📄 chemistry

Crystal Structure, DFT Studies, Molecular Docking and In Vitro Antibacterial–Antifungal Evaluation of 2,4-Dinitro-1-naphthyl 4-Toluenesulfonate (PTN): A Combined Experimental–Theoretical Study with an Outlook on AI Applications

This study integrates single-crystal X-ray diffraction, DFT calculations, molecular docking, and in vitro screening to characterize the structural and electronic properties of the antimicrobial candidate 2,4-dinitro-1-naphthyl 4-toluenesulfonate (PTN), demonstrating its superior binding affinity to key pathogen targets and effective activity against drug-resistant strains compared to reference drugs.

Original authors: G Ramachandran, charles kanakam

Published 2026-10-23

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: G Ramachandran, charles kanakam

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

In the microscopic world where bacteria and fungi thrive, life often hinges on a single chemical handshake. These tiny organisms rely on specific proteins to survive, acting as essential machinery for their growth and reproduction. Scientists who study how to stop these pathogens look for molecules that can slip into these protein machines and jam the gears. To do this effectively, they need to understand two things: the exact shape of the molecule they are testing, and the shape of the protein it is trying to block. It is a game of three-dimensional fit, where even a slight twist in a molecule's structure can mean the difference between a cure and a failure. For decades, researchers have used powerful computers to predict how well a drug candidate might fit, but these predictions are only as good as the map they start with. If the map of the molecule is slightly off, the prediction will be wrong. This is why combining a precise physical measurement of a molecule's structure with computer simulations remains one of the most reliable ways to understand how a new chemical might fight infection.

A team of researchers at Presidency College in Chennai, India, recently applied this rigorous approach to a specific chemical compound known as 2,4-dinitro-1-naphthyl 4-toluenesulfonate, or PTN for short. This molecule belongs to a class of chemicals that have shown promise in fighting microbes, but its exact behavior in the body was not fully understood. The researchers wanted to see if they could prove, with both physical evidence and computer modeling, that PTN is a strong candidate for stopping harmful bacteria and fungi. They began by growing a perfect, single crystal of the substance and shining X-rays through it. This process, known as X-ray crystallography, allowed them to build a precise, three-dimensional map of every atom in the molecule, showing exactly how they are arranged in space. They found that the atoms form a specific shape where a central sulfur atom connects to an oxygen atom in a flexible bond, creating a structure that is slightly twisted rather than perfectly flat.

With this physical map in hand, the team turned to computer simulations to see how PTN behaves when it is not trapped in a crystal. They used a method called density functional theory, which calculates how electrons move around the atoms to determine the molecule's stability and reactivity. The computer model confirmed that the shape they saw in the crystal was largely correct, with only a tiny difference in the length of that flexible sulfur-oxygen bond, a variation that is expected when moving from a solid crystal to a free-floating molecule. The calculations also revealed that the molecule has a strong electrical pull, with a significant separation of positive and negative charges. This electrical imbalance suggests the molecule is well-equipped to interact with the charged surfaces of biological proteins, a key requirement for a drug to work.

The researchers then took the next step, using the computer to simulate how PTN would interact with the specific proteins that keep dangerous microbes alive. They tested the molecule against three different targets: a protein that helps a common skin bacterium called Staphylococcus aureus make DNA, a protein that helps Shigella bacteria build their cell walls, and a protein that helps the fungus Candida albicans build its cell membrane. In every case, the computer predicted that PTN would bind to these proteins more tightly than the standard drugs currently used to treat them. For instance, when tested against the fungal protein, the simulation showed PTN holding on with a strength that was significantly better than fluconazole, a common antifungal medication. This was a striking result, especially because the researchers had also tested the real-world effectiveness of the compound in a lab dish.

When the team actually exposed bacteria and fungi to PTN in the laboratory, the results matched their computer predictions. The compound successfully stopped the growth of Staphylococcus aureus, Shigella, and Candida albicans. Most notably, it worked against a strain of Candida that was completely immune to fluconazole, the standard treatment. This confirmed that the strong binding predicted by the computer was real and that the molecule could indeed jam the gears of these microscopic machines. The researchers also noted that while the computer predicted the strongest binding against the fungal target, the bacteria were actually more sensitive in the lab dish. They explained that this difference is not a contradiction but a reminder that computer models measure how tightly a molecule sticks to a protein, whereas a lab test also depends on how easily the molecule can move through a gel and reach the bug. The two methods measure different things, but together they tell a consistent story: PTN is a potent agent that fits well into the targets it is designed to hit.

Looking ahead, the authors suggest that the tools used in this study could be made even more powerful with the help of artificial intelligence. While the current methods are reliable, they can be slow and computationally expensive. Newer AI techniques could potentially speed up the process of finding the right shape for a molecule or predicting how it will bind to a protein, allowing scientists to test thousands of candidates in the time it currently takes to test a few. However, the researchers caution that these AI tools are not yet perfect for every type of chemical, especially those with complex or unusual structures like the one they studied. For now, the combination of physical crystal measurement and careful computer simulation remains the gold standard. This study provides a complete and reproducible blueprint for understanding PTN, offering a clear path for future scientists to design even better versions of this compound to fight the growing threat of drug-resistant infections.

Technical Summary: Crystal Structure, DFT Studies, Molecular Docking, and In Vitro Evaluation of PTN

Problem Statement
The study addresses the need to establish a comprehensive structure–activity rationale for 2,4-dinitro-1-naphthyl 4-toluenesulfonate (PTN), a sulfonate-ester antimicrobial candidate. While the compound's solid-state structure was previously determined, a gap existed in correlating its specific electronic properties, crystal packing, and theoretical target-binding behavior with its experimentally observed antibacterial and antifungal efficacy. The authors sought to integrate single-crystal X-ray diffraction (XRD), density functional theory (DFT), and molecular docking to validate PTN's mechanism of action against specific pathogens (Staphylococcus aureus, Shigella spp., and Candida albicans) and to benchmark its performance against clinical reference drugs.

Methodology
The research employed a combined experimental–theoretical workflow utilizing exclusively open-source software to ensure reproducibility:

Crystallographic Reconstruction: Cartesian coordinates were reconstructed from reported unit-cell and fractional-coordinate data. The crystal structure (monoclinic, space group P21/c) was analyzed for hydrogen bonding and $\pi$ – $\pi$ stacking interactions.
DFT Calculations: Geometry optimization was performed using a staged protocol to balance computational cost and accuracy: optimization at the B3LYP/3-21G* level (specifically chosen to avoid artificial elongation of sulfonate S–O bonds) followed by single-point energy calculations at B3LYP/6-31G(d,p). This yielded frontier orbital energies (HOMO/LUMO), dipole moments, and Mulliken population analyses.
Molecular Docking: PTN and relevant reference drugs (carbenicillin, fluconazole) were docked against organism-matched protein targets: Dihydrofolate reductase (DHFR, PDB 3SH2) for S. aureus, Penicillin-binding protