Molecular docking study and molecular dynamics simulation of quinazoline derivatives targeting p53 and EGFR proteins as anti gastric cancer

1. INTRODUCTION

Gastric cancer is one of the most common and fatal cancers worldwide. According to GLOBOCAN 2022 data, gastric cancer ranks as the third leading cause of cancer-related mortality [1]. Gastric cancer arises as a consequence of somatic gene mutations, with the most frequently detected mutations occurring in the CDH1, TP53 [2], and EGFR genes [3]. The tumor suppressor p53 (TP53), which encodes the p53 protein, regulates the expression of target genes involved in cell cycle arrest, apoptosis, DNA repair, and other processes [4]. Mutations in TP53 can disrupt its antitumor activity and confer oncogenic properties to the resulting p53 protein [5].

Epidermal growth factor receptor (EGFR) is a tyrosine kinase involved in cell proliferation, division, and mitosis, as well as in cancer development [6]. Mutations in EGFR lead to abnormal receptor activity, resulting in reduced apoptotic function and enhanced cancer cell proliferation. EGFR is one of the receptor targets of tyrosine kinase inhibitors (TKIs) [7].

Building on these therapeutic targets, previous studies have explored quinazoline derivatives as modulators of p53 and EGFR pathways. Compound Q ((E)-2-(4-nitrostyryl)-4-(4-aminobutyl)aminoquinazoline), synthesized by Wei et al. [8], belongs to the 2-styryl-4-aminoquinazoline class derived from the CP-31398 analog and regulates the p53 pathway. In their study, a series of derivatives was evaluated through in vitro and in vivo assays. Among these, Q showed the highest cytotoxicity against MGC-803 and T24 cell lines and was therefore selected for further mechanistic studies in MGC-803 cells. In vivo xenograft evaluation also demonstrated superior therapeutic efficacy at lower doses than CP-31398, confirming its anticancer potential [8]. However, Wei et al. [8] did not characterize the molecular interactions of Q with the p53 protein, leaving its mechanism insufficiently understood. Moreover, given its quinazoline-based TKI structure, Q may also interact with EGFR [9,10]. In silico drug discovery, using structure-activity relationship analysis to understand protein–ligand interactions, is important because it directly targets disease-related proteins [11].

Therefore, the present study focuses on investigating the molecular interactions of quinazoline derivatives, particularly compound Q, with p53 and EGFR using computational approaches. Protein–ligand interactions were analyzed using molecular docking, molecular dynamics (MD) simulations, and pharmacokinetic analysis via ADMET [12]. These computational approaches allow rapid and accurate evaluation of drug–protein interactions, stability, and pharmacokinetic properties [13]. This study specifically analyzed the interactions of quinazoline derivatives with p53 and EGFR proteins as gastric cancer targets, using the crystal structures of p53 (PDB ID: 5O1H) and EGFR (PDB ID: 4HJO), with key residues Val147, Pro151, Pro222, and Pro223 for p53 [14–16], as well as Asp831 and Met769 for EGFR [17,18], playing roles in the interactions.

Structural modifications of quinazoline derivatives were performed by introducing electron-donating groups (NH₂ and OCH₃) and electron-withdrawing groups (NO₂, CN, and CF₃) on the phenyl ring to enhance antiproliferative activity [8]. Specifically, substitutions on the nitrostyryl phenyl ring were carried out to strengthen interactions with the receptor’s active site. These modifications are expected to increase both the number and strength of interactions with key amino acid residues, thereby lowering binding energy and enhancing complex stability.

Electron-withdrawing substituents on the aromatic ring increase the positive charge of protons at the ring edge, thereby enhancing electrostatic contacts with the negative π-face of the receptor’s aromatic ring [19]. In π–π stacking interactions, electron-withdrawing substituents reduce the negative charge on the π-face of the ligand, decreasing electrostatic repulsion with the receptor’s π-face and stabilizing the ligand–receptor complex. Conversely, electron-donating substituents tend to destabilize the ligand complex [20]. Compounds exhibiting the best results were further evaluated through pharmacokinetic analysis as potential gastric cancer candidates.

