Introduction

Breast cancer is one of the most frequent malignancies in women, with approximately globally 1.7 million new cases being diagnosed each year, with more than 0.5 million deaths [1]. In 2012, 1.67 million instances have been diagnosed, and approximately 522,000 deaths had been reported [2, 3]. In the year 2018, breast cancer led to the deaths of 627,000 women, accounting for about 15% of all cancer deaths within females. Whereas rates of breast cancer are greater among females in more advanced nations, they are rising in nearly every region around the world. Impacting 2.1 million deaths among women, this information was obtained from the World Health Organization (WHO) [4]. After this, WHO updated the new data on breast cancer cases in the year 2020, according to that 685,000 women deaths due to breast cancer and 2.3 million cases were diagnosed [4a]. Apart from this, breast cancer is on the top list as compared to other cancer types represented in Fig. 1 [4b].

Fig. 1
figure 1

Diagnosed cases among different cancer types

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Nowadays, patients with estrogen-dependent breast cancer (EDBC) or hormonal-dependent breast cancer (HDBC) [5] are treated primarily based on a constellation of medical and histopathological criteria, as well as the assessment of three hormonal receptors (HER2 (human epidermal growth factor receptor 2), estrogen receptor (ER), and progesterone receptor (PR)). Patients with breast cancer who have ER-positive tumors typically have a better prognosis than those who have ER-negative tumors [6]. Additionally, upon reviewing the literature, we discovered that approximately 70 to 75% of breast cancers express positive estrogen receptors (ERa), and the responsible receptors are ERα-positive (ERα+) [7,8,9,10], whereas only 24.9% of breast cancer express the negative estrogen receptor and are considered ER-negative (ER −); due to this reason, we have selected ERα-positive as a protein for molecular docking study [8].

Additionally, EDBC-based breast cancer is dependent on estrogen. The aromatization of the A ring results in the production of estrogens, which is catalyzed by the cytochrome p450 enzyme or aromatase; this aromatase is encoded by the CYP19A1 gene [11]. Aromatase converts testosterone (T) and androstenedione (ASD) and then synthesizes estrogens such as estrone (E1) and estradiole (E2) [11,12,13]. The synthesized estrogens then react with Era, which is encoded by the ESR1 gene that is responsible for breast epithelial cell proliferation and tumor development in cancer patients [14]. Additionally, two pathways exist for suppressing the progression of breast cancer: the first is AIs, which inhibit estrogen production and eventually treat the cancer [15], and the second is antagonist modulators, such as SERDs (selective estrogen down-regulators) and SERMs (selective estrogen receptor modulators), which directly inhibit genomic estrogen signaling [16]. Perhaps AIs are only effective against postmenopausal breast cancer, whereas antagonist modulators are effective against both types of breast cancer [17, 18].

Several medications are available in the drug market for both ways, AIs and antagonists. First is exemestane (AIs) (Fig. 2a). This medicine is used to treat a positive type of breast cancer, and this was approved by US FDA in the year 2005; this drug is the type of steroidal aromatase inhibitor [19, 20]. Moreover, drug’s docking study was done by Setti et al., and that study reveals the MolDock score of −143.607 kcal/mol [21]. Apart from this, another two drugs are utilized to treat the same, which are tamoxifen (Fig. 2b) (antagonist-SERM) under the brand name (like Nolvadex and Soltamox) [22] and letrozole (non-steroidal). Tamoxifen is a widely sold drug but there are certain detrimental drawbacks such as it produces endometric cancer, blood clot in the lung and legs, stroke, and cataracts. Similarly, letrozole (AIs) (Fig. 2c) consists of hazardous side effects of polycystic ovary syndrome (PCOS) [23, 24]. The docking analysis of these two medicines was carried out by TilakVijay et al. and Verma et al., on breast cancer, which depicted binding affinity of −149.856 and −136.784 kcal/mol, respectively [24, 25]. Another medication on the list is the anastrazole (AIs) (Fig. 2d); this is also a non-steroidal drug that comprises adverse effects such as hot flashes, weakness, bone, joint, and muscle pain or stiffness, sore throat or cough, and high blood pressure [26]. This drug docking investigation was done by Setti et al. and reveals the energy score of −149.521 kcal/mol. Docking data for these medications on breast cancer were obtained using MVD software [21]. On the other hand, some natural products or phytochemicals have been docked for the same disease. For example, Rahman et al. and Thareja et al. used compounds such as abruquiones and flavonoids, which have MolDock scores of −86.309 and −94.36 kcal/mol, respectively [27, 28]. Additionally, these studies’ minimum binding affinity values are relatively low in comparison to ours. Table 1.

Fig. 2
figure 2

Chemical structure of drugs used to treat breast cancer. (a) Exemestane, (b) tamoxifen, (c) letrozole, and (d) anastrazole

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Table 1 MolDock scores of previously reported drugs against breast cancer by MVD
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The purpose of this paper is to identify DKC derivatives with anti-aromatase and anti-Era + receptor activity for use in breast cancer treatment. Indeed, curcumin, a potential anti-cancer agent, has been demonstrated to be more effective in the prevention and treatment of several cancers. Curcumin is a type of polyphenol compound derived from the South Asian plant Curcuma domestica. Curcuminoids found in C. longa include curcumin, bisdemethoxycurcumin, and demethoxycurcumin. Curcumin is widely used in Ayurveda drugs for a variety of medicinal purposes due to its antioxidant, antiseptic, analgesic, antimalarial, and anti-inflammatory properties. Curcumin effectively suppresses human carcinomas such as malignant melanoma, cancers of the neck and head, breast, colon, pancreas, prostate, and gonads. Additionally, curcumin derivatives exhibit biological activity against the deadly virus COVID-19 [29]. The inhibitory effects of curcumin compounds on human malignancies are mediated primarily through the control of biochemical cascades, numerous transcription factors, growth factors, pro-inflammatory cytokines, super molecular kinases, and various enzymes [30].

Curcumin’s bioavailability, on the other hand, is low due to insufficient stomach absorption, restricted tissue distribution, rapid metabolism, and consequent removal from the body [31]. To address this issue, we chose derivatives of deketene curcumin for a variety of reasons, including the possibility of increasing metabolic stability by omitting the b-diketone moiety (Fig. 3). Despite this, some researchers argue that the presence of the b-diketone moiety is necessary for curcumin’s therapeutic properties. Recent research from a variety of agencies confirmed that certain curcumin analogues with a 5-carbon enone spacer but no b-diketone retained or enhanced growth-suppressive activity against various cancer cells. Certain mono-carbonyl analogues of curcumin that lack the b-diketone moiety have been shown greater anti-bacterial and anti-inflammatory activity than curcumin [32]. Compounds with the chemical formula 1, 5-diaryl-1, 4-pentadien-3-ones are the derivatives of deketene curcumin or mono-carbonyl analogues of curcumin. These are structural analogues of curcumin (1, 7-bis-(4-hydroxy-3-methoxyphenyl)-1, 6-heptadiene-3, 5-Dione), a prominent pigment found in the Indian spice turmeric Curcuma longa, Zingiberaceae. The structures of curcumin and deketene curcumin (DKC) are more or less same; however, DKC has greater biological activity than pure curcumin [33].

Fig. 3
figure 3

Structural modification in deketene curcumin, in comparison with curcumin and active sites of the compound responsible for anti-cancer activity [31]

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Certain deketene curcumin derivatives are synthesized and evaluated for their efficacy as anti-cancer agents. In comparison, the technique used to determine whether these compounds can inhibit cancer cell progression is time-consuming and expensive, such as in vivo and in vitro studies. As a result, numerous studies have demonstrated that computational approaches, such as structural bioinformatics and pharmacophore modeling, are the optimal choice due to their high accuracy and reduced time requirements [34]. Docking multiple ligands to the active protein and scoring them to determine binding affinity and interaction intensity has become a widely used technique for virtual screening of large databases as well as lead optimization [35]. On the other hand, the characteristics of a medication’s absorption, distribution, metabolism, and elimination (ADME) are critical for a treatment candidate’s eventual clinical success. They play a critical role in lowering the failure rate of drug candidates in early-stage clinical trials [36]. Additionally, the synthesis of curcumin derivatives is both environmentally and economically sustainable, as a variety of green routes are available. Numerous benefits can be demonstrated, ranging from high yields to secure, low-cost, and straightforward workup procedures [37].

