Aqueous-Phase Detection of Explosive Residues: Sulfonyl Chloride-Appended Oxacalix[4]arene for 2,4-DNT Monitoring

Aqueous-Phase Detection of Explosive Residues: Sulfonyl Chloride-Appended Oxacalix[4]arene for 2,4-DNT Monitoring | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Aqueous-Phase Detection of Explosive Residues: Sulfonyl Chloride-Appended Oxacalix[4]arene for 2,4-DNT Monitoring Himali Upadhyay, Uma Harikrishnan, Devanshi Bhatt, Kapil Kumar, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6881765/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We designed and synthesized a novel sulfonyl chloride-substituted oxacalixarene (DCOC) as a selective probe for the detection of 2,4- DNT. Its structure was characterized with the use of 1H-NMR, 13C-NMR, and FT-IR analysis techniques. The binding property of DCOC to various NACs, such as 1,3-DNB, 2,3-DNT, MNA, 2,6-DNT, 4-NP, 4-NT,4-NP, and PA, revealed that the sensor binds with remarkable selectivity to 2,4- DNT. The binding constant reveals lower 2,4- DNT detection limits. The HOMO-LUMO energy calculation, chemical reactivity and geometric properties of reveal the stable confirmation of the DCOC through using the DFT approach. Additionally, a docking study between DCOC and PARP-1 protein shows a strong molecular interaction with a binding energy of -2.1 kcal/mol. The stability was checked using an MD simulation that ran for 500ns. The cytotoxic potential of the synthesized compound was evaluated on the MCF-7 breast cancer cell line using the MTT assay. Oxacalixarene 2 4-Dinitrotoluene (2 4-DNT) Explosives Fluoroscence studies Density functional theory (DFT) Cytotoxicity studies Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction In recent times, one of the most crucial safety and security concerns has emerged: the identification of explosives and other illicit substances that can be utilized to manufacture explosives. As a result, there have been several terrorist attacks, fatalities, and property damages. Explosive detection is consequently crucial in today's world.[ 1 ]. Explosive detection is essential for forensic investigations such as arson[ 2 ] or post-blast residue findings[ 3 ], mine fields, military bases, remediation sites, urban transportation areas[ 4 ] and workplace monitoring [ 5 ]. A primary aspect of homeland security is the detection of explosives. Military engagements and terrorist assaults predominantly rely on explosive substances. Consequently, the identification of these explosive substances is essential for the preservation of all living creatures and the environment. Terrorists utilize explosives in enormous quantities due to their convenience in production and deployment[ 6 ] Due to the following, it is likely that 2,4-dinitrotoluene (DNT) will be found when looking for explosives:(i) 1,3,5-trinitrotoluene (TNT) is the main ingredient in explosives, but production-grade TNT has a lot of DNT in it [ 7 ].(ii) DNT is a by-product of TNT's decomposition. (iii) At 20 degrees Celsius, the saturation concentration of DNT in air is approximately 25 times that of TNT (148 parts per billion (ppb) versus 6 ppb) [ 7 ]. Using the basic toluene nitration and dinitration reactions, nitrotoluene and dinitrotoluene can be synthesised [ 8 ]. There are opportunities for 4-NT and 2,6- DNT to easily permeate the soil and harm groundwater or nearby streams. Epidemiological data indicate that nitro-aromatic compounds (including nitrobenzene, dinitrotoluenes, mono and di-nitrophenols) are potent carcinogens, and are therefore referred to as priority pollutants [ 9 ]. Electrochemical [ 7 ], chromatographic techniques [ 10 ], optical noses [ 11 ], fluorescence [ 12 ][ 13 ], X-ray diffraction [ 9 ], and surface acoustic wave (SAW) sensors [ 10 , 11 ] are among the techniques investigated for detecting explosive compounds. The development of a sensor material that provides specific and sensitive results for analysing the chemical signatures of explosives in the presence of other analytes is currently the subject of intense research. It has been reported that calixarene-based chemo sensors exhibit a unique response to any analyte of interest based on the optimisation of structural and geometrical parameters [ 14 ][ 15 ][ 16 ]. Calixarenes represent third-generation supramolecular frameworks[ 17 ]. Calixarenes are defined by a central annulus encircled by a broad upper rim and a lower rim, exhibiting a high degree of preorganization and conformational preferences. Calix[n]arenes are third-generation supramolecules characterized by a hollow, hydrophobic cavity capable of encapsulating various guest analytes, including cations, anions, and neutral molecules [ 18 ][ 19 ][ 20 ]. Oxacalixarenes (OC) are notable heteracalixarene analogues containing oxygen as the bridge atom [ 21 ]. These macrocycles are readily available because they can be synthesised in a single step at room temperature by high yield nucleophilic aromatic substitution (SNAr) reactions [ 22 ]. 2. Experimental section 2.1 Chemical reagents and instruments All the chemicals including 1,5-difluoro-2,4-dinitrobenzene, Pyrogallol and NACs were purchased from Sigma-Aldrich. The solvents used for synthesis and analysis were purchased from Finar Chemicals and utilised without further purification. Thin-layer chromatography (TLC) with E-Merck silica gel 60 F254 precoated plates, visualised with UV-light (254 nm) or I 2 vapour, was used to monitor the progression of the reaction. 2.2 Synthesis of DCOC: At room temperature, the reaction is conducted by adding Potassium Carbonate to a solution of Oxacalixarene (0.5g, 0.86mmol) in dry acetone (20 ml)[ 23 ]. After agitating the reaction mixture for 1 h, biphenyl 4 sulfonyl chloride (0.4g, 0.17 mmol) is added and the mixture is swirled vigorously for 24 h. The solvent is evaporated in vacuo. The crude obtained is subjected to the column chromatography using silica gel with EtOA-C: Hexane (3:7) as an eluent. The fractions were separated, dried and recrystallized in alcohol to give DCOC (0.35 g) (Fig. 2 ). 2.3 Spectroscopic measurement With the aid of spectrofluorometric measurements, the sensitivity of the instrument, DPOC, towards NACs was investigated. The 1 mM stock solution of the receptor DCOC was initially prepared with Acetonitrile as the solvent. In order to conduct spectrofluorometric experiments, the stock solution was further diluted to 100 M. In the same manner, acetonitrile stock solutions (1 mM) of explosives such as 1,3- DNB, 2,3- DNT, 2,4- DNT, 2,6- DNT, 4-NP, 4- NT, and PA were prepared. In addition, the receptor was excited at 330 nm, and the addition of the aforementioned analytes resulted in a change in emission maxima. The temperature was maintained at (298 ± 2) K throughout the duration of the investigation. Excitation and emission slit widths were set to 5 nm for all measurements. 2.4 Computational methodology 2.4.1 Density Functional Theory calculations The Jaguar program utilized for efficient quantum chemical computational for electronic structure prediction of medium and large molecular systems. we mostly focused on improving Jaguar's performance and precision in DFT computations, employing the B3LYP-D3 functional of the DCOC with the 6-31g(d,p) basis set of the Schrodinger ( Material Science Suite14.0) was followed in the simulations. Exploring the theoretical aspects of the highest occupied molecular orbital energy (E HOMO ) and the lowest unoccupied molecular orbital energy (E LUMO ), including the energy gap (ΔE) between LUMO and HOMO. The structure has been optimized, showing no negative frequencies. The DCOC compound exhibits distinct conformations. Conformational analysis examines the various energy states connected to the distinct conformations of a molecule. We obtained 100 conformations of this molecule, from which we selected the optimal geometry for further optimization. The conformational exploration is a crucial phase in computational chemistry for investigating the diverse spatial configurations of a molecule. The main aim of a conformational search is to determine the most stable structure or the global minimum energy conformation of a molecule. Different molecular conformations have different potential energies. To accurately predict molecular properties to find the conformation with the lowest energy. This procedure generally utilizes molecular mechanics or semi-empirical approaches, contingent upon the systems size and complexity. 