2. MATERIALS AND METHODS

3. RESULTS AND DISCUSSION

3.1. Molecular docking

The molecular docking method was validated by redocking the standard ligands to p53 and EGFR 100 times. Validation criteria were based on RMSD values <2 Å [26], indicating good agreement between the simulated ligand conformation and the native ligand conformation in the receptor.

The validation results showed RMSD values of 1.8292 Å for p53 and 1.5265 Å for EGFR, with corresponding binding energies of −6.3550 kcal/mol and −8.6640 kcal/mol. Interactions of the standard ligand with p53 were primarily hydrophobic, involving key catalytic residues, whereas the standard ligand formed conventional hydrogen bonds with Met769, a key catalytic residue of EGFR. Ligands that interact with these key residues are likely to function as p53 activators and EGFR inhibitors [27].

Molecular docking results showed that compound Q exhibited binding energies comparable to the standard ligands for both p53 and EGFR, with values of −6.5500 kcal/mol for p53 and −8.6190 kcal/mol for EGFR. These binding energies indicate spontaneous and stable bindings. Ligand Q formed a conventional hydrogen bond with the key catalytic residue Val147 of p53, while its binding with EGFR involved the primary catalytic residue Asp831 (Fig. 2). Lower binding energies reflect stronger and more stable ligand–receptor interactions.

Figure 1. The chemical structures of (E)-2-(4-nitrostyryl)-4-(4-aminobutyl)-aminoquinazoline. [Click here to view]

Figure 2. 2D views of the binding site interactions of (E)-2-(4-nitrostyryl)-4-(4-aminobutyl)-aminoquinazoline with p53 (a) and EGFR (b). [Click here to view]

Docking analysis indicated that the introduction of electron-donating and electron-withdrawing groups on the styryl phenyl ring of quinazoline derivatives reduced the binding energy. Modifications of compound Q were performed to enhance binding with key catalytic residues and to establish additional contacts with other residues, thereby increasing complex stability compared to the parent compound. Electron-withdrawing groups particularly improved compound stability and activity, as evidenced by more negative binding energies compared to electron-donating groups [28].

Derivatives Q-2, Q-4, and Q-9 exhibited bindings with the key residue Val147 (Table 1, Fig. 3) and showed the lowest binding energies (Table 1). The addition of electron-withdrawing groups shortened the hydrogen bond distance with Val147, strengthening the binding. Conversely, electron-donating groups increased ligand basicity through conjugation effects, enhancing electron density on the butylamino group, which acts as an effective hydrogen donor. This increased electrostatic attraction with electron-rich acceptor atoms, such as the carbonyl oxygen of Val147, further stabilizes the binding. The best ligands (Q-2, Q-4, and Q-9) were also docked with EGFR. They formed conventional hydrogen bonds with the key residue Asp831 (Fig. 4) and additional interactions with other residues. Complete ligand–receptor docking data are presented in Table 2.

Figure 3. 2D views of the binding site interactions of best docked ligands, Q-2 (a), Q-4 (b), and Q-9 (c) with receptor p53. [Click here to view]

Figure 4. 2D views of the binding site interactions of ligand Q-2 (a), Q-4 (b), and Q-9 (c) with receptor EGFR. [Click here to view]

Table 1. Binding energy value and contacting receptor residue from docking result in p53 receptor.

Code	Substituent	Binding energy (kcal/mol)	Interactions
			Hydrogen bonds	Additional interactions
Redocking		−6.3550		Pro151, Pro222, Pro 223, Val 147, Leu145, Cys220, Glu221, Asp228
Q	non-substituted	−6.5500	Val 147, Asp228, Cys220	Pro 223
Q-1	3-NO2	−7.8610	Val 147, Asp228, Gly154	Pro151, Pro222, Pro 223, Leu145, Cys220, Glu221, Pro153, Thr150
Q-2	2-NO2	−8.3120	Val 147, Gly154, Thr155	Pro151, Pro222, Pro 223, Leu145, Cys220, Glu221, Thr150, Pro153
Q-3	2-CN	−6.7460	Val 147, Asp228, Thr150	Pro 223, Val 147, Cys229, Trp146
Q-4	3-CN	−8.1330	Val 147, Gly154	Pro151, Pro222, Pro 223, Leu145, Cys220, Glu221, Thr150, Pro153
Q-5	3- NH2	−7.0280	Val 147, Asp228, Asp148	Pro 223, Val 147, Leu145, Cys220, Glu221, Pro153, Thr150
Q-6	3-OCH3	−7.8460	Val 147, Asp228, Gly154	Pro151, Arg110, Cys220
Q-7	2-OCH3	−6.4940	Val 147, Asp228, Cys220	Pro151, Pro222, Pro 223, Val 147, Cys220, Asp228
Q-8	2-NH2	−6.9940	Val 147, Asp228, Leu145, Glu221	Pro 223, Val 147, Leu145, Glu221, Cys220, Asp228
Q-9	3-CF3	−8.1240	Val 147, Asp228, Gly154	Pro151, Pro222, Pro 223, Pro153, Thr150, Leu145, Glu221, Cys220,

