Skip to main content
NCBI home page
In Silico Pharmacology logoLink to In Silico Pharmacology
. 2017 Nov 18;5(1):15. doi: 10.1007/s40203-017-0034-0

Molecular docking analysis of curcumin analogues against kinase domain of ALK5

Shivananda Kandagalla 1, B S Sharath 1, Basavapattana Rudresh Bharath 2, Umme hani 1, Hanumanthappa Manjunatha 1,✉
PMCID: PMC5755696  PMID: 29308351

Abstract

During metastasis, cancer cells transcend from primary site to normal cells area upon attaining epithelial to mesenchymal transition (EMT) causing malignant cancer disease. Increased expression of TGF-β and its receptor ALK5 is an important hallmark of malignant cancer. In the present study, efficacy of curcumin and its analogues as inhibitors of ALK5 (TGFβR-I) receptor was evaluated using in silico approaches. A total of 142 curcumin analogues and curcumin were retrieved from peer reviewed literature and constructed a combinatorial library. Further their drug-likeness was assessed using Molinspiration, cheminformatics and preADMET online servers. The interaction of 142 curcumin analogues and curcumin with ALK5 receptor was studied using Autodock Vina. This study revealed six curcumin analogues as promising ALK5 inhibitors with significant binding energy and H-bonding interaction.

Electronic supplementary material

The online version of this article (10.1007/s40203-017-0034-0) contains supplementary material, which is available to authorized users.

Keywords: EMT, Metastasis, TGF-β, ALK5, Autodock Vina, Curcumin, Curcumin analogues

Background

Transforming growth factor-β (TGF-β) emerged as a potent cytokine that play a critical role in several biological process, such as immune response, inflammation, wound healing, extracellular matrix (ECM) formation and remodelling, embryonic development through EMT (Massagué Joan 2012; Dijke Peter et al. 1997) and regulate key cellular functions such as differentiation, migration and death during normal development. Excessive production/activation of TGF-β implicated in wide spectrum of pathological conditions such as cancer, fibrotic disorders, myelodysplastic syndrome, autoimmune diseases and Marfan syndrome (Massagué 2008, 2000; Akhurst and Derynck 2001).

TGF-β signals through two serine/threonine kinase receptors, the type I (TGFβR-I) and type II (TGFβR-II) receptors. The type I receptor is inactive in the absence of ligand while type II receptor is constitutively active. (Massagué 1998; Wrana 1994). The binding of TGF-β to its receptors induces the oligomeric receptor-ligand complex formation which enables the type II receptor to phosphorylate the type I receptor, known as Acting Receptor-like Kinase 5 (ALK5) in the GS domain (Wrana 1994a; Shi 2003). Activated TbRI with the help of SARA (Smad Anchor for Receptor Activation) recruits and phosphorylates Smad2/Smad3 (R-Smads) (Tsukazaki 1998). Phosphorylation of R-Smads leads to the dissociation from SARA to form a homo-oligomeric complexes with Smad4, which eventually translocating the complex into the nucleus involved in regulating the target gene transcription process. Deregulation of TGF-β signalling implicated in many human disease including cancer cells to flee from primary site through the induction of EMT that ultimately leads to cancer metastasis. During EMT, epithelial cells acquire mesenchymal phenotype, leading to enhanced motility and invasion property (Xu 2009).

Several small molecules of LY2109761 (Melisi et al. 2008), Galunisertib (LY2157299) (Phase 2/3) (Liu et al. 2009), LY364947 (Li et al. 2006), SB505124 (DaCosta et al. 2004) that bind ALK5 were reported. Among ALK5 inhibitors only Galunisertib was proven to be safe and passed phase I and phase II clinical trials against hepatocellular carcinoma (HCC) and pancreatic cancer (Jin et al. 2014) and EW-7197 is under phase I clinical trial against advanced stage solid tumors research (Son et al. 2014). ALK5 inhibitors specifically occupying the ATP binding site of ALK5 kinase domain, which phosphorylates SMAD2 and SMAD3 and ultimately inhibit SMAD signaling pathway. Hence, new ALK5 inhibitors are propinquity need for the treatment of cancer and related diseases.

Curcumin, the main active component of turmeric, is a polyphenol derived from the rhizomes of Curcuma Longa Linn. It has been used for centuries in folk medicinal remedies, daily dietary spice and as a coloring agent in Asian countries (Ammon and Martin 1991; Gupta et al. 2012). Numerous evidences point that the pleiotropic nature of curcumin is having anti-inflammatory (Gupta et al. 2012), antioxidant (Sharma 1976), anti-microbial (Negi et al. 1999) as well as anticancer activities (Kuttan et al. 1985). While some of the experimental evidence authenticate non-toxic effect in high dose (Senft et al. 2010). Nevertheless, poor stability and bioavailability of curcumin prevents its potency as selective cancer drug and to overcome this property, researchers have been focusing on the synthesis of new curcumin analogues. Several curcumin-analogues proved to be effective in preclinical studies (Allegra et al. 2017) and several reports have been unveiled reduced expression of TGF-β receptor type I (TβR-I) and TGF-β receptor type II (TβR II) expression in several epithelial cells and inhibited TGF-β induced EMT during fibrosis and cancer (Li et al. 2013; Gaedeke et al. 2004).