2.4.2. Molecular Docking The Glide program, part of the Schrödinger suite and enhanced with Extra Precision (XP) capabilities, was used to dock the eight calix[ 4 ]pyrroles into the crystal structure of the catalytic domain of PARP1 in conjunction with Olaparib (PDB ID: 7KK4). XP docking, which is more thorough and discriminating than the standard precision (SP) approach, requires a longer duration due to its complex operation. This is designed for ligands that have attained high scores in SP docking, using a comprehensive grading system that requires improved congruence between ligand and receptor geometries. This stringent process aims to eliminate false positives often linked to SP by imposing penalties on ligands that poorly conform to the designated receptor structure. DCOC was selected as a ligand for the molecular docking analysis. LigPrep from Schrödinger software (Schrödinger 2024-3) was used for ligand generation, whereas the Protein Preparation Wizard was applied for protein preparation. This encompasses the addition of hydrogen, the removal of water molecules, and energy reduction with the Optimized Potentials for Liquid Simulations (OPLS) 2005 forcefield. The active site of the receptor was identified based on the known binding affinity of 09L (4-(3-{[4-(cyclopropylcarbonyl)piperazin-1-yl]carbonyl}-4- fluorobenzyl)phthalazin-1(2H)-one). The active site was used for docking PARP-1 and DCOC. Therefore, it is advisable to dock against many receptor conformations to ascertain ligands with appropriate binding energies for molecular dynamics modeling studies. 2.4.3 Molecular dynamics (MD) simulation A Desmond package [ 24 ][ 25 ][ 26 ][ 27 ][ 28 ] molecular dynamics simulation was conducted for 100 ns on the PARP1-DCOC complex [ 29 ]. The complex was generated with a protein preparation wizard that aids in complex relaxation. The procedure included the incorporation of hydrogens, removal of water, allocation of bond order, and finalization of missing side chains and loops, while optimizing hydrogen-bond assignments at pH 7.0 and monitoring water orientations. Complexes underwent energy reduction with the OPLS-2005 force field prior to molecular dynamics simulations. The TIP3P solvent model was then used to construct the system [ 30 ]. A 10 Å buffer zone was established around the ligand-protein complex inside an orthorhombic simulation box appropriate for the protein-ligand combination. The subsequent phase included using OPLS-2005 force fields to incorporate Na + ions and a salt concentration of 0.15 M Na + and Cl- counter ions into a simulation box for neutralization, therefore simulating physiological conditions and background salt levels. Following the successful construction of the system, molecular dynamics simulation was conducted using an NPT (constant number of particles, pressure, and temperature) ensemble at 300 K and 1.013 bar of atomic pressure [ 31 ]. The default surface tension was determined using the Smooth Particle Mesh Ewald (PME) approach [ 32 ], which concurrently calculates the long-range electrostatic interaction potential energies employing the RESPA integrator [ 33 ]. Trajectories for 100 frames were recorded throughout the 100 ns molecular dynamics simulation. Each trajectory was analyzed using the Simulation Interaction Diagram wizard, which computes trajectories for Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) upon completion of the simulation [ 34 ]. Furthermore, the contact patterns between protein and ligand, together with the timeframe of these specific interactions, are assessed in the context of a 100 ns simulation for key interacting amino acid residues. The molecular dynamics simulation method was used to validate the docking orientations and interactions anticipated between both ligands and the PARP-1 protein throughout the docking process. 3 Results and Discussion 3.1 Synthesis and Characterization The desired receptor DCOC was synthesised through the cyclo-condensation of 1,5-difluoro-2,4-dinitrobenzene and bis-sulfonyl chloride in dry acetone. After completion of the reaction, the crude was purified with column chromatography using 10% ethyl acetate/hexane as eluent (Rf ~ 0.27). The receptor DCOC was obtained as a yellow solid with 75% yield, m.p. >300 ºC. Elemental analysis of C 48 H 28 N 4 O 18 S 2 : 1H NMR (400 MHz, DMSO) δ 8.74 (s, 1H), 7.71 (s, 1H), 7.69 (d, J = 2.0 Hz, 1H), 7.65 (s, 1H), 7.61 (s, 1H), 7.58 (dd, J = 6.9, 2.1 Hz, 1H), 7.50–7.48 (m, 1H), 5.75 (s, 1H), 3.36 (s, 1H). 3C NMR (101 MHz, DMSO) δ 145.62 (s), 131.82 (s), 129.63 (s), 128.26 (s), 127.76 (d, J = 13.9 Hz), 123.67 (s), 40.62 (s), 40.41 (s), 40.20 (s), 39.99 (s), 39.78 (s), 39.57 (s), 39.36 (s). 3.2 Selectivity of probe In order to obtain the selectivity of the probe DCOC, towards NACs, absorption and fluorescence studies were performed in binary mixture. The receptor DCOC exhibits maximum absorption at 370 nm. Among all the NACs involved in the interaction with the receptor, only 2,4- DNT shows change in absorption maxima (Fig. 3 ). Although the absorption study briefs about the selectivity of the guest analytes but cannot provide information about the sensitivity, stoichiometry, nature of binding and mechanism involved for sensing. Therefore, emission studies were introduced to get insight about these parameters. DCOC exhibits shows strong fluorescence at an emission wavelength of 370 nm (λ ex = 370 nm). A remarkable decrease in emission intensity of receptor was observed upon addition of 2,4- DNT (Fig. 4 ). On other hand 2,4- DNT substantially binds with the probe resulting quenching in fluorescence intensity, keeping rest of the NACs unaffected. Mass analysis data supports chemical reaction and complexation between DCOC and 2,4- DNT. 3.3 Computational methodology 3.3.1 Density Functional Theory calculations The conformational search provides multiple possible molecule configurations and analyses their energy. The conformers possessing the lowest energies undergo further optimization procedures to get an appropriate molecular geometry. Density Functional Theory (DFT) or Hartree-Fock (HF) approaches, HOMO (Highest Occupied Molecular Orbital) and LUMO (Lowest Unoccupied Molecular Orbital) are essential principles in molecular orbital theory. HOMO and LUMO are referred to as FMO (frontier molecular orbitals) as they represent the highest occupied and lowest unoccupied molecular orbitals. The aim of geometry optimization is to determine the lowest energy configuration by adjusting bond lengths, bond angles, and dihedral angles. The geometry optimization value at gas phase and solvent phase are − 4215.005980 Hatree and − 4215.037751 Hatree, respectively. This is typically achieved by quantum mechanical methods. These orbitals are essential in defining the optical and electrical characteristics of molecules, including their HOMO-LUMO energy difference provides a basic measurement for chemical reactivity, with a large HOMO-LUMO gap signifying substantial stability and low chemical reactivity. DCOC compound shown HOMO, LUMO and energy band gap, were discovered to be -6.6291 eV, -2.9861 eV and − 3.643 eV, respectively. The intramolecular charge transfer interaction is indicated by the HOMO-LUMO energy gap, which corresponds to the band gap energy of bioactive chemicals (Fig. 5 ). Table 1 Calculated energy values of DCOC Ligand using b3lyp-d3/ 6-31g (d,p) method. Energy Value (eV) ∆E Global hardness (η) Global softness (S) chemical potential (µ) Electrophilicity index (ω) stabilization energy (∆E) HOMO LUMO -6.6291 -2.9861 -3.643 1.8215 0.274499 -4.8076 6.344501 -6.3445012 The global molecular descriptors, including global hardness, Softness (S), Chemical Potential, Electrophilicity, and stabilization energy the results are reported in Table 1 DCOC compound considerable energy transition, this molecule is not hard and also exhibit low toxicity due to its minimal softness value. 