Table 2. Binding energy value and contacting receptor residue from docking result in EGFR receptor.

Code	Substituent	Energy binding (kcal/mol)	Interaction
			Hydrogen bonds	Additional interactions
Redocking		−8.6640	Cys773, Met769	Pro770, Gly772, Phe771, Val 702, Lys721, Leu764, Ala719, Gln767, Leu820, Leu694
Q-2	2-NO2	−8.7090	Asp831, Cys773	Gly772, Thr830, Leu694, Phe771, Ala719, Lys721, Val70
Q-4	3-CN	−8.7800	Asp831, Cys751, Met742, Ser696	Ala719, Lys721, Val702, Cys773, Thr830, Leu764, Leu753
Q-9	3CF3	−9.1060	Asp831, Cys773, Lys721	Thr830, Ala719, Val702, Leu694, Phe771, Gly772

Regarding substituent effects, the addition of NO₂ and CN groups did not significantly reduce binding energies, whereas the introduction of CF₃ effectively lowered the binding energy. This is attributed to the strong hydrophobic character of CF₃ [29], allowing efficient binding with hydrophobic sites on EGFR.

3.2. Molecular dynamic

RMSD was used to evaluate deviations of ligand conformations from their initial positions during the simulations. Low RMSD values and slopes approaching zero indicate ligand stability within the complex. All ligands Q-2, Q-4, and Q-9 exhibited RMSD values below 5 Å (Table 3) with slopes near zero (Fig. 5). Among them, Q-2 showed the lowest RMSD and a slope closest to zero in complexes with both p53 and EGFR, indicating the highest stability. Higher RMSD values suggest that the ligand moves away from key catalytic residues, reducing hydrogen bond interactions. Ligands forming multiple hydrogen bonds during the simulation tend to maintain conformations similar to their initial positions.

Figure 5. Plot of RMSD versus time (ns) and intercept lines of Q-2, Q-4, and Q-9 in receptor p53 (a) and EGFR (b) complexes are shown in blue, orange, and gray respectively from 0–100 ns time scale. [Click here to view]

Table 3. Average RMSD, Rg, SASA, and MM/PBSA binding energies for Q-2, Q-4, and Q-9 in complexes with p53 and EGFR.

Ligand	Score
	RMSD (Å)		Rg (Å)		SASA (Ų)		MM/PBSA (kJ/mol)
	p53	EGFR	p53	EGFR	p53	EGFR	p53	EGFR
Q-2	2.527	3.586	17.138	19.625	14,343.25	14,343.25	−227.752	−184.638
Q-4	4.383	2.831	17.316	19.716	14,423.22	14,423.22	−224.927	−86.013
Q-9	3.318	2.781	17.462	19.567	13,866.91	13,866.91	−237.994	−159.554

Conventional hydrogen bonds, strong dipole–dipole interactions (±10–40 kJ/mol) [30], are critical for ligand–protein complex stability. In MD simulations with p53, Q-2 maintained stable hydrogen bonding with the key residue Val147 from 0 to 97.9 ns. Q-4 interacted with Val147 only up to 8.2 ns, while Q-9 was unstable, forming bindings with another residue (Asp228) due to RMSD fluctuations. Thus, Q-2 was the most stable ligand based on hydrogen bonding with Val147. For EGFR, Q-2 consistently interacted with Asp831 throughout the 0–100 ns simulation, indicating the highest stability. Q-4 maintained bindings with Asp831 up to 81 ns, whereas Q-9 interacted with Asp831 only until 4.5 ns before switching to stable bindings with Met769, consistent with the hydrophobic nature of Met769 complementing the CF₃ group of Q-9.