The present work describes the screening of various deketene curcumin derivatives for their ability to bind directly or indirectly to the ERα-positive receptor and the aromatase enzyme, both of which were obtained from the Protein Data Bank, using the Molegro Virtual Docker software. Apart from this, we conducted ADME studies on the ligands and docking score comparisons with existing drugs used to treat positive type breast cancer. While deketene curcumin derivatives demonstrate potential for both target proteins, they are more effective in terms of binding affinity on the Era + receptor. As a result, DKCs are considered to be effective agents for the treatment of breast cancer.

Computational methodology

Protein and ligand preparation

The crystal structures of Erα + receptor and aromatase which consist of the PDB ID: 3ERT and 3S79 respectively were directly downloaded from the workspace of software MVD v.7.0.0, where the key of the PDB ID (Protein Data Bank) needs to enter which might be accessed at the URL (http://www.rscb.org/pdb). These IDs have resolutions of 1.90 Å and 2.75 Å correspondingly. Additionally, the downloaded protein contains some rumpled amino acids that are repaired and rebuilt using the mutated and optimization using MVD [38, 39]. The two-dimensional (2D) structures of deketene curcumin derivatives as ligands were obtained using the Chem Bio Draw 12.0.02 computer program. Then, 2D to three-dimensional (3D) representations were converted by the use of Chem Bio 3D 12.0.02 software, and afterward, these were energetically minimized by using a method implemented in the same software and saved as SDF format (*.sdf). Table 2 displays the 2D and 3D structures of all ligands with their specific pharmacological activities. To generate accurate predictions, the imported structures must be properly prepared, which means they must have the correct atom connectivity and bond ordering. This is because when the PDB file is downloaded in its original state, it contains co-factors and water molecules that may cause an error. To address this issue, water molecules and co-factors were manually removed from the MVD after the PDBs were introduced. The absent charges, protonation states, and polar hydrogen allocation were also carried out using a special Molegro algorithm [40].

Cavity or active site detection

Cavity detection is a critical operation that takes place during the docking process. Through the use of a preparation window in the MVD software, efficient binding sites for the selected aromatase and Erα + were identified during this process. This was accomplished through the use of a program called the grid-based anticipation algorithm. Moreover, for the accomplishment of this computational algorithm, the steps had been selected as the greatest numbers of cavities were 5 within 30 × 30 × 30 Å3 cube and the volume was selected between 5 and 10,000 Å. Here, from the five selected cavities, the one with having an optimum value of the cavity has been taken for further consideration in the docking process, such as for aromatase and Erα + the volume (96.256, 364.032 Å3), and surface area (204.8, 844.8 Å2) respectively. Table 3 and Fig. 4a, b illustrate all detected cavities and their associated values such as volume, surface area, and coordinates.

Table 2 List of deketene curcumin and its derivatives utilized for docking study with aromatase and Erα+
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Table 3 Potential binding cavities (1–5) predicted inside Erα + (PDB ID: 3ERT) and aromatase (PDB ID: 3S79), as well as their volume, surface area, and location coordinates
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Fig. 4
figure 4

MVD detects five active sites in the protein structure of (a) aromatase and (b) Erα + (PDB ID: 3S79, 3ERT) (cavity volume shown in Å). Detected cavity representation: color with specifications, green, cavity; blue (ß sheets); and red (α helices). Cartoon models of aromatase (a) and Erα + (b): yellow, binding ligand