3.4 Cytotoxic assessment of compound using MTT assay Cytotoxicity was evaluated using the MTT assay as described by Mosmann (1983) [ 35 ]. MCF-7 cells were cultured in complete media consisting of 10% Fetal Bovine Serum (FBS) and 90% Dulbecco's Modified Eagle Medium (DMEM) at 37°C in a humidified atmosphere with 5% CO₂ (Eppendorf CellXpert). After reaching confluency, 1×10⁴ cells were seeded in each well of a 96-well plate (Tarsons). The following day, treatments with sample DCOC at specified concentrations were applied, with control wells for untreated cells and solvent-only treatments. After 24 hours of incubation, 50 µl of MTT reagent (5 mg/ml in 1x PBS) was added to each well, followed by a 3–4-hour incubation in the dark to allow for formazan crystal formation. The crystals were dissolved in 100 µl DMSO (HiMedia) for 15–20 minutes. Absorbance was measured at 570 nm using an Epoch multiplate reader (BioTek), and results were expressed as mean ± SD. IC₅₀ values were determined from the concentration-response curves. Cell viability (%) was calculated using the formula: Cell Viability (%) = Absorbance of Treated Wells/Absorbance of Untreated Wells × 100. Morphological changes were observed using an inverted microscope (Weswox) equipped with a KEOWA CE-500X camera and Capture Pro software (Fig. 6 ). The cytotoxic potential of the synthesized compound was evaluated on the MCF-7 breast cancer cell line using the MTT assay. Increasing concentrations of the compound were applied to the cells, and the results indicated a significant reduction in cell viability in a dose-dependent manner (Fig. 7). As illustrated in Graph 1, the compound demonstrated increasing cytotoxicity with higher concentrations. The IC50 value, representing the concentration at which 50% of cell viability was inhibited, was calculated to be 417.76 ± 1.77 µM. This IC50 value suggests moderate cytotoxicity, highlighting the compound's potential as a therapeutic agent. 3.5 Computational methodology for cytotoxic assessment 3.5.1 Evaluation of DCOC Docking with PARP-1 Protein The interaction between DCOC and PARP1 protein indicates that DCOC draws Q759 and Y907, leading to the formation of hydrogen bonds. Q759, E763, M890, Y896, and Y907 established hydrophobic interactions with the PARP1 protein. π-π stacking was also established using Y889, Y896, and Y907 (Figure-8). The binding energy of this interaction is calculated to be -2.1 kcal/mol using the XP docking method. 3.5.2 MD simulations investigation of the PARP1-DCOC complex Molecular dynamics (MD) simulations are essential for corroborating the outcomes of docking evaluations. These simulations provide a dependable method for validating the results acquired by molecular docking. The MD simulations will precisely determine the ligand's contact time and the exact types of interactions it engages in with the protein. Docking offers insights into the possible interaction between a ligand and a protein, whereas molecular dynamics simulations can measure the strength and duration of intermolecular contact. The molecular dynamics simulations of the PARP1-DCOC complex were performed for 1000 ns. The resultant data was further evaluated to assess the similarity in the interaction pattern between the ligand DCOC and the PARP1 protein. Figure-9 illustrates the outcomes derived from molecular dynamics simulations of the DCOC complex. The first assessment performed is the computation of Root Mean Square Deviations (RMSD). The RMSD plot has two main assessments: (i) Protein RMSD and (ii) Ligand RMSD relative to the protein, which are to be compared. The RMSD graph illustrates the protein's root- mean-square deviation (RMSD) on the left Y-axis. The root mean square deviation (RMSD) of the ligand DCOC inside the catalytic site of the PARP1-DCOC complex is shown on the Y-axis to demonstrate the stability of the binding. The RMSD range of variations seen in the figure is up to 5 Å, with these differences being less prominent among proteins of varying sizes. The RMSF evaluative metric identifies atoms and amino acid residues undergoing significant variations over the simulation period. Figure-10 illustrates the RMSF profile of the simulated PARP-1-DCOC complex, exhibiting fluctuations between 25–50 ns and 60–80 ns (Fig. 10 ). The Protein–Ligand interaction fraction plot for the PARP-1-DCOC complex (Figure-11) illustrates that both proteins engage with identical amino acids, establishing significant interactions as shown by the interaction fraction values. The interaction fraction in the plot is a numerical value derived from the conversion of the proportion of interactions. A value of 1.0 indicates that the whole of the simulation duration (100 ns) is dedicated to establishing contacts, including hydrogen bonds, hydrophobic interactions, ionic interactions, and water bridges. MD simulation studies accurately demonstrate the relationship between ligands and protein receptors, achieving a high degree of precision and scientific agreement. The increasing computational capability facilitates the use of this technology to validate ideas about ligand-protein interactions. The PARP-1-DCOC complex was established by many hydrophobic interactions (light purple) including the amino acids His 862, Tyr 889, Ile 895, Tyr 896, Tyr 907, Leu 984, and Tyr 986. In the protein chain, the amino acids Gln 759, Ala 760, Asn 767, Gly 863, Ser 864, Arh 865, Met 890, Lys 903, Asn 906, and Tyr 907 formed hydrogen bonds (shown in green). Five ionic bonds (magenta) were identified with the amino acids Glu 688, Glu 763, Arg 865, Lys 903, and Glu 988. The 2D interaction graphs of DCOC illustrate the continuity of interactions during the simulation period. The DCOC molecule exhibited five hydrophobic interactions with Gln 636 (39%) and His 198 (44%), along with two positive charges with Arg 865 (59%) and Lys 903 (47%). Furthermore, there were three pi-pi interactions with Ala 760 (58%), Tyr 896 (35%), and Tyr 889 (32%); one polar contact with His 862 (67%); and two salt bridges with Arg 865 (59%) and Lys 903 (47%) (Figure-12). The interaction of ligands, including DCOC (Figure-13), is shown by charts that demonstrate the characteristics of the ligands. The extent of a ligand's extension, indicative of its primary measure of inactivity, is measured by the radius of gyration (rGyr). The evaluation of the 690 to 720 range of atomic surface configuration is shown by the molecular surface area (MolSA). This number is directly proportional to the surface area of the Van der Waals surface. Solvent Accessible Surface Area (SASA) denotes the portion of a molecule's surface that is reachable by water molecules (300 to 420). Conversely, Polar Surface Area (PSA) is the segment of a molecule's surface attributed to oxygen and nitrogen atoms that is soluble. Conclusion We have created a unique calixarene-based probe (DCOC) that exhibits a selective and sensitive response to 2,4-DNT compared to other NACs. The fluorescence intensity of a DCOC solution in ACN is significantly reduced upon addition of 2,4-DNT. The findings indicate that DCOC binds to 2,4-DNT strongly and acts as a highly selective and sensitive sensor for these ions, exhibiting exceptional detection limits. Furthermore, 1H-NMR and 13C-NMR analyses were performed to describe DCOC, and mass spectrometry was utilized to elucidate the development of the complex between DCOC and 2,4-DNT. The results of the molecular interaction and stability studies showed that the DCOC's relationship with the PARP-1 protein was very stable. The synthesized compound DCOC exhibited moderate cytotoxicity against MCF-7 breast cancer cells, with an IC₅₀ value of 417.76 ± 1.77 µM. The dose-dependent decrease in cell viability highlights its potential as a therapeutic candidate for further anticancer evaluation. Declarations Acknowledgement Himali Upadhyay and Devanshi Bhatt would like to express their sincere gratitude for the financial support provided by SHODH- ScHemefor Developing High-Quality Research. Funding This study was financially supported by the funds granted to Himali Upadhyay and Devanshi Bhatt by SHODH- ScHeme for Developing High-Quality Research (Student reference No. 201901380089 and 202001380098 respectively). The author, Pooja Trivedi would like to thank the Department of Science and Technology (DST), New Delhi for providing the SRF-INSPIRE fellowship (IF190292). Author’s contribution Himali Upadhyay: Methodology, Data curation Investigation, Writing - original draft. Devanshi Bhatt: Methodology, editing, Kapil Kumar: Resources, Visualization. Manthan Panchal: Conceptualization, Writing - review & editing. Pooja Trivedi: Cytotoxic investigation, Methodology, Gaurang Sindhav: Visualization, Methodology. Uma Harikrishnan: Supervision. Bhumi Patel, Chirag Patel, Krunal Modi: Software analysis, Data curation. Data availability Not applicable Conflict of Interest There are no conflicts to declare. 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J Am Chem Soc 118(45):11225–11236 Schrödinger (2024) Release 2024-2: LigPrep, Schrödinger, LLC, New York, NY Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz,D. T., … Shenkin, P. S. (2004). Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. Journal of medicinal chemistry,47(7), 1739–1749. Bowers KJ, Chow E, Xu H, Dror RO, Eastwood MP, Gregersen BA, Klepeis JL, Kolossvary I, Moraes MA, Sacerdoti FD, Salmon JK, Shan Y, Shaw DE (2006) Scalable algorithms for molecular dynamics simulations on commodity clusters, Proceedings of the ACM/IEEE Conference on Supercomputing (SC06), Tampa, Florida, November 11–17 Price DJ, Brooks III, C. L (2004) A modified TIP3P water potential for simulation with Ewald summation. J Chem Phys 121(20):10096–10103 Patel CN, Mall R, Bensmail H (2023) AI-driven drug repurposing and binding pose meta dynamics identifies novel targets for monkeypox virus. J Infect Public Health 16(5):799–807 Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG (1995) A smooth particle mesh Ewald method. J Chem Phys 103(19):8577–8593 Wang J, Hou T, Xu X (2006) Recent advances in free energy calculations with a combination of molecular mechanics and continuum models. Curr Comput-Aided Drug Design 2(3):287–306 Zhang D, Lazim R (2017) Application of conventional molecular dynamics simulation in evaluating the stability of apomyoglobin in urea solution. Sci Rep 7(1):44651 Mosmann T (1983) Rapid colorimetric assay for cellular growth and survival: application to proliferation and cytotoxicity assays. J Immunol Methods 65(1–2):55–63 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6881765","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":478203508,"identity":"e122ddb3-a5b1-4097-bfa3-2c398f498a44","order_by":0,"name":"Himali Upadhyay","email":"","orcid":"","institution":"International Forensic Research Institute (IFRI), Florida International University","correspondingAuthor":false,"prefix":"","firstName":"Himali","middleName":"","lastName":"Upadhyay","suffix":""},{"id":478203509,"identity":"4c630305-f925-43f5-8230-5ab099a5dff7","order_by":1,"name":"Uma Harikrishnan","email":"","orcid":"","institution":"St. Xavier’s College","correspondingAuthor":false,"prefix":"","firstName":"Uma","middleName":"","lastName":"Harikrishnan","suffix":""},{"id":478203510,"identity":"57486e79-eee6-442e-a506-ec65e47cd085","order_by":2,"name":"Devanshi Bhatt","email":"","orcid":"","institution":"Gujarat University","correspondingAuthor":false,"prefix":"","firstName":"Devanshi","middleName":"","lastName":"Bhatt","suffix":""},{"id":478203511,"identity":"51dd0308-d508-4e81-828b-211844efbd46","order_by":3,"name":"Kapil Kumar","email":"","orcid":"","institution":"Gujarat University","correspondingAuthor":false,"prefix":"","firstName":"Kapil","middleName":"","lastName":"Kumar","suffix":""},{"id":478203512,"identity":"1a0b1dcc-aa84-458a-a7df-49f81e237773","order_by":4,"name":"Manthan Panchal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYJACZiSGhRyIceABfh2MzUhaJIzBWhJI0ZLYAGLh06Lbfvb444KaOwzm7GcPfi6okUjfcO3wQ6AtdnK6Ddi1mJ3JS2yecewZg2VPXrL0jGMSuRtupxkAtSQbmx3AoeVAjmEzD9thBoMDOWbMPGwgLQkgLQcSt+HScv4NUMs/oJbzb4Ba/kmkG9xO/4Bfyw2gLbxtQC03gLbwtkkkGNzOIWDLjTeGs3n7DjNYznhjLM3bJ2E483ZOwYEEAzx+OZ9j8Jnn22EGc/4cQyDDRp7vdvrmDx8q7ORwaYGB+g0wlgJYpQF+5QwoauQbiFA9CkbBKBgFIwoAABsVYnn5T9aaAAAAAElFTkSuQmCC","orcid":"","institution":"Silver Oak Institute of Science, Silver Oak University","correspondingAuthor":true,"prefix":"","firstName":"Manthan","middleName":"","lastName":"Panchal","suffix":""},{"id":478203514,"identity":"d0a43562-5049-4ceb-9b73-65f6f092000a","order_by":5,"name":"Pooja Trivedi","email":"","orcid":"","institution":"Gujarat University","correspondingAuthor":false,"prefix":"","firstName":"Pooja","middleName":"","lastName":"Trivedi","suffix":""},{"id":478203515,"identity":"23e4a168-eb1e-4c67-86c7-3ed4f6f99b74","order_by":6,"name":"Gaurang Sindhav","email":"","orcid":"","institution":"Gujarat University","correspondingAuthor":false,"prefix":"","firstName":"Gaurang","middleName":"","lastName":"Sindhav","suffix":""},{"id":478203520,"identity":"c0a2bde0-11d2-42a4-ad30-e9622732ff84","order_by":7,"name":"Bhumi Patel","email":"","orcid":"","institution":"Indrashil University","correspondingAuthor":false,"prefix":"","firstName":"Bhumi","middleName":"","lastName":"Patel","suffix":""},{"id":478203528,"identity":"1901a9fc-e105-43e4-8a8f-c05926b997cb","order_by":8,"name":"Chirag N. Patel","email":"","orcid":"","institution":"National Institute on Aging, NIH","correspondingAuthor":false,"prefix":"","firstName":"Chirag","middleName":"N.","lastName":"Patel","suffix":""},{"id":478203529,"identity":"05df8f87-faed-4a18-ab9a-b9d34be8cea0","order_by":9,"name":"Krunal Modi","email":"","orcid":"","institution":"Indrashil University","correspondingAuthor":false,"prefix":"","firstName":"Krunal","middleName":"","lastName":"Modi","suffix":""}],"badges":[],"createdAt":"2025-06-12 15:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6881765/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6881765/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85797337,"identity":"33abbe6e-6cbc-49f6-a09d-354cb792727a","added_by":"auto","created_at":"2025-07-01 20:12:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":28000,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical representation of reaction between DCOC and 2,4- DNT\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881765/v1/782b1c4cc38e49b0691e8643.jpg"},{"id":85797453,"identity":"a89bf8fa-c131-4c60-9387-b7b8619018a0","added_by":"auto","created_at":"2025-07-01 20:20:32","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19188,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSynthetic route for the preparation of sulfonyl chloride functionalized oxacalix[4]arene\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881765/v1/89d65db901ef0291c8b23c5f.jpg"},{"id":85797339,"identity":"3fbf6823-c541-4ee7-aef9-116d9f71519b","added_by":"auto","created_at":"2025-07-01 20:12:32","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30955,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariation in emission responses of the receptor DCOC at 370 nm towards various NACs in ACN\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881765/v1/6ae4934736932b132b99cbac.jpg"},{"id":85797733,"identity":"d814159d-2010-41e1-9710-72f97e8eedd1","added_by":"auto","created_at":"2025-07-01 20:28:32","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30429,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariation in emission intensity of DCOC upon gradually increasing concentration of 2,4-DNT\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881765/v1/885aa1a67e3126d9ec867687.jpg"},{"id":85797344,"identity":"af5e7ebc-9165-4db3-b482-d0e9b424d331","added_by":"auto","created_at":"2025-07-01 20:12:32","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":45146,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHOMO and LUMO of DCOC compound with energy band gap\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881765/v1/db2a1f4c7a86d8ce0a79cf77.jpg"},{"id":85797454,"identity":"fc5fae0d-2b4f-4a4d-93fe-cf71683d0ba0","added_by":"auto","created_at":"2025-07-01 20:20:32","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":91830,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCytotoxic assessment of DCOC using MTT assay\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881765/v1/63f06feec83d1f9786cef8c3.jpg"},{"id":85797359,"identity":"e59fe328-4465-4f0a-a5bd-537e1c88402a","added_by":"auto","created_at":"2025-07-01 20:12:33","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":814687,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSample DCOC treatment on MCF-7 cell line (Magnification 200x)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881765/v1/cf4c4e7943c09ad966208e01.jpg"},{"id":85797348,"identity":"2238d818-50db-4112-bbd2-9e62fe0cbec7","added_by":"auto","created_at":"2025-07-01 20:12:32","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":100968,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular docking of DCOC with PARP1 protein (7KK4). (A) 3 D representation and\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B) 2 D representation describing ligand interactions by formation of various H-bonds and hydrophobic interactions with the active site of the protein.