Complex stability was further evaluated using RMSF. High RMSF values indicate increased mobility and instability of receptor residues [31]. RMSF dihitung terhadap masing-masing interaksi dengan residu asam aminonya. RMSF plots are shown in Figure 6. RMSF analysis indicated that the key catalytic residues of p53 (Val147, Pro151, Pro222, and Pro223) and EGFR (Met769 and Asp831) exhibited low fluctuations, suggestingstability of receptor active sites during the simulation. The highest fluctuations in p53 were observed at residues 117–120 (loop L1) and residue 291 (C-terminal), while EGFR showed peak fluctuations at residues 679 (juxtamembrane) and 960 (C-terminal). These peaks correspond to residues in flexible regions and therefore do not affect complex stability, as they are located outside the active site. Ligands with the lowest RMSF values are considered the most stable; based on these results, Q-2 is the most stable ligand for p53, and Q-9 is the most stable ligand for EGFR.

Figure 6. RMSF plots of Q-2, Q-4, and Q-9 in p53 (a) and EGFR (b) complexes (blue, orange, and gray) over 0–100 ns. The red line highlights key active-site residues (p53: Val147, Pro155, Pro222, Pro223; EGFR: Met769, Asp831), and yellow circles indicate regions of highest fluctuation (p53: 117–120, 291; EGFR: 679, 960). [Click here to view]

The radius of gyration (Rg) reflects the structural volume and tertiary organization of proteins, serving as a key indicator of their structural compactness. An increase in Rg signifies loosening or reduced compactness of amino acid residues within the protein. Ligand–protein complexes maintained average Rg values with slopes near zero (Fig. 7, Table 3), indicating no significant structural changes. Q-2 (p53) and Q-9 (EGFR) exhibited the lowest Rg and slopes closest to zero, suggesting the highest stability.

Figure 7. Plot of Rg versus time (ns) of Q-2, Q-4, and Q-9 in receptor p53 (a) and EGFR (b) complexes are shown in blue, orange, and gray respectively from 0–100 ns time scale. [Click here to view]

SASA, which is defined as the surface area of a protein that is accessible to surrounding water molecules and is crucial for protein binding. This parameter also serves as a fundamental indicator for describing protein folding. Fluctuations or increases in SASA values indicate an expansion of the protein’s volume. SASA analyses (Fig. 8, Table 3) showed that Q-9 had the most stable profile for both receptors. While Q-2 and Q-4 showed slightly higher slopes in p53 and EGFR due to loop flexibility, the core structures remained stable, consistent with Rg results.

Figure 8. Plot of SASA versus time (ns) of Q-2, Q-4, and Q-9 in receptor p53 (a) and EGFR (b) complexes are shown in blue, orange, and gray respectively from 0–100 ns time scale. [Click here to view]

Negative MM/PBSA binding energy values indicate that the ligand-receptor complexes form spontaneously and stably. MM/PBSA binding energy analysis (Table 3) revealed values with slopes approaching zero (Fig. 9). For p53, Q-4 exhibited the most stable binding, with a strong binding energy and a slope closest to zero. Although Q-2 had a lower binding energy, its stability was slightly below that of Q-4. Q-9 showed the highest binding energy but with a larger slope; nonetheless, both Q-2 and Q-9 are predicted to form sufficiently stable bindings with p53. In EGFR complexes, Q-9 was the most stable ligand with the strongest bindings. While Q-2 had a lower binding energy and Q-4 higher one, both maintained stable bindings, as indicated by slopes near zero.

Figure 9. Plot of MM/PBSA versus time (ns) of Q-2, Q-4, and Q-9 in receptor p53 (a) and EGFR (b) complexes are shown in blue, orange, and gray respectively from 0–100 ns time scale. [Click here to view]

Table 4. ADMET prediction of Q-2, Q-4, and Q-9.

