | 7.9640 |
Methyl 3,3,6-tri-O-galloyl-beta-D-glucopyranoside | 30.7405 | 6.0369 | 8.3193 | 6.7670 | 6.3240 | 6.6300 |
Stachyose | 40.5491 | 5.9779 | 8.5055 | 7.4300 | 5.6830 | 4.4510 |
Chlorogenic acid | 17.3951 | 4.2335 | 7.8897 | 7.8080 | 7.9640 | 7.7680 |
Jionoside D | 26.0421 | 5.5238 | 8.2089 | 7.5080 | 7.4900 | 7.2820 |
Isochlorogenic acid | 16.1484 | 4.4196 | 7.4839 | 7.1990 | 6.3590 | 6.4480 |
Jionoside C | 23.7203 | 5.6640 | 8.2741 | 7.7600 | 7.0800 | 6.9110 |
Rutin | 30.3096 | 5.6910 | 8.2465 | 6.5720 | 8.0190 | 7.6830 |
The pIC50 experimental values of MTX was 8.5229.
Correlation of observed and predicted activity (pIC50) using 2D-QSAR models and 3D-QSAR models. MLR, Bayesian network, and SVM were 2D-QSAR model. CoMFA, CoMSIA_EHDA, and CoMSIA_EHA were 3D-QSAR model.
Ligand activities of MTX and the TCM candidates can be predicted based on the 3D-QSAR contour map, including features in steric field, hydrophobic field, and H-bond donor/acceptor characteristics. MTX and the TCM candidates contoured well to the steric features of the CoMFA in Figure
The CoMFA contour maps for DHFR. (a) MTX, (b) adenosine triphosphate, (c) manninotriose, (d) raffinose, and (e) stachyose. Green and yellow contours denote regions favoring and disfavoring steric fields, respectively. Blue and red contours denote regions favoring and disfavoring electrostatic fields, respectively.
The CoMSIA contour maps of EHA model for DHFR. (a) MTX, (b) adenosine triphosphate, (c) manninotriose, (d) raffinose, and (e) stachyose. Blue and orange contours denote regions favoring and disfavoring electrostatic fields, respectively. Yellow and white contours denote regions favoring and disfavoring hydrophobic fields, respectively. Green and red contours denote regions favoring and disfavoring H-bond acceptor fields, respectively.
The CoMSIA contour maps of EHDA model for DHFR. (a) MTX, (b) adenosine triphosphate, (c) manninotriose, (d) raffinose, and (e) stachyose. Blue and orange contours denote regions favoring and disfavoring electrostatic fields, respectively. Yellow and white contours denote regions favoring and disfavoring hydrophobic fields, respectively. Green and red contours denote regions favoring and disfavoring H-bond acceptor fields, respectively. Cyan and purple contours denote regions favoring and disfavoring H-bond donor fields, respectively.
Contour to steric favoring and hydrophobic favoring regions was observed for adenosine triphosphate, manninotriose, raffinose, and stachyose. Consistent with the docking pose contour (Figures
DHFR and TS proteins are key regulators in de novo synthesis of purines and thymidylate. Inhibiton of these proteins has the potential for treating acute lymphoblastic leukemia. In this study, we applied virtual screen and QSAR models based on structure-based and ligand-based methods in order to identify the potential TCM compounds. The TCM compounds adenosine triphosphate, manninotriose, raffinose, and stachyose could bind on DHFR and TS specifically and had low hepatotoxicity. These TCM compounds had potential to improve the side effects of MTX for ALL treatment.
The authors declare that they have no conflict of interests regarding the publication of this paper.
The research was supported by Grants from the National Science Council of Taiwan (NSC102-2632-E-468-001-MY3, NSC102-2325-B039-001, and NSC102-2221-E-468-027-), Asia University (101-ASIA-24, 101-ASIA-59, ASIA100-CMU-2, and ASIA101-CMU-2), and China Medical University Hospital (DMR-103-001, DMR-103-058, and DMR-103-096). This study is also supported in part by Taiwan Department of Health Clinical Trial and Research Center of Excellence (DOH102-TD-B-111-004) and Taiwan Department of Health Cancer Research Center of Excellence (MOHW103-TD-B-111-03), and CMU under the Aim for Top University Plan of the Ministry of Education, Taiwan.