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881765/v1/585c8ec8f99f98b63aa3505c.jpg"},{"id":85797468,"identity":"f13027eb-8942-450f-b09d-89ac92e11ac4","added_by":"auto","created_at":"2025-07-01 20:20:33","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":44124,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRMSD plot of PARP1-DCOC complex. (Desmond Molecular Dynamics System, D. E. Shaw Research v6.1; \u003c/strong\u003e\u003ca href=\"https://www.deshawresearch.com/resources_desmond.html/\"\u003e\u003cstrong\u003ehttps://www.deshawresearch.com/resources_desmond.html/\u003c/strong\u003e\u003c/a\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881765/v1/b98e3964c6258d908d82a5c4.jpg"},{"id":85797735,"identity":"6ff99ee4-949a-4e9d-9701-3df47efefc72","added_by":"auto","created_at":"2025-07-01 20:28:32","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":35571,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRMSF plot of PARP1-DCOC complex\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881765/v1/6e6e929636bc35c3aad62a69.jpg"},{"id":85797350,"identity":"158e8a26-c9bb-48a8-ac99-b9d794107962","added_by":"auto","created_at":"2025-07-01 20:12:32","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":25491,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVarious intermolecular interactions, made by PARP1-DCOC, captured during molecular dynamics simulations. (Desmond Molecular Dynamics System, D. E. Shaw Research v6.1; \u003c/strong\u003e\u003ca href=\"https://www.deshawresearch.com/resourcesdesmond.html/\"\u003e\u003cstrong\u003ehttps://www.deshawresearch.com/resourcesdesmond.html/\u003c/strong\u003e\u003c/a\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881765/v1/3aeabe794ddf093e1c5b4b28.jpg"},{"id":85797377,"identity":"f89fe6d3-e020-4266-b599-318bf446b1a6","added_by":"auto","created_at":"2025-07-01 20:12:33","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":156874,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePreserved contacts obtained during molecular dynamics simulations of PARP1-DCOC complex.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881765/v1/6f4af5788c6725a81ced8607.jpg"},{"id":85797461,"identity":"621d9a5f-413b-458c-bc40-59a8e276a11d","added_by":"auto","created_at":"2025-07-01 20:20:32","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":56124,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLigand properties of DCOC such as Solvent Accessible Surface Area (SASA), the radius of gyration (rGyr) on interacting with PARP1 protein\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6881765/v1/972f3381c204ece722de11b0.jpg"},{"id":86426361,"identity":"401e6497-2978-446b-8187-85225dbcde58","added_by":"auto","created_at":"2025-07-10 13:32:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2854224,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6881765/v1/539887f0-8126-4c6a-aa5e-2b2f801f23d9.pdf"},{"id":85797345,"identity":"cc77569a-1002-495a-994e-ba1b7dbf9149","added_by":"auto","created_at":"2025-07-01 20:12:32","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":353515,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-6881765/v1/285e8f7c58ac7bf66960c6c7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Aqueous-Phase Detection of Explosive Residues: Sulfonyl Chloride-Appended Oxacalix[4]arene for 2,4-DNT Monitoring","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn recent times, one of the most crucial safety and security concerns has emerged: the identification of explosives and other illicit substances that can be utilized to manufacture explosives. As a result, there have been several terrorist attacks, fatalities, and property damages. Explosive detection is consequently crucial in today's world.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Explosive detection is essential for forensic investigations such as arson[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] or post-blast residue findings[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], mine fields, military bases, remediation sites, urban transportation areas[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and workplace monitoring [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A primary aspect of homeland security is the detection of explosives. Military engagements and terrorist assaults predominantly rely on explosive substances. Consequently, the identification of these explosive substances is essential for the preservation of all living creatures and the environment. Terrorists utilize explosives in enormous quantities due to their convenience in production and deployment[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Due to the following, it is likely that 2,4-dinitrotoluene (DNT) will be found when looking for explosives:(i) 1,3,5-trinitrotoluene (TNT) is the main ingredient in explosives, but production-grade TNT has a lot of DNT in it [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].(ii) DNT is a by-product of TNT's decomposition. (iii) At 20 degrees Celsius, the saturation concentration of DNT in air is approximately 25 times that of TNT (148 parts per billion (ppb) versus 6 ppb) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Using the basic toluene nitration and dinitration reactions, nitrotoluene and dinitrotoluene can be synthesised [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. There are opportunities for 4-NT and 2,6- DNT to easily permeate the soil and harm groundwater or nearby streams. Epidemiological data indicate that nitro-aromatic compounds (including nitrobenzene, dinitrotoluenes, mono and di-nitrophenols) are potent carcinogens, and are therefore referred to as priority pollutants [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Electrochemical [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], chromatographic techniques [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], optical noses [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], fluorescence [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e][\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], X-ray diffraction [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and surface acoustic wave (SAW) sensors [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] are among the techniques investigated for detecting explosive compounds. The development of a sensor material that provides specific and sensitive results for analysing the chemical signatures of explosives in the presence of other analytes is currently the subject of intense research. It has been reported that calixarene-based chemo sensors exhibit a unique response to any analyte of interest based on the optimisation of structural and geometrical parameters [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e][\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e][\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Calixarenes represent third-generation supramolecular frameworks[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Calixarenes are defined by a central annulus encircled by a broad upper rim and a lower rim, exhibiting a high degree of preorganization and conformational preferences. Calix[n]arenes are third-generation supramolecules characterized by a hollow, hydrophobic cavity capable of encapsulating various guest analytes, including cations, anions, and neutral molecules [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e][\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e][\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Oxacalixarenes (OC) are notable heteracalixarene analogues containing oxygen as the bridge atom [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These macrocycles are readily available because they can be synthesised in a single step at room temperature by high yield nucleophilic aromatic substitution (SNAr) reactions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Experimental section","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Chemical reagents and instruments\u003c/h2\u003e \u003cp\u003eAll the chemicals including 1,5-difluoro-2,4-dinitrobenzene, Pyrogallol and NACs were purchased from Sigma-Aldrich. The solvents used for synthesis and analysis were purchased from Finar Chemicals and utilised without further purification. Thin-layer chromatography (TLC) with E-Merck silica gel 60 F254 precoated plates, visualised with UV-light (254 nm) or I\u003csub\u003e2\u003c/sub\u003e vapour, was used to monitor the progression of the reaction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Synthesis of DCOC:\u003c/h2\u003e \u003cp\u003eAt room temperature, the reaction is conducted by adding Potassium Carbonate to a solution of Oxacalixarene (0.5g, 0.86mmol) in dry acetone (20 ml)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. After agitating the reaction mixture for 1 h, biphenyl 4 sulfonyl chloride (0.4g, 0.17 mmol) is added and the mixture is swirled vigorously for 24 h. The solvent is evaporated in vacuo. The crude obtained is subjected to the column chromatography using silica gel with EtOA-C: Hexane (3:7) as an eluent. The fractions were separated, dried