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Molecular docking

During the molecular docking process, the energy-optimized conformers of the generated compounds were loaded into MVD’s pre-saved workspace, with the optimized aromatase and Erα having an anticipated binding cavity. For docking with 25 deketene curcumin derivatives and currently available medications, the most plausible aromatase (cavity 1) and Erα receptor (cavity 1) binding sites were chosen. The 30 Å grid resolution was set during the docking process, and then 10 runs and the population size of 50 were chosen for operating molecular docking simulation; this can be done by the usage of MolDock Simplex Evolution search algorithm [48]. Here, the term number of runs means the number of docking simulations that were run for each ligand that was docked, with each iteration returning to a single final solution, for instance, pose. This process in the software was done by a special algorithm in which a 12–6 conceivable and sp2-sp2 torsion by Lennard Jones term was used [49]. Based on pilot docking findings, re-rank scores were determined for rating the inhibitor poses in the MolDock, and the poses chosen as the best for all of the deketene curcumin derivatives and current medicines were evaluated here. Further, default parameters were selected for maximum iteration (1500), threshold (100), binding radius (20 Å), SE maximum steps (300), and SE neighboring distance factor (1.00). For the study of binding interactions like hydrogen bonding and steric interaction, the best conformer or pose of the ligand was selected, which has the lowest MolDock score [50].

The binding affinity or MolDock score can be determined by the utilization of differential evolution algorithm. Here, Eq. 1 describes the total biding affinity or MolDock score (Escore), and the terms used in this equation are Einter which shows the ligand and receptor energy interaction and Eintra which depicts the ligand internal energy. Furthermore, to calculate the Einter and Eintra, Eqs. 2 and 3 are utilized correspondingly. Despite this, piecewise linear potential (EPLP) was used to check the steric interaction among the atoms which are charged [48, 51, 52].

$${E}_{\mathrm{score}}={E}_{\mathrm{inter}}+{E}_{\mathrm{intra}}$$
(1)
$${E}_{\mathrm{inter}}=\sum_{i=\mathrm{ligand}}\sum_{j=\mathrm{protein}}\left[{E}_{\mathrm{PLP}}\left({r}_{ij}\right)+332.0\frac{{{q}_{i}q}_{j}}{4{r}_{ij}^{2}}\right]$$
(2)
$${E}_{\mathrm{intra}}=\sum\limits_{i=\mathrm{ligand}}\sum\limits_{j=\mathrm{protein}}\left[{E}_{\mathrm{PLP}}\left({r}_{ij}\right)\right]+\sum_{\mathrm{flexible\;bond}}A\left[1-\mathrm{cos}(m\theta -{\theta }_{^\circ })\right]+{E}_{\mathrm{clash}}$$
(3)

The first term in Eq. 3 describes the energy of the ligand’s pair of atoms; however, this is only valid for a single bond. The torsional energy has been depicted by the second term of the equation, in that the torsional angle of the bond is denoted by Θ. The average of the torsional energy can be taken if the number of torsions would determine. The third term which is Eclash is used when the distance among 2 dense atoms is lesser than 2.0 Å and allotting penalty of 1000 kcal/mol [48]. Apart from this, Molegro Virtual Docker (MVD) is suggested by various scientists because, if compared to specific accessible docking programs, MVD has great accuracy (MD: 87%, Glide: 82%, Surflex: 75%, FlexX: 58%) and has been tested to be worthwhile in several recent studies; additionally, this program is less costly and produces docking results in less time [48]. The binding of ligands by molecular docking (deketene curcumin derivatives) and protein was visualized using Molegro Molecular Viewer 7.0.0 (MMV) (AIs and Erα). It is software for studying and simulating molecular protein–ligand interactions, sequences, and structures.

ADME/pharmacokinetics studies

The antagonistic response of an inhibitor to an enzyme or a protein receptor does not guarantee its suitability as a potential drug [53]. As a result, ADME (absorption, distribution, metabolism, and excretion) analysis and drug-likeness analysis have been critical in drug discovery because they aid in making the correct decision about whether or not to evaluate inhibitors against a biological system [53, 54]. Additionally, the majority of failed medications in clinical trials were produced by inhibitors with insufficient ADME properties, causing severe damage to biological systems as a result of the excessive toxicity. Additionally, the ADME study was conducted via the Swiss ADME web server (Swiss Institute of Bioinformatics, Switzerland). The selected 25 deketene curcumin derivatives are then imported into the program via SMILES (Simplified Molecular Input Line Entry System) in order to generate a pharmacokinetic profile or drug-likeness specification [55].