Parameter	Standard	Q-2	Q-4	Q-9
Lipinski’s rule of five
Molecule mass (g/mol)	100–600	408.15	3888.160	431.16
H-bond acceptor	0–12	10	8	7
H-bond donor	0–7	3	3	3
LogP	<5	3.096	2.791	4.041
Absorption
Caco-2 permeability (Log unit)	≥−5.15	−5.211	−5.223	−5.236
HIA (%)		---	---	---
Distribution
VD (L/kg)	0.04–20	0.696	0.336	1.451
BBB Penetration		-	--	--
Metabolism
CYP1A2 inhibitor		+++	++	+++
CYP1A2 substrate		--	--	+
CYP2C19 inhibitor		+++	+	+++
CYP2C19 substrate		---	---	---
CYP2C9 inhibitor		++	++	+
CYP2C9 substrate		++	+	+
CYP2D6 inhibitor		++	+	++
CYP2D6 substrate		++	++	++
CYP3A4 inhibitor		++	++	++
CYP3A4 substrate		--	--	--
Excretion
Clearance (CL) (ml/min/kg)	High: >15; Moderate: 5–15; Low: <5	3.113	5.193	4.698
Toxicity
Rat oral acute toxicity		--	--	---
Human hepatotoxicity (H-HT)		++	+++	++
Carcinogenicity		-	--	-

Overall, MD analysis indicates that Q-2 forms the most stable complex with p53 through consistent bindings with key catalytic residues, while Q-9 shows the greatest stability with EGFR. Q-4 failed to maintain stable bindings with p53 catalytic residues. Although Q-9 is stable with p53, it does not interact with key residues, and Q-2 and Q-4 interact with EGFR catalytic residues but are less stable than Q-9.

3.3. ADMET predictions

All three compounds met the drug-likeness criteria according to Lipinski’s rules, indicating potential as suitable drug candidates. Although membrane permeability (Caco-2) was slightly lower, intestinal absorption (HIA) was high, suggesting good oral absorption. VD values were within standard ranges, indicating uniform plasma distribution. Regarding blood–brain barrier (BBB) permeability, Q-2 and Q-9 are safer compared to Q-4.

All compounds acted as CYP450 inhibitors and were non-substrates for certain All compounds acted as CYP450 inhibitors and were non-substrates for certain subfamilies, but they remain potential substrates for CYP2C9 and CYP2D6, with Q-9 also a substrate for CYP1A2, suggesting possible accumulation in the body. In terms of excretion, Q-2 and Q-9 showed longer elimination times (CL), while Q-4 was faster. None of the compounds was orally toxic, although there was some potential hepatotoxicity (higher for Q-4), which remains acceptable. All compounds were non-carcinogenic. Overall ADMET results are summarized in Table 4.

4. CONCLUSION

Modification of quinazoline derivatives can target p53 and EGFR, particularly compounds Q-2, Q-4, and Q-9. These three compounds demonstrated the strongest potential targeting p53 and have potential targeting EGFR. The stability of the quinazoline derivatives was evaluated through 100 ns MD against p53 and EGFR. Q-2 was the most stable within the p53, while Q-9 was the most stable within the EGFR. ADMET analysis of Q-2, Q-4, and Q-9 revealed favorable pharmacokinetic properties, with minor limitations observed in H-HT, which are still considered acceptable for potential anti-gastric cancer candidates.

5. ACKNOWLEDGMENTS

The authors gratefully acknowledge the research facilities provided by the Austrian-Indonesian Center for Computational Chemistry, Department of Chemistry, Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada.

6. AUTHOR CONTRIBUTIONS

All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work. All the authors are eligible to be author as per the International Committee of Medical Journal Editors (ICMJE) requirements/guidelines.

7. FINANCIAL SUPPORT

There is no funding to report.

8. CONFLICTS OF INTEREST

The authors report no financial or any other conflicts of interest in this work.

9. ETHICAL APPROVALS

This study does not involve experiments on animals or human subjects.

10. DATA AVAILABILITY

All data generated and analyzed are included in this research article.

11. PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of the publisher, the editors and the reviewers. This journal remains neutral with regard to jurisdictional claims in published institutional affiliation.

12. USE OF ARTIFICIAL INTELLIGENCE (AI)-ASSISTED TECHNOLOGY

The authors declare that they have not used artificial intelligence (AI)-tools for writing and editing of the manuscript, and no images were manipulated using AI.

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