and recrystallized in alcohol to give DCOC (0.35 g) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Spectroscopic measurement\u003c/h2\u003e \u003cp\u003eWith the aid of spectrofluorometric measurements, the sensitivity of the instrument, DPOC, towards NACs was investigated. The 1 mM stock solution of the receptor DCOC was initially prepared with Acetonitrile as the solvent. In order to conduct spectrofluorometric experiments, the stock solution was further diluted to 100 M. In the same manner, acetonitrile stock solutions (1 mM) of explosives such as 1,3- DNB, 2,3- DNT, 2,4- DNT, 2,6- DNT, 4-NP, 4- NT, and PA were prepared. In addition, the receptor was excited at 330 nm, and the addition of the aforementioned analytes resulted in a change in emission maxima. The temperature was maintained at (298\u0026thinsp;\u0026plusmn;\u0026thinsp;2) K throughout the duration of the investigation. Excitation and emission slit widths were set to 5 nm for all measurements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Computational methodology\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Density Functional Theory calculations\u003c/h2\u003e \u003cp\u003eThe Jaguar program utilized for efficient quantum chemical computational for electronic structure prediction of medium and large molecular systems. we mostly focused on improving Jaguar's performance and precision in DFT computations, employing the B3LYP-D3 functional of the DCOC with the 6-31g(d,p) basis set of the Schrodinger (\u003cb\u003eMaterial Science Suite14.0)\u003c/b\u003e was followed in the simulations. Exploring the theoretical aspects of the highest occupied molecular orbital energy (E\u003csub\u003eHOMO\u003c/sub\u003e) and the lowest unoccupied molecular orbital energy (E\u003csub\u003eLUMO\u003c/sub\u003e), including the energy gap (ΔE) between LUMO and HOMO. The structure has been optimized, showing no negative frequencies. The DCOC compound exhibits distinct conformations. Conformational analysis examines the various energy states connected to the distinct conformations of a molecule. We obtained 100 conformations of this molecule, from which we selected the optimal geometry for further optimization. The conformational exploration is a crucial phase in computational chemistry for investigating the diverse spatial configurations of a molecule. The main aim of a conformational search is to determine the most stable structure or the global minimum energy conformation of a molecule. Different molecular conformations have different potential energies. To accurately predict molecular properties to find the conformation with the lowest energy. This procedure generally utilizes molecular mechanics or semi-empirical approaches, contingent upon the systems size and complexity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Molecular Docking\u003c/h2\u003e \u003cp\u003eThe Glide program, part of the Schr\u0026ouml;dinger suite and enhanced with Extra Precision (XP) capabilities, was used to dock the eight calix[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]pyrroles into the crystal structure of the catalytic domain of PARP1 in conjunction with Olaparib (PDB ID: 7KK4). XP docking, which is more thorough and discriminating than the standard precision (SP) approach, requires a longer duration due to its complex operation. This is designed for ligands that have attained high scores in SP docking, using a comprehensive grading system that requires improved congruence between ligand and receptor geometries. This stringent process aims to eliminate false positives often linked to SP by imposing penalties on ligands that poorly conform to the designated receptor structure. DCOC was selected as a ligand for the molecular docking analysis. LigPrep from Schr\u0026ouml;dinger software (Schr\u0026ouml;dinger 2024-3) was used for ligand generation, whereas the Protein Preparation Wizard was applied for protein preparation. This encompasses the addition of hydrogen, the removal of water molecules, and energy reduction with the Optimized Potentials for Liquid Simulations (OPLS) 2005 forcefield. The active site of the receptor was identified based on the known binding affinity of 09L (4-(3-{[4-(cyclopropylcarbonyl)piperazin-1-yl]carbonyl}-4- fluorobenzyl)phthalazin-1(2H)-one). The active site was used for docking PARP-1 and DCOC. Therefore, it is advisable to dock against many receptor conformations to ascertain ligands with appropriate binding energies for molecular dynamics modeling studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Molecular dynamics (MD) simulation\u003c/h2\u003e \u003cp\u003eA Desmond package [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e][\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e][\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e][\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e][\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] molecular dynamics simulation was conducted for 100 ns on the PARP1-DCOC complex [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The complex was generated with a protein preparation wizard that aids in complex relaxation. The procedure included the incorporation of hydrogens, removal of water, allocation of bond order, and finalization of missing side chains and loops, while optimizing hydrogen-bond assignments at pH 7.0 and monitoring water orientations. Complexes underwent energy reduction with the OPLS-2005 force field prior to molecular dynamics simulations. The TIP3P solvent model was then used to construct the system [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A 10 \u0026Aring; buffer zone was established around the ligand-protein complex inside an orthorhombic simulation box appropriate for the protein-ligand combination. The subsequent phase included using OPLS-2005 force fields to incorporate Na\u0026thinsp;+\u0026thinsp;ions and a salt concentration of 0.15 M Na\u0026thinsp;+\u0026thinsp;and Cl- counter ions into a simulation box for neutralization, therefore simulating physiological conditions and background salt levels. Following the successful construction of the system, molecular dynamics simulation was conducted using an NPT (constant number of particles, pressure, and temperature) ensemble at 300 K and 1.013 bar of atomic pressure [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The default surface tension was determined using the Smooth Particle Mesh Ewald (PME) approach [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which concurrently calculates the long-range electrostatic interaction potential energies employing the RESPA integrator [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Trajectories for 100 frames were recorded throughout the 100 ns molecular dynamics simulation. Each trajectory was analyzed using the Simulation Interaction Diagram wizard, which computes trajectories for Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) upon completion of the simulation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Furthermore, the contact patterns between protein and ligand, together with the timeframe of these specific interactions, are assessed in the context of a 100 ns simulation for key interacting amino acid residues. The molecular dynamics simulation method was used to validate the docking orientations and interactions anticipated between both ligands and the PARP-1 protein throughout the docking process.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results and Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Synthesis and Characterization\u003c/h2\u003e\n \u003cp\u003eThe desired receptor DCOC was synthesised through the cyclo-condensation of 1,5-difluoro-2,4-dinitrobenzene and bis-sulfonyl chloride in dry acetone. After completion of the reaction, the crude was purified with column chromatography using 10% ethyl acetate/hexane as eluent (Rf\u0026thinsp;~\u0026thinsp;0.27). The receptor DCOC was obtained as a yellow solid with 75% yield, m.p. \u0026gt;300 \u0026ordm;C. Elemental analysis of C\u003csub\u003e48\u003c/sub\u003eH\u003csub\u003e28\u003c/sub\u003eN\u003csub\u003e4\u003c/sub\u003eO\u003csub\u003e18\u003c/sub\u003eS\u003csub\u003e2\u003c/sub\u003e: 1H NMR (400 MHz, DMSO) \u0026delta; 8.74 (s, 1H), 7.71 (s, 1H), 7.69 (d, J\u0026thinsp;=\u0026thinsp;2.0 Hz, 1H), 7.65 (s, 1H), 7.61 (s, 1H), 7.58 (dd, J\u0026thinsp;=\u0026thinsp;6.9, 2.1 Hz, 1H), 7.50\u0026ndash;7.48 (m, 1H), 5.75 (s, 1H), 3.36 (s, 1H). 3C NMR (101 MHz, DMSO) \u0026delta; 145.62 (s), 131.82 (s), 129.63 (s), 128.26 (s), 127.76 (d, J\u0026thinsp;=\u0026thinsp;13.9 Hz), 123.67 (s), 40.62 (s), 40.41 (s), 40.20 (s), 39.99 (s), 39.78 (s), 39.57 (s), 39.36 (s).