In the present study, we investigate the potential of curcumin and its analogues (curcuminoids) against TGF-β receptor type I (ALK5) by molecular docking studies.

Methods

Preparation of ligands

The IUPAC name of curcumin and its analogues were procured from peer reviewed literature (Ahmed et al. 2013) and using OPSIN (Open Parser for Systematic IUPAC nomenclature) (http://opsin.ch.cam.ac.uk/) ‘SMILES’ of curcumin and its analogues were fetched (Lowe et al. 2011). They are used as an input to identify the 2D structure of curcumin and its analogues in ChemSpider database (http://chemspider.com/) and the PDB file of ligand were generated using Open Babel software (Pence and Williams 2010; O’Boyle et al. 2011). The known inhibitors of ALK5 such as Ly364947, SB431543 and SD-408 were reaped through ChemSpider database (Li et al. 2006; Callahan et al. 2002). Energy minimization was carried out using PRODRG server and PDB file of curcumin and its analogues with known inhibitors were converted into PDBQT file format using Auto Dock Tool (ADT) for further analysis (Morris et al. 1998).

Preparation of receptor

The atomic coordinates of ALK5 kinase domain was retrieved from the RCSB PDB (https://www.rcsb.org/pdb/home/home.do). The co-crystallized structure of ALK5 (PDB ID: 1RW8, resolution: 2.4 Å) was retrieved and selected for docking study (Sawyer et al. 2004). Prior to docking analysis, the structure was emended by removing co-crystallized heteroatoms and water molecules using SPDBV software, followed by addition of polarhydrogen and Gasteiger charges using Auto Dock Tool (ADT). Then structures were saved in PDBQT files, for further analysis.

Drug-likeness prediction

Drug likeness provides whether the molecule of interest is similar to known drug based on the molecular and structural features of drug molecule. The important properties of drug likeness are hydrophobicity, hydrogen bonding, electron distribution, molecular size and other pharmacophore features influence the behaviour of a molecule in terms of bioavailability, transportation, toxicity, reactivity and other properties on living organism. In the present work the molecular properties and bioactivity of curcumin analogues was evaluated using Molinspiration cheminformatics server (http://www.molinspiration.com/). The server supports wide range of tools for the processing and manipulation of molecules including generation of tautomer, molecule fragmentation, normalization of molecules, calculation of various molecular properties needed in QSAR study as well supports fragment based virtual screening. The server calculate the molecular properties based on Lipinski Rule of five (Lipinski 2004) and predicts bioactivity score for the most important therapeutic targets like GPCR receptors kinase inhibitors, ion channel modulators, enzymes and nuclear receptors (Ertl et al. 2000).

Molecular docking studies

Binding mode and interaction of ALK5 with Curcumin and its analogues was performed using Autodock Vina platform (Trott and Olson 2010). This Program require pre-calculated grid box, Serves as frontier of active pocket amino acids in the receptor by attaining XYZ co-ordinates. The active pocket amino acid residues were identified using PoSSuM server (http://possum.cbrc.jp/PoSSuM/) by comparing the ALK5 (PDB ID: 1RW8) with other ALK5 crystallographic structure present within PDB. PoSSuM predicts the specific ligand for unbound structures and also enables rapid exploration of similar binding sites among structures with different global folds as well as similar folds (Ito et al. 2015). The grid optimization was carried out using Autogrid program and the grid box was centered at region covering all the identified active pocket amino acid residues (ILE 211, LYS213, VAL219, ALA230, VAL231, LYS232, GLU245, TYR249, LEU260, PHE262, LEU278, VAL279, SER280, ASP281, TYR282, HIS283, LYS337, ASN338, LEU340, ALA350, ASP351). The grid box size was set at 16, 16 and 16 and centered on mass center 3.753, 15.918 and 9.96 for x, y and z co-ordinates respectively with space separated by 1.0 Å (grid-point spacing). The exhaustiveness was set to the value of 10 for all docking analysis. Docking was accomplished under AutoDock program, and ten different mode of confirmations were generated with their respective binding energy/affinity. Lowest binding energy results were considered for further post docking analysis. Docked complex of ALK5 protein and curcumin and its analogues with good binding affinity were visualized in Pymol viewer, then LigPlot 1.4.5 was used to generate two dimensional docking for the analysis of hydrogen bond and hydrophobic interaction (Wallace et al. 1995).