Apart from that, a specific rule has been established to determine whether or not an inhibitor with specific medical and pharmacological characteristics is a safe and orally active medication in the human body. Christopher A. Lipinski developed a thumb rule for determining drug-likeness in 1997. Additionally, the rule of five (5) may be referred to as Pfizer’s rule of five (5) or Lipinski’s rule of five (5) [54]. As per this rule, if two (2) or more of these thresholds are met, a drug could be orally absorbed into the human body. These cut-offs are as follows: the MW (molecular weight) of the molecule should be less than 500 g/mol, iLOGP (octanol/water partition coefficient) should be ≤ 5, then the nHBD (H-bond donor) and nHBA (H-bond acceptor) should be in the range of ≤ 10 and ≤ 5, respectively, and further, the TPSA (topology polar surface area) should be 40 Å2 [56, 57]. Table 4 illustrates the drug-like data of deketene curcumin and its derivatives. If these cut-off values correspond to the respective drug, there will be no violation of the Lipinski rule.

Table 4 Drug likeness and ADME/Pharmacokinetics data of DKC derivatives
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Result and discussion

In present work, 25 DKC compounds were successfully docked to the human placental aromatase cytochrome p450 and Erα + receptor in this study (PDB ID: 3S79, 3ERT, respectively). Additionally, a comparative docking study was conducted using currently used drugs (tamoxifen, anastrazole, exemestane, and letrozole) to treat breast cancer. As previously stated and as determined through the literature, tamoxifen is an antagonist type of drug (study conducted on Erα +), whereas the other three are AI type of drugs (study conducted on aromatase); thus, different PDBs were used for the comparative study. Moreover, the best poses were selected to calculate the MolDock and re-rank scores while performing the docking study. Here, Table 5 represents the MolDock score, H-bonding, re-rank score, and steric score between protein of breast cancer and ligand (DKC derivatives and 4 drugs). In addition to this, through literature it was confirmed that ligands (DKC-10, DKC-20, and DKC-21 for Erα +) which are exhibited binding affinity less than −150 kcal/mol would be regarded as a more effective inhibitor [40,41,42,43,44,45,46,47,48]. In this study, 3 deketene curcumin derivatives (DKC-10, DKC-20, and DKC-21) out of 25 have been abstracted for depth explanation as these derivatives elicit better binding energy (for Erα + −204.461 kcal/mol, −177.278 kcal/mol, and −161.958 kcal/mol, respectively, for aromatase, −201.613 kcal/mol, −131.397 kcal/mol, and −123.724 kcal/mol correspondingly), re-rank score, and H-bonding. The MolDock scores of DKC derivatives exhibited good energy in comparison with core DKC-1; the core DKC-1 has been taken for comparison among selected 3 ligands. Apart from this, other 21 ligands of DKC derivatives show optimum binding affinity in comparison with DKC-1. Despite this, three DKC derivative activities in terms of binding affinity on aromatase are low as compared to estrogen receptor.

Table 5 Re-rank score, MolDock score, H-bonding, and steric score of DKC derivatives and reference drugs against breast cancer (PDB: 3ERT, 3S79)
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Furthermore, cartoon representations of the binding interaction between ligand and protein have been included in Figs. 5 and 6. Docking results indicate that DKC-10 has the highest MolDock score, H-bonding, and steric interaction with the estrogen and aromatase protein structures, in comparison to other DKC derivatives. Tables 6 and 7 and Figs. 7 and 8 illustrate the H-bonding and steric interaction of the DKC-1, DKC-10, DKC-20, and DKC-21 with Erα + . Similarly, the aromatase’s ligand interactions are depicted in Tables 8 and 9 and Figs. 9 and 10. Additionally, these tables and figures include interactions for the reference drugs.