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Selectivity of probe\u003c/h2\u003e\n \u003cp\u003eIn order to obtain the selectivity of the probe DCOC, towards NACs, absorption and fluorescence studies were performed in binary mixture. The receptor DCOC exhibits maximum absorption at 370 nm. Among all the NACs involved in the interaction with the receptor, only 2,4- DNT shows change in absorption maxima (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Although the absorption study briefs about the selectivity of the guest analytes but cannot provide information about the sensitivity, stoichiometry, nature of binding and mechanism involved for sensing. Therefore, emission studies were introduced to get insight about these parameters. DCOC exhibits shows strong fluorescence at an emission wavelength of 370 nm (\u0026lambda;\u003csub\u003eex\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;370 nm). A remarkable decrease in emission intensity of receptor was observed upon addition of 2,4- DNT (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). On other hand 2,4- DNT substantially binds with the probe resulting quenching in fluorescence intensity, keeping rest of the NACs unaffected. Mass analysis data supports chemical reaction and complexation between DCOC and 2,4- DNT.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Computational methodology\u003c/h2\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1 Density Functional Theory calculations\u003c/h2\u003e\n \u003cp\u003eThe conformational search provides multiple possible molecule configurations and analyses their energy. The conformers possessing the lowest energies undergo further optimization procedures to get an appropriate molecular geometry. Density Functional Theory (DFT) or Hartree-Fock (HF) approaches, HOMO (Highest Occupied Molecular Orbital) and LUMO (Lowest Unoccupied Molecular Orbital) are essential principles in molecular orbital theory. HOMO and LUMO are referred to as FMO (frontier molecular orbitals) as they represent the highest occupied and lowest unoccupied molecular orbitals. The aim of geometry optimization is to determine the lowest energy configuration by adjusting bond lengths, bond angles, and dihedral angles. The geometry optimization value at gas phase and solvent phase are \u0026minus;\u0026thinsp;4215.005980 Hatree and \u0026minus;\u0026thinsp;4215.037751 Hatree, respectively. This is typically achieved by quantum mechanical methods. These orbitals are essential in defining the optical and electrical characteristics of molecules, including their HOMO-LUMO energy difference provides a basic measurement for chemical reactivity, with a large HOMO-LUMO gap signifying substantial stability and low chemical reactivity. DCOC compound shown HOMO, LUMO and energy band gap, were discovered to be -6.6291 eV, -2.9861 eV and \u0026minus;\u0026thinsp;3.643 eV, respectively. The intramolecular charge transfer interaction is indicated by the HOMO-LUMO energy gap, which corresponds to the band gap energy of bioactive chemicals (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCalculated energy values of DCOC Ligand using b3lyp-d3/ 6-31g (d,p) method.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEnergy Value (eV)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e∆E\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003cp\u003ehardness (\u0026eta;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGlobal softness (S)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003echemical potential (\u0026micro;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eElectrophilicity index (\u0026omega;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003estabilization energy (∆E)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHOMO\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLUMO\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.6291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.9861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.274499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.8076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.344501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.3445012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe global molecular descriptors, including global hardness, Softness (S), Chemical Potential, Electrophilicity, and stabilization energy the results are reported in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e DCOC compound considerable energy transition, this molecule is not hard and also exhibit low toxicity due to its minimal softness value.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cstrong\u003e3.4 Cytotoxic assessment of compound using MTT assay\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eCytotoxicity was evaluated using the MTT assay as described by Mosmann (1983) [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. MCF-7 cells were cultured in complete media consisting of 10% Fetal Bovine Serum (FBS) and 90% Dulbecco\u0026apos;s Modified Eagle Medium (DMEM) at 37\u0026deg;C in a humidified atmosphere with 5% CO₂ (Eppendorf CellXpert). After reaching confluency, 1\u0026times;10⁴ cells were seeded in each well of a 96-well plate (Tarsons). The following day, treatments with sample DCOC at specified concentrations were applied, with control wells for untreated cells and solvent-only treatments. After 24 hours of incubation, 50 \u0026micro;l of MTT reagent (5 mg/ml in 1x PBS) was added to each well, followed by a 3\u0026ndash;4-hour incubation in the dark to allow for formazan crystal formation. The crystals were dissolved in 100 \u0026micro;l DMSO (HiMedia) for 15\u0026ndash;20 minutes. Absorbance was measured at 570 nm using an Epoch multiplate reader (BioTek), and results were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. IC₅₀ values were determined from the concentration-response curves. Cell viability (%) was calculated using the formula: Cell Viability (%)\u0026thinsp;=\u0026thinsp;Absorbance of Treated Wells/Absorbance of Untreated Wells \u0026times; 100. Morphological changes were observed using an inverted microscope (Weswox) equipped with a KEOWA CE-500X camera and Capture Pro software (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe cytotoxic potential of the synthesized compound was evaluated on the MCF-7 breast cancer cell line using the MTT assay. Increasing concentrations of the compound were applied to the cells, and the results indicated a significant reduction in cell viability in a dose-dependent manner (Fig. 7). As illustrated in Graph 1, the compound demonstrated increasing cytotoxicity with higher concentrations. The IC50 value, representing the concentration at which 50% of cell viability was inhibited, was calculated to be 417.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77 \u0026micro;M. This IC50 value suggests moderate cytotoxicity, highlighting the compound\u0026apos;s potential as a therapeutic agent.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Computational methodology for cytotoxic assessment\u003c/h2\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.1 Evaluation of DCOC Docking with PARP-1 Protein\u003c/h2\u003e\n \u003cp\u003eThe interaction between DCOC and PARP1 protein indicates that DCOC draws Q759 and Y907, leading to the formation of hydrogen bonds. Q759, E763, M890, Y896, and Y907 established hydrophobic interactions with the PARP1 protein. \u0026pi;-\u0026pi; stacking was also established using Y889, Y896, and Y907 (Figure-8). The binding energy of this interaction is calculated to be -2.1 kcal/mol using the XP docking method.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.2 MD simulations investigation of the PARP1-DCOC complex\u003c/h2\u003e\n \u003cp\u003eMolecular dynamics (MD) simulations are essential for corroborating the outcomes of docking evaluations. These simulations provide a dependable method for validating the results acquired by molecular docking. The MD simulations will precisely determine the ligand\u0026apos;s contact time and the exact types of interactions it engages in with the protein. Docking offers insights into the possible interaction between a ligand and a protein, whereas molecular dynamics simulations can measure the strength and duration of intermolecular contact. The molecular dynamics simulations of the PARP1-DCOC complex were performed for 1000 ns. The resultant data was further evaluated to assess the similarity in the interaction pattern between the ligand DCOC and the PARP1 protein.