ADMET prediction

PreADMET is a web-based application used to predict ADMET (absorption, distribution, metabolism, excretion and toxicity) (http://preadmet.bmdrc.kr/) of curcumin and its analogues possessing good binding affinity with receptor protein based on lowest binding energy. The ADMET is the most important parameter in drug discovery studies and is considered before designing drug as attributes of the molecules play decisive role in pre-clinical and clinical phase. The Caco2 cell (human colon adenocarcinoma) and MDCK (Madin-Darby Canine Kidney) cell models are important in the prediction of oral absorption and Human intestinal absorption (HIA) of drugs. Plasma protein binding (PPB) and blood brain barrier (BBB) are critical factor in distribution phase of the drugs. PreADMET predicts percentage of drug bound in plasma protein to emulate in vitro data on human and envisage in vivo data based on the value of BBB penetration. The drug action depends on its interaction with plasma proteins, generally unbound drug molecule shows better interaction with target molecules, which influence directly/indirectly on dissemination and efficacy of the drug molecule.

PreADMET predicts toxicity value based on Ames test and the value of the prediction results would be decided either as “positive” or “negative”. Finally carcinogenicity was speculated using preADMET, based on NTP (National Toxicology Program) and which comprise results of the in vivo carcinogenicity tests in mice and rats during 2 years (Yamashita et al. 2000).

Metabolic site prediction

MetaPrint 2D server was used to predict the sites of metabolism of the best docked compounds, that are most likely undergo phase I metabolism (Carlsson et al. 2010). This software was used on the web platform (http://wwwmetaprint2d.ch.cam.ac.uk/metaprint2d/), by uploading the SMILES string of compounds.

Results and discussions

A total of 143 Curcumin analogues (including curcumin) were collected from the literature with their complete information including PubChem Id as furnished in the supplementary Table 1.

Drug-likeness properties

The drug-likeness properties of 143 Curcumin analogues obtained from Molinspiration server are framed in Supplementary Table 2. It deduce that only 139 molecules possess molecular weight in the acceptable range (MWT ≤ 500), and four molecules violated the Lipinski’s rule of five thereby excluded for further analysis, as an important phenomenon which determines directly the discovery of drug effecting the target function. The number of hydrogen bond donors (OH and NH atoms) and acceptors (O and N atoms) in Curcumin analogues were found to be 0 to 8 and 0 to 5 respectively, which establishes Lipinski’s limit range less than 10 and 5 respectively (Lipinski 2004). Lipophilicity and Hydrophobicity of the molecule play an important role in distribution phase of drug molecule and is evaluated under the MLogP value (octanol/water partition co efficient) (Clark 1999). The MLogP of all the curcumin analogues were within the acceptable range (< 5 Lipinski’s rule).

TPSA (Topological Polar Surface Area) is another important physicochemical property used to predict drug distribution attributes based on sum of all polar atoms such as oxygen, nitrogen and attached hydrogen. TPSA is a good descriptor illustrating intestinal absorption, hydrogen bonding potential, bioavailability, blood brain barrier penetration (BBB) and Caco-2 cell permeability. The TPSA value ≤ 140 Å and number of rotatable bonds (≤ 10) indicate good bioavailability (Clark 1999). The number of rotatable bonds found to be ≤ 10 in 119 curcumin analogues excluding 20 molecules. Rotatable polar atomic bonds increase the flexibility of molecules for more adaptable and efficient interaction with the enzyme active site and the value of TPSA of all curcumin analogues found to be in the range of 0.00–140 Å explicitly showing good oral bioavailability.

Biological activity of the curcumin analogues were evaluated against GPCR ligand, kinase inhibitor, nuclear receptor ligand, ion channel modulator, protease and enzyme inhibitory activity (Supplementary Table  3). The molecule having bioactivity score more than 0.00, is likely considerable biological activity, score between − 0.50 and 0.0 indicate moderate activity and if the score less than − 0.50 shows the inactive state. The present study illustrates that the curcumin analogues are biologically active and exhibit physiological effect with nuclear receptor ligands, inhibiting proteases, GPCR ligands and other enzymes. The bioactivity score for GPCR ligand, Ion channel modulator, Kinase inhibitor, Nuclear receptor ligand, protease inhibitor and enzyme inhibitor was found to be < − 0.50 for all tested molecules excluding five molecules.

Taken together the results show that curcumin and its analogues having significant physicochemical properties in consonance with better biological activity.