Fig. 5
figure 5

Docking interaction of 4 DKC ligands and tamoxifen with Erα + (PDB: 3ERT). (a–d) DKC-1, 10, 20, and 21 in stick line, yellow color; protein back bone, red color (α helices); and cavity, green color and (e) tamoxifen in pink color with same protein specifications

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Fig. 6
figure 6

Docking interaction of 4 DKC ligands, anastrazole, exemestane, and letrozole with aromatase (PDB: 3S79). (a–d) DKC-1, 10, 20, and 21 in stick line, yellow color; protein back bone, red color (α helices) and blue (β-sheets); and cavity, green color, (e) anastrazole, (f) exemestane, and (g) letrozole in pink, purple, and orange color, respectively, with same protein specifications

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Table 6 Properties of H-bond interaction between top three docking poses of DKC derivative and tamoxifen with PDB ID: 3ERT
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Table 7 Properties of steric interaction between top three docking poses of DKC derivative and tamoxifen with PDB ID: 3ERT
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Fig. 7
figure 7

H-bond interactions (blue-dotted bonds) displayed between 4 DKC ligands and tamoxifen with Erα + PDB ID: 3ERT. (a–d) DKC-1, 10, 20, and 21 hydrogen bonding interaction with estrogen receptor and (e) tamoxifen hydrogen bonding with Erα + 

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Fig. 8
figure 8

H-bond interactions (blue-dotted bonds) displayed between 4 DKC ligands and 3 drugs with aromatase PDB ID: 3S79. (a–d) DKC-1, 10, 20, and 21 hydrogen bonding interaction with aromatase and (e–g) exemestane, anastrazole, and letrozole hydrogen bonding with aromatase

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Table 8 Properties of H-bond interaction between top 3 docking poses of DKC derivative and exemestane, anastrazole, and letrozole with PDB ID: 3S79
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Table 9 Properties of steric interaction between top 3 docking poses of DKC derivative and exemestane, anastrazole, and letrozole with PDB ID: 3S79
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Fig. 9
figure 9

Steric interactions (red-dotted bonds) displayed between ligands DKC-1, DKC-10, DKC-20, DKC-21, and tamoxifen with Erα+ (PDB ID: 3ERT)

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Fig. 10
figure 10

Steric interactions (red-dotted bonds) displayed between ligands DKC-1, DKC-10, DKC-20, DKC-21, exemestane, anastrazole, and letrozole with aromatase (PDB ID: 3S79)

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To begin with, a depth description of each of these ligand's hydrogen and steric interaction with Erα + and aromatase has been discussed. First, DKC-1 forms H-bond with Erα + and aromatase by binding with amino acids His 524, Gly 521, Gly 420, Thr 347, Leu 387, Arg 394 and Arg 403, Tyr 366, Gln 367, Met 68, Ser 72, and His 475, respectively. In the case of estrogen receptor H-bonding, the interacting atoms (O (24), O(25) of the hydroxyl functional group, then atoms O (22), O (15) of the methoxy group, and atoms O (6) of the carbonyl group responsible for H-bonding) of DKC-1 ligand with corresponding amino acids (Gly 521, Gly 420, Leu 387, His 524, Arg 394 and Thr 347) (Fig. 7a). Similarly, for H-bonding in aromatase by DKC-1, the same functional groups except carbonyl are involved with binding residues and ligand atoms which are Ser 72 with O(24), Met 68 with O(24), and Tyr 366 with O(15) for hydroxyl and His 475, Arg 403, and Gln 367 for methoxy group; here noticeable observation can be seen that the same interacting oxygen atom number “O(15)” for methoxy group is involved for all the amino acids (Fig. 8a) (Tables 6 and 8). Apart from this, steric interaction can be observed in both the proteins (Erα + and aromatase) and amino acids which involved in it are Met 343, Glu 419, Gly 521, Gly 420, Thr 347, Phe 404, Leu 346, Glu 353, Ala 350, Leu 387, Leu 349 and His 475, Gln 367, Lys 473, Tyr 366, and Met 68, respectively. Here, it can be observed that more number of residues are involved for binding with Erα + in steric interactions as compared to aromatase (Figs. 9a and 10a, Tables 7 and 9). In addition to this, steric interactions of DKC-10 ligand with both the proteins have been illustrated in Figs. 9b and 10b. The involving residues for steric interactions are Ser 341, Cys 530, Leu 536, and Tyr 526 for estrogen receptor while Arg 400, Arg 79, Leu 479, Arg 403, His 475, Lys 473, Met 68, Trp 67, and Ser 72 for aromatase. Besides this, as per Figs. 7b and 8b, the H-bonding interactions are observed between DKC-10 ligand and subsequent protein structures of Erα + and aromatase. Here, in DKC-10, H-bonding interaction with estrogen receptor consists of amino acids binding with the interacting atom of respective functional groups which are Ser 341 with O(21) of –OCH3 group, Cys 530 with O(4) of –C = O group, Leu 536 with N(35) of triazole moiety, and Tyr 526 with N(48) of –NH group (Fig. 7b), whereas in H-bonding between DKC-10 and aromatase, the interacting atoms O(12), O(12), O(51), O(62), and N(48) of the functional groups –OCH3, −C = O, and −NH correspondingly are present with subsequent amino acids which are Arg 79, Arg 400, Ser 72, Trp 67, and Lys 413 (Fig. 8b).