\u003c/p\u003e\n \u003cp\u003eFigure-9 illustrates the outcomes derived from molecular dynamics simulations of the DCOC complex. The first assessment performed is the computation of Root Mean Square Deviations (RMSD). The RMSD plot has two main assessments: (i) Protein RMSD and (ii) Ligand RMSD relative to the protein, which are to be compared. The RMSD graph illustrates the protein\u0026apos;s root- mean-square deviation (RMSD) on the left Y-axis. The root mean square deviation (RMSD) of the ligand DCOC inside the catalytic site of the PARP1-DCOC complex is shown on the Y-axis to demonstrate the stability of the binding. The RMSD range of variations seen in the figure is up to 5 \u0026Aring;, with these differences being less prominent among proteins of varying sizes.\u003c/p\u003e\n \u003cp\u003eThe RMSF evaluative metric identifies atoms and amino acid residues undergoing significant variations over the simulation period. Figure-10 illustrates the RMSF profile of the simulated PARP-1-DCOC complex, exhibiting fluctuations between 25\u0026ndash;50 ns and 60\u0026ndash;80 ns (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe Protein\u0026ndash;Ligand interaction fraction plot for the PARP-1-DCOC complex (Figure-11) illustrates that both proteins engage with identical amino acids, establishing significant interactions as shown by the interaction fraction values. The interaction fraction in the plot is a numerical value derived from the conversion of the proportion of interactions. A value of 1.0 indicates that the whole of the simulation duration (100 ns) is dedicated to establishing contacts, including hydrogen bonds, hydrophobic interactions, ionic interactions, and water bridges. MD simulation studies accurately demonstrate the relationship between ligands and protein receptors, achieving a high degree of precision and scientific agreement. The increasing computational capability facilitates the use of this technology to validate ideas about ligand-protein interactions. The PARP-1-DCOC complex was established by many hydrophobic interactions (light purple) including the amino acids His 862, Tyr 889, Ile 895, Tyr 896, Tyr 907, Leu 984, and Tyr 986. In the protein chain, the\u003c/p\u003e\n \u003cp\u003eamino acids Gln 759, Ala 760, Asn 767, Gly 863, Ser 864, Arh 865, Met 890, Lys 903, Asn 906, and Tyr 907 formed hydrogen bonds (shown in green). Five ionic bonds (magenta) were identified with the amino acids Glu 688, Glu 763, Arg 865, Lys 903, and Glu 988.\u003c/p\u003e\n \u003cp\u003eThe 2D interaction graphs of DCOC illustrate the continuity of interactions during the simulation period. The DCOC molecule exhibited five hydrophobic interactions with Gln 636 (39%) and His 198 (44%), along with two positive charges with Arg 865 (59%) and Lys 903 (47%). Furthermore, there were three pi-pi interactions with Ala 760 (58%), Tyr 896 (35%), and Tyr 889 (32%); one polar contact with His 862 (67%); and two salt bridges with Arg 865 (59%) and Lys 903 (47%) (Figure-12).\u003c/p\u003e\n \u003cp\u003eThe interaction of ligands, including DCOC (Figure-13), is shown by charts that demonstrate the characteristics of the ligands. The extent of a ligand\u0026apos;s extension, indicative of its primary measure of inactivity, is measured by the radius of gyration (rGyr). The evaluation of the 690 to 720 range of atomic surface configuration is shown by the molecular surface area (MolSA). This number is directly proportional to the surface area of the Van der Waals surface. Solvent Accessible Surface Area (SASA) denotes the portion of a molecule\u0026apos;s surface that is reachable by water molecules (300 to 420). Conversely, Polar Surface Area (PSA) is the segment of a molecule\u0026apos;s surface attributed to oxygen and nitrogen atoms that is soluble.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":" \u003cp\u003eWe have created a unique calixarene-based probe (DCOC) that exhibits a selective and sensitive response to 2,4-DNT compared to other NACs. The fluorescence intensity of a DCOC solution in ACN is significantly reduced upon addition of 2,4-DNT. The findings indicate that DCOC binds to 2,4-DNT strongly and acts as a highly selective and sensitive sensor for these ions, exhibiting exceptional detection limits. Furthermore, 1H-NMR and 13C-NMR analyses were performed to describe DCOC, and mass spectrometry was utilized to elucidate the development of the complex between DCOC and 2,4-DNT. The results of the molecular interaction and stability studies showed that the DCOC's relationship with the PARP-1 protein was very stable. The synthesized compound DCOC exhibited moderate cytotoxicity against MCF-7 breast cancer cells, with an IC₅₀ value of 417.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77 \u0026micro;M. The dose-dependent decrease in cell viability highlights its potential as a therapeutic candidate for further anticancer evaluation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHimali Upadhyay and Devanshi Bhatt would like to express their sincere gratitude for the financial support provided by SHODH- ScHemefor Developing High-Quality Research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was financially supported by the funds granted to Himali Upadhyay and Devanshi Bhatt by SHODH- ScHeme for Developing High-Quality Research (Student reference No. 201901380089 and 202001380098 respectively). The author, Pooja Trivedi would like to thank the Department of Science and Technology (DST), New Delhi for providing the SRF-INSPIRE fellowship (IF190292).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHimali Upadhyay: Methodology, Data curation Investigation, Writing - original draft. Devanshi Bhatt: Methodology, editing, Kapil Kumar: Resources, Visualization. Manthan Panchal: Conceptualization, Writing - review \u0026amp; editing. Pooja Trivedi: Cytotoxic investigation, Methodology, Gaurang Sindhav: Visualization, Methodology. Uma Harikrishnan: Supervision. Bhumi Patel, Chirag Patel, Krunal Modi: Software analysis, Data curation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no conflicts to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declaration Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Participation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUpadhyay H, Harikrishnan U, Bhatt D, Dhadnekar N, Kumar K, Panchal M (2023) Calixarene: The Dawn of a New Era in Forensic Chemistry. 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J Immunol Methods 65(1\u0026ndash;2):55\u0026ndash;63\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Oxacalixarene, 2,4-Dinitrotoluene (2,4-DNT), Explosives, Fluoroscence studies, Density functional theory (DFT), Cytotoxicity studies","lastPublishedDoi":"10.21203/rs.3.rs-6881765/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6881765/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe designed and synthesized a novel sulfonyl chloride-substituted oxacalixarene (DCOC) as a selective probe for the detection of 2,4- DNT. Its structure was characterized with the use of 1H-NMR, 13C-NMR, and FT-IR analysis techniques. The binding property of DCOC to various NACs, such as 1,3-DNB, 2,3-DNT, MNA, 2,6-DNT, 4-NP, 4-NT,4-NP, and PA, revealed that the sensor binds with remarkable selectivity to 2,4- DNT. The binding constant reveals lower 2,4- DNT detection limits. The HOMO-LUMO energy calculation, chemical reactivity and geometric properties of reveal the stable confirmation of the DCOC through using the DFT approach. Additionally, a docking study between DCOC and PARP-1 protein shows a strong molecular interaction with a binding energy of -2.1 kcal/mol. The stability was checked using an MD simulation that ran for 500ns. The cytotoxic potential of the synthesized compound was evaluated on the MCF-7 breast cancer cell line using the MTT assay.\u003c/p\u003e","manuscriptTitle":"Aqueous-Phase Detection of Explosive Residues: Sulfonyl Chloride-Appended Oxacalix[4]arene for 2,4-DNT Monitoring","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 20:12:27","doi":"10.21203/rs.3.rs-6881765/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"47c16bdc-a665-4245-81e0-3113565155f0","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-10T13:23:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-01 20:12:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6881765","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6881765","identity":"rs-6881765","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}