Molecular docking studies

All 139 Curcumin analogues showing the good drug-likeness properties were docked against kinase domain of ALK5 along with known inhibitors. Based on binding energy, the ligands were sorted and compared with known inhibitors. The results shows binding energy of S4, S5, S6, S30, S57 and S58 providing a decent antagonists based on docking energy above − 10.0 kcal/mol. Among them binding energy of S5 and S6 are close to known inhibitors by exhibiting H-bonding (Table 1). The complete results were furnished in the Supplementary Table 4.

Table 1.

Top curcumin analogues showing highest binding energy

Symbol ChemSpider ID IUPAC name of the compound
S4 4579943 (1E, 6E)-1-(3,4-dihydroxyphenyl)-7-(4-hydroxy-3-methoxyphenyl)hepta-1,6-diene- 3,5-dione
S5 4579942 (1E,6E)-1,7-bis(3,4-dihydroxyphenyl)hepta-1,6-diene-3,5-dione
S6 24660343 (1E,6E)-1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)hepta-1,6-diene-3,5-dione
S30 24676587 (1E,6E)-1-(4-hydroxy-3-methoxy-5-nitrophenyl)-7-(4-hydroxy-3-methoxyphenyl)hepta-1,6-diene-3,5-dione
S57 – (E)-7-(3,4-dihydroxyphenyl)-1-(4-hydroxy-3-methoxyphenyl) hept-4-en-3-one
S58 – (E)-1-(3,4-dihydroxyphenyl)-7-(4-hydroxy-3-methoxyphenyl) hept-4-en-3-one
Open in a new tab

These Curcumin analogues were classified into four groups based on the middle-linear seven carbon linkage between phenyl rings and β-diketone, monoketone, pyrazole, and isoxazole group (Ahmed et al. 2013). The S4, S5, S6 and S30 molecules belongs to β-diketone group and the remaining S57 and S58 belongs to monoketone.

Theoretically, all the six curcumin analogues exhibit good binding energy compared to the reference drugs LY364947, SD-208 and SB505124. The curcumin analogues S4, S5, S6, S30, S58 and S59 form hydrogen bond and the bond lengths ranging from 2.70 to 3.33 Å with the active pocket amino acid residues Leu278, His283, Asp281, Ala230, Ser280 and Lys232 of 1RW8 (Fig. 1). When compared with known inhibitors the curcumin analogues are having better hydrogen bonding potential with the active pocket amino acid residues of the receptor molecule (Table 2).

Fig. 1.

Fig. 1

Open in a new tab

Molecular docking study: Ligand as ball and stick (1B. S4; 2B. S5; 3B. S6; 4B. S30; 5B. S58; 6B.S59) at the binding pocket of ALK5 receptor

Table 2.

Molecular docking results of curcumin analogues against ALK5 receptor

Compounds Binding energy (kcal/mol) Protein ligands interaction
No. of H bonds Amino acid residues Distance (Å)
S4 − 10.0 3 Leu278 2.91
His283 2.80, 3.04
S5 − 10.2 4 Asp281 2.71
His283 2.72, 3.33, 3.15
S6 − 10.2 3 Leu278 2.86
His283 2.88, 2.92
S30 − 10.0 6 Leu278 2.70
Ala230 2.89
Ser280 3.11
His283 3.18, 3.03, 2.97
S57 − 10.0 5 Lys232 3.16
Leu278 2.87
Asp281 2.86
His283 3.05, 3.15
S58 − 10.0 4 Leu278 2.78
Asp281 2.91
His283 3.17, 3.24
Known Inhibitors
 LY364947 − 10.2 – – –
 SD-208 − 10.0 – – –
 SB505124 − 10.2 1 Ser280 2.88
Open in a new tab

Pharmacokinetics and toxicity

Pharmacokinetics attributes of six curcumin analogues holding better binding energy were further evaluated using preADMET program. Molecules retaining weak pharmacokinetic and higher toxicity properties get failed in clinical trials. The physicochemical properties such as lipophilicity (clogP), polar surface area, molecular weight (MW) and aqueous solubility (logS) were earlier evaluated using molinspiration server, these properties affect absorption and bioavailability of drug molecule. When compared with known inhibitors (LY364947, SD-208 and SB50512), the curcumin analogues pertaining almost similar physicochemical properties are listed in the Table 3. Lipophilicity (log P) is important property for the calculation of oral availability of the molecules. The six Curcumin analogues have ideal log p value ranging from 1.62 to 3.02 (≤ 5). TPSA (Topological Polar Surface Area) of the curcumin analogues is found to be 140 Å indicating the good bioavailability (Veber et al. 2002). The number of rotatable bond in curcumin analogues is above 5 when compared with known inhibitors, which indicate that the curcumin analogues are more flexible than known inhibitors as a result of which it shows good binding potentials in docking studies. The number of hydrogen bond acceptors and donors in the curcumin analogues are in the ideal range of 0–8 and 0–5 respectively.

Table 3.