On the other hand, the ligand DKC-20 forms a hydrogen bond with Erα + via interactions between residues Leu 346 and Glu 353, which interact with the hydroxyl group’s atom number O(21), and then Thr 347 interacts with the carbonyl group’s atom number O(6) (Fig. 7c). In the case of DKC-20 H-bonding interaction with aromatase, Pro 481, His 480 with interacting atom number O(42), Glu 483, Arg 192 with interacting atom number O(43), Lys 243, Tyr 249 with interacting atom O(24), the ligand’s lone common functional group, “ − OH,” is responsible for hydrogen bonding (Fig. 8c). In addition, the steric interactions of this ligand with both the proteins have been illustrated in Figs. 9c and 10c, in which involving amino acids are Leu 349, Ala 350, Leu 346, Met 388, Met 421, Met 343, His 524, and Trp 383for Erα + interaction with DKC-20, while Lys 230, Tyr 244, Asp 222, Gln 218, Phe 221, Glu 483, Pro 481, and His 480 for aromatase interaction. Moreover, the steric interaction in DKC-21 ligand with Erα + and aromatase is depicted in Figs. 9d and 10d; the interacting amino acids are Asp 351, Gly 420, Leu 525, Met 421, Met 343, Ala 350 and Glu 218, Glu 483, Asp 222, Ile 474, Glu 225, Ala 226, and Met 68, respectively. Apart from this, H-bonding of DKC-21 with Erα + can be seen in Fig. 7d, in which ylmethylphenyl group of the ligand interacts with His 524 amino acid with the interacting atom number O(33), and then Cys 550 and Met 528 interact with –OH group of the ligand with interacting atom number O(22). While, in the case of DKC-21, the H-bonding interaction with aromatase involves amino acids (Lys 230, Tyr 244) binds with atom O(33) of the ligand, and amino acid (Glu 218) binds with atom O(6) of the ylmethylphenyl group, the amino acid (Leu 230 and Trp 67) binds with the oxygen atom of O(38) number of the hydroxyl group. Similarly, amino acids (Arg 192 and Glu 483) binds with the oxygen atom [O(22)] of the –OH group.

After the comprehensive discussion of the interactions of DKC derivatives, it was observed that DKC-10 ligand exhibits the highest MolDock score (− 204.461 kcal/mol with Erα + and − 201.613 kcal/mol with aromatase) against the breast cancer. This is due to the strong H-bond and steric interactions, as well as the shorter interacting distance. Additionally, other supporting data for hydrogen bonding and steric interaction, such as interacting distance, interaction energy, strength, and the H bonding donor atom between the ligand and protein, are well represented in Tables 6, 7, 8, and 9.