Comparison of physicochemical properties of top six curcumin analogues against known inhibitors

No Compounds (chemspider ID) Molecular properties
MW miLogP TPSA nON nOHNH nrotb Volume
S4 4579943 354.3533 2.00 104.06 6 3 7 314.65
S5 4579942 340.3268 1.69 115.05 6 4 6 297.13
S6 24660343 324.3273 2.18 94.83 5 3 6 289.11
S30 24676587 413.3775 2.42 138.89 9 2 9 355.52
S57 – 342.3856 3.02 86.99 5 3 8 318.66
S58 – 342.3856 3.02 86.99 5 3 8 318.66
S59 553011 328.3591 2.71 97.98 5 4 7 301.13
Known inhibitors
 1 394909 (LY364947) 272.31 2.81 54.47 4 1 2 243.37
 2 8491497 (SD-208) 352.76 3.84 76.49 6 1 3 280.94
 3 8034640 (SB505124) 335.41 3.88 60.04 5 1 3 310.21
Open in a new tab

The HIA (human intestinal absorption) value for all six curcumin analogues and known inhibitors are in the range of 80–100%. This indicates that all curcumin analogues can be well absorbed via intestinal tract. In addition, curcumin analogues show average permeability to Caco2 cell (4–70) and to MDCK cell model (predicted value in the range 4–70) except S57 and S58 (predicted value above 70). In the distribution phase, the PPB binding assessment of curcumin analogues exhibit strong binding energy with plasma proteins (predicted value above 90%) except S4 and S30(< 90%). Generally weak plasma protein binding compounds exist freely for transport across the cell membrane and also for interaction with target. In addition, BBB penetration revealed that the S4, S5, S6 and S30 curcumin analogues showed low absorption in CNS (predicted value less than 0.1), while S57 and S58 has shown middle absorption (predicted value between 2.0 and 1.0). On the other hand known inhibitors shows high absorption to CNS compared to curcumin analogues by illustrating minimal side effects of curcumin analogues. The skin permeability is an another important risk assessment factor of the compounds during accidental contact with the skin and all the six curcumin analogues had revealed least skin permeability compared with the known inhibitors (Singh and Singh 1993) as shown in the Table 4. In the toxicity phase, the carcinogenic and mutagenic effects of curcumin analogues were evaluated. Ames test is a simple method to test mutagenicity of six curcumin analogues, among them S30, S57 and S58 had shown positive result indicating that these analogues act as mutagens, and the remaining S4, S5 and S6 had shown negative property depicting non-mutagen type. Finally, The drug-likeness and ADMET values of curcumin analogues with known inhibitors connotes that the selected curcumin analogues S4, S5, S6, S30, S58 and S59 are having the potential to act as good inhibitors against ALK5 receptor.

Table 4.

Comparison of ADMET properties of top six curcumin analogues against known inhibitors

Name ADMET Toxicity
Compounds (chemspider ID) HIA (%) PPB (%) BBB (%) Caco2 (nm/sec) Skin-permeability MDCK (nm/sec) Ames test Rodent carcinogenicity
Carcino_Mouse Carcino_Rat
S4 4579943 90.52 89.31 0.317 18.34 − 2.93 78.55 Non-mutagen Negative Positive
S5 4579942 83.96 95.62 0.479 18.89 − 3.15 53.37 Non-mutagen Negative Positive
S6 24660343 90.32 94.04 0.783 19.99 − 2.93 20.10 Non-mutagen Negative Positive
S30 24676587 85.47 89.19 0.070 12.09 − 2.36 15.99 Mutagen Positive Positive
S57 – 90.25 100.0 1.290 18.55 − 2.46 96.52 Mutagen Negative Positive
S58 – 90.25 100.0 1.217 19.45 − 2.46 96.52 Mutagen Negative Positive
Known inhibitors
 1 394909 (LY364947) 93.34 91.17 2.29 20.36 − 3.76 274.5 Mutagen Negative Negative
 2 8491497 (SD-208) 96.39 100.0 0.17 27.67 − 3.79 31.05 Mutagen Negative Negative
 3 8034640 (SB505124) 92.98 88.26 4.50 44.59 − 3.23 14.51 Non-mutagen Negative Positive
Open in a new tab

Prediction of metabolic site curcumin analogues

MetaPrint 2D predictor is a fast, efficient and accurate predictor which uses circular fingerprints and substrate/product ratios for predicting the sites and products of metabolism in small molecules. The atoms indicated in orange color would be moderately active at metabolic site, green color atoms indicate low metabolic active site, whereas red color atoms indicate high metabolic active site and no color would be metabolically very less/nil active (Boyer et al. 2007).