In this study, we also did a comparison analysis (Tables 10 and 11), in which we evaluated the drugs that are presently used to treat breast cancer. First, the interactions of tamoxifen (antagonist type drug) has been evaluated on Erα+ (Fig. 7e). The results received through these interactions are very poor in terms of MolDock score, H-bonding, and steric interaction in comparison with DKC derivatives. On the other hand, exemestane, anastrazole, and, letrozole have been taken for study against p450 enzyme (Fig. 8e-g), since these medications block aromatase, but, here also inadequate MolDock score, hydrogen bonding, and steric interactions received. Therefore, it is possible to argue that DKC derivatives, might be superior therapeutic agents for treating breast cancer.

Table 10 Protein–ligand binding energies of known medicines and four DKC ligands were compared using MVD with two distinct PDB IDs
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Table 11 Parameters of ADME and drug-likeness of DKC-1 and its 3 derivatives
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Furthermore, in the ADME/pharmacokinetics predictions profile, the foremost step of oral bioavailability of the drug can be determined by the drug aqueous solubility as well as intestinal permeability [56]. The results revealed that DKC-1 and its 3 derivatives have high gastrointestinal absorption, and then DKC-10 and DKC-21 possess P-glycoprotein, whereas DKC-1 and DKC-21 possess barrier to P-glycoprotein. Further, DKC-10, DKC-20, and DKC-21 exhibit the barrier towards the blood–brain barrier (BBB) except DKC-1 (Table 11). Despite this, one common violation has been observed in DKC-10, DKC-20, and DKC-21 due to molecular weight > 500 g/mol as per the Lipinski rule. Moreover, through SWISS ADME web server, boiled egg graph was also generated which illustrates absorption of the ligands in the gastrointestinal tract and brain. This type of graph is also known as the brain or intestinal estimated permeation predictive model or Egan egg graph [58]. The boiled egg graph of the four DKC ligands has been shown in Fig. 11. Apart from this, Fig. 12 shows the bioavailability radar of the DKC-1 and its 3 derivatives based on six physicochemical properties such as lipophilicity, saturation, size, polarity, solubility, and flexibility [58]. Through radar images, it can be observed that DKC-1 and its 3 derivatives are expected to be orally bioavailable (less toxic and good absorption), less polarity, and low flexibility.

Fig. 11
figure 11

Bioavailability radar of the lead molecules DKC derivatives

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Fig. 12
figure 12

Boiled egg graph of the most lead ligands DKC derivatives

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Conclusion

Overall, it can be concluded that breast cancer in women is a serious global concern at the moment, as it ranks first on the list of cancers when compared to other malignancies. As a result, extensive research is being conducted to determine the most effective inhibitors for the treatment of breast cancer, particularly positive breast cancer. In this work, we discovered DKC derivatives' potential against breast cancer in both inhibitory pathways, such as an antagonist to Erα+ and an inhibitor of the p450 cytochrome enzyme. The molecular docking study of 25 deketene curcumin derivatives have been conducted in this work against two proteins Erα + (PDB: 3ERT) and aromatase (PDB: 3S79), also carried out the comparative study with drugs that are already used to treat the breast cancer. From this study, three DKC derivatives DKC-10, DKC-20, and DKC-21 are discovered with the lowest binding affinity by interaction with Erα+ and aromatase (−204.461 kcal/mol, −177.279 kcal/mol, −161.958 kcal/mol, and −201.613, −131.397, −123.724, respectively). However, other parameters of these derivatives such as H-bonding and steric interactions are more favorable for the inhibition of Erα + (positive type of breast cancer host) in comparison with aromatase. In addition to this, docking study of existing drugs with breast cancer reveals poor outcomes in terms of MolDock score, H-bonding, and steric interactions in contrast to DKC derivatives. Besides this, satisfactory results were received in the ADME/pharmacokinetics study of DKC derivatives. Therefore, it can be said that DKC-10, DKC-20, and DKC-21 are the leading candidate to treat a positive type of breast cancer. Apart from this, in the future, these candidates should be undergone the process of pre-clinical trials such as in vivo and in vitro studies against breast cancer.