All the selected compounds-metabolic sites have been shown in the Fig. 2, among them S5 compound has more metabolic site than other compounds. In the case of lead S5 compound, the carbon atom number two, three, eighteen and nineteen showed high metabolic site. The carbon atom fourth and seventeenth exhibit moderate metabolic site and the carbon atom in five and sixteen shows low metabolic active site. Through molecular docking studies the Selectivity and steric information of the potential compounds studied, these helpful in predicting the site of metabolism and possible toxic metabolites.

Fig. 2.

Fig. 2

Open in a new tab

Metabolic site of lead molecules: NOR indicates the Normalized Occurrence Ratio; a high NOR indicates a more commonly reported site of metabolism in the metabolite database. The atoms are colored according to the chances of a metabolic site; No data: grey, Very low: Not colored, Low: green, Medium: orange and High: red

Conclusion

In the present study, six curcumin analogues S4, S5, S6, S30, S58 and S59 have shown promising ALK5 inhibition with excellent binding affinity as well as drug-likeness. Among them, S5 and S6 have been validated in all the drug designing parameters by showing significant binding interaction with the conserved amino acids in the receptor molecule by depicting greater binding affinity compared to standard ALK5 inhibitors. With these results, the selected curcumin analogues were further explored for detailed investigations and structural modification to arrive possible newer potent agents with better therapeutic activity against ALK5 receptor.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 23 kb) (23.1KB, docx)
Supplementary material 2 (DOCX 24 kb) (24.9KB, docx)
Supplementary material 3 (DOCX 21 kb) (21.2KB, docx)
Supplementary material 4 (DOCX 18 kb) (18.4KB, docx)

Acknowledgements

All the authors thankful to Registrar Kuvempu University Shivamogga, Karnataka, India for providing the necessary facility to complete the work and also Mr. Pavan kumar G S helping in the preparation of images.

Authors’ contributions

SK and SBS carried out the literature survey and molecular docking studies. UH carried out Drug-likeness and ADMET analysis. Dr. BBR and Dr. MH have guided and supported in the preparation of manuscript. All authors have read and approved the final manuscript.

Competing interests

All authors declared that they have no competing interest.

Footnotes

Electronic supplementary material

The online version of this article (10.1007/s40203-017-0034-0) contains supplementary material, which is available to authorized users.

References

  1. Ahmed M, Sadek MM, Serrya RA, et al. Assessment of new anti-HER2 ligands using combined docking, QM/MM scoring and MD simulation. J Mol Graph Model. 2013;40(2):91–98. doi: 10.1016/j.jmgm.2012.12.001. [DOI] [PubMed] [Google Scholar]
  2. Akhurst RJ, Derynck R. TGF-Beta Signaling in Cancer–a Double-Edged Sword. Trends Cell Biol. 2001;11(11):S44–S51. doi: 10.1016/S0962-8924(01)02130-4. [DOI] [PubMed] [Google Scholar]
  3. Allegra Alessandro, et al. Anticancer activity of curcumin and its analogues: Preclinical and Clinical Studies. Cancer Invest. 2017;35(1):1–22. doi: 10.1080/07357907.2016.1247166. [DOI] [PubMed] [Google Scholar]
  4. Ammon Hermann, Wahl Martin. Pharmacology of Curcuma Longa. Planta Med. 1991;57(1):1–7. doi: 10.1055/s-2006-960004. [DOI] [PubMed] [Google Scholar]
  5. Boyer S, et al. Reaction site mapping of xenobiotic biotransformations. J Chem Inf Model. 2007;47(2):583–590. doi: 10.1021/ci600376q. [DOI] [PubMed] [Google Scholar]
  6. Callahan JF, et al. Identification of novel inhibitors of the transforming growth factor beta1 (TGF-beta1) type 1 receptor (ALK5) J Med Chem. 2002;45(5):999–1001. doi: 10.1021/jm010493y. [DOI] [PubMed] [Google Scholar]
  7. Carlsson L, et al. Use of historic metabolic biotransformation data as a means of anticipating metabolic sites using MetaPrint2D and Bioclipse. BMC Bioinform. 2010;11(1):362. doi: 10.1186/1471-2105-11-362. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Clark David E. rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 2. prediction of blood–brain barrier penetration. J Pharm Sci. 1999;88(8):815–821. doi: 10.1021/js980402t. [DOI] [PubMed] [Google Scholar]
  9. DaCosta Byfield S, Major Christopher, Laping Nicholas J, Roberts Anita B. SB-505124 Is a selective inhibitor of transforming growth factor- type i receptors ALK4, ALK5, and ALK7. Mol Pharmacol. 2004;65(3):744–752. doi: 10.1124/mol.65.3.744. [DOI] [PubMed] [Google Scholar]
  10. Ertl P, Rohde B, Selzer P. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J Med Chem. 2000;43:3714–3717. doi: 10.1021/jm000942e. [DOI] [PubMed] [Google Scholar]
  11. Gaedeke Jens, Noble Nancy A, Border Wayne A. Curcumin blocks multiple sites of the TGF-β signaling cascade in renal cells. Kidney Int. 2004;66(1):112–120. doi: 10.1111/j.1523-1755.2004.00713.x. [DOI] [PubMed] [Google Scholar]
  12. Gupta, Subash C, Kim Ji Hye, Prasad Sahdeo, Aggarwal Bharat B. Regulation of survival, proliferation, invasion, angiogenesis, and metastasis of tumor cells through modulation of inflammatory pathways by nutraceuticals. Cancer Metastasis Rev. 2010;29(3):405–434. doi: 10.1007/s10555-010-9235-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Gupta Subash C, Patchva Sridevi, Koh Wonil, Aggarwal Bharat B. Discovery of curcumin, a component of golden spice, and its miraculous biological activities. Clin Exp Pharmacol Physiol. 2012;39(3):283–299. doi: 10.1111/j.1440-1681.2011.05648.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Ito Ji, et al. PoSSuM v. 2.0: data update and a new function for investigating ligand analogs and target proteins of small-molecule drugs. Nucleic Acids Res. 2015;43(D1):D392–D398. doi: 10.1093/nar/gku1144. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Jin Cheng Hua, et al. Discovery of N -((4-([1,2,4]Triazolo[1,5- a]Pyridin-6-Yl)-5-(6-Methylpyridin-2-Yl)-1 H -Imidazol-2-Yl)methyl)-2-Fluoroaniline (EW-7197): a highly potent, selective, and orally bioavailable inhibitor of TGF-β type I receptor kinase as cancer immunotherapeutic/antifibrotic agent. J Med Chem. 2014;57(10):4213–4238. doi: 10.1021/jm500115w. [DOI] [PubMed] [Google Scholar]
  16. Kuttan R, Bhanumathy P, Nirmala K, George MC. Potential anticancer activity of turmeric (Curcuma Longa) Cancer Lett. 1985;29(2):197–202. doi: 10.1016/0304-3835(85)90159-4. [DOI] [PubMed] [Google Scholar]
  17. Li H, et al. Dihydropyrrolopyrazole transforming growth factor-β Type I receptor kinase domain inhibitors: a novel benzimidazole series with selectivity versus transforming growth factor-β Type II receptor kinase and mixed lineage kinase-7. J Med Chem. 2006;49(6):2138–2142. doi: 10.1021/jm058209g. [DOI] [PubMed] [Google Scholar]
  18. Li Rui, et al. Curcumin Inhibits transforming growth factor-β1-induced EMT via PPARγ pathway, not smad pathway in renal tubular epithelial cells. PLoS One. 2013;8(3):e58848. doi: 10.1371/journal.pone.0058848. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Lipinski Christopher A. Lead- and Drug-like Compounds: the rule-of-five evolution. Drug Discov Today Technol. 2004;1(4):337–341. doi: 10.1016/j.ddtec.2004.11.007. [DOI] [PubMed] [Google Scholar]
  20. Liu Z, et al. VEGF and inhibitors of TGF Type-I receptor kinase synergistically promote blood-vessel formation by inducing 5-integrin expression. J Cell Sci. 2009;122(18):3294–3302. doi: 10.1242/jcs.048942. [DOI] [PubMed] [Google Scholar]
  21. Lowe DM, et al. Chemical name to structure: OPSIN, an open source solution. J Chem Inf Model. 2011;51(3):739–753. doi: 10.1021/ci100384d. [DOI] [PubMed] [Google Scholar]
  22. Massagué J. TGF-β signal transduction. Annu Rev Biochem. 1998;67(1):753–791. doi: 10.1146/annurev.biochem.67.1.753. [DOI] [PubMed] [Google Scholar]
  23. Massagué Joan. TGFβ in Cancer. Cell. 2008;134(2):215–230. doi: 10.1016/j.cell.2008.07.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Massagué Joan. TGFβ signalling in context. Nat Rev Mol Cell Biol. 2012;13(10):616–630. doi: 10.1038/nrm3434. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Massagué J, Blain SW, Lo RS. TGF beta signaling in growth control, cancer, and heritable disorders. Cell. 2000;103(2):295–309. doi: 10.1016/S0092-8674(00)00121-5. [DOI] [PubMed] [Google Scholar]
  26. Melisi D, et al. LY2109761, a novel transforming growth factor receptor type I and Type II dual inhibitor, as a therapeutic approach to suppressing pancreatic cancer metastasis. Mol Cancer Ther. 2008;7(4):829–840. doi: 10.1158/1535-7163.MCT-07-0337. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Morris GM, et al. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem. 1998;19(14):1639–1662. doi: 10.1002/(SICI)1096-987X(19981115)19:14&#x0003c;1639::AID-JCC10&#x0003e;3.0.CO;2-B. [DOI] [Google Scholar]
  28. Negi PS, Jayaprakasha GK, Jagan Mohan Rao L, Sakariah KK. Antibacterial activity of turmeric oil: a byproduct from curcumin manufacture. J Agric Food Chem. 1999;47(10):4297–4300. doi: 10.1021/jf990308d. [DOI] [PubMed] [Google Scholar]
  29. O’Boyle NM, et al. Open Babel: an open chemical toolbox. J Cheminform. 2011;3(1):33. doi: 10.1186/1758-2946-3-33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Pence HE, Williams A. ChemSpider: an online chemical information resource. J Chem Educ. 2010;87(11):1123–1124. doi: 10.1021/ed100697w. [DOI] [Google Scholar]
  31. Sawyer JS, et al. Synthesis and activity of new aryl- and heteroaryl-substituted 5,6-dihydro-4H-pyrrolo[1,2-b]pyrazole inhibitors of the transforming growth factor-beta type I receptor kinase domain. Bioorg Med Chem Lett. 2004;14(13):3581–3584. doi: 10.1016/j.bmcl.2004.04.007. [DOI] [PubMed] [Google Scholar]
  32. Senft Christian, et al. The nontoxic natural compound curcumin exerts anti-proliferative, anti-migratory, and anti-invasive properties against malignant gliomas. BMC Cancer. 2010;10(1):491. doi: 10.1186/1471-2407-10-491. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Sharma OP. Antioxidant activity of curcumin and related compounds. Biochem Pharmacol. 1976;25(15):1811–1812. doi: 10.1016/0006-2952(76)90421-4. [DOI] [PubMed] [Google Scholar]
  34. Shi Yigong, Massagué Joan. Mechanisms of TGF-beta signaling from cell membrane to the nucleus. Cell. 2003;113(6):685–700. doi: 10.1016/S0092-8674(03)00432-X. [DOI] [PubMed] [Google Scholar]
  35. Singh S, Singh J. Transdermal drug delivery by passive diffusion and iontophoresis: a review. Med Res Rev. 1993;13(5):569–621. doi: 10.1002/med.2610130504. [DOI] [PubMed] [Google Scholar]
  36. Son JY, et al. EW-7197, a novel ALK-5 kinase inhibitor, potently inhibits breast to lung metastasis. Mol Cancer Ther. 2014;13(7):1704–1716. doi: 10.1158/1535-7163.MCT-13-0903. [DOI] [PubMed] [Google Scholar]
  37. ten Dijke Peter, et al. Identification of Smad7, a TGFbeta-Inducible antagonist of TGF-beta signalling. Nature. 1997;389(6651):631–635. doi: 10.1038/39369. [DOI] [PubMed] [Google Scholar]
  38. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. NIH Public Access. J Comput Chem. 2010;31(2):455–461. doi: 10.1002/jcc.21334. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Tsukazaki T, Chiang TA, Davison AF, Attisano L, Wrana JL. SARA, a FYVE domain protein that recruits Smad2 to the TGFbeta receptor. Cell. 1998;95(6):779–791. doi: 10.1016/S0092-8674(00)81701-8. [DOI] [PubMed] [Google Scholar]
  40. Veber Daniel F, et al. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002;45(12):2615–2623. doi: 10.1021/jm020017n. [DOI] [PubMed] [Google Scholar]
  41. Wallace AC, Laskowski RA, Thornton JM. LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng. 1995;8(2):127–134. doi: 10.1093/protein/8.2.127. [DOI] [PubMed] [Google Scholar]
  42. Wrana Jeffrey L, Attisano Liliana, Wieser Rotraud, Ventura Francesc, Massagué Joan. Mechanism of activation of the TGF-β receptor. Nature. 1994;370(6488):341–347. doi: 10.1038/370341a0. [DOI] [PubMed] [Google Scholar]
  43. Xu Jian, Lamouille Samy, Derynck Rik. TGF-β-induced epithelial to mesenchymal transition. Cell Res. 2009;19(2):156–172. doi: 10.1038/cr.2009.5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Yamashita S, et al. Optimized conditions for prediction of intestinal drug permeability using Caco-2 cells. Eur J Pharm Sci Off J Eur Fed For Pharm Sci. 2000;10(3):195–204. doi: 10.1016/s0928-0987(00)00076-2. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material 1 (DOCX 23 kb) (23.1KB, docx)
Supplementary material 2 (DOCX 24 kb) (24.9KB, docx)
Supplementary material 3 (DOCX 21 kb) (21.2KB, docx)
Supplementary material 4 (DOCX 18 kb) (18.4KB, docx)

Articles from In Silico Pharmacology are provided here courtesy of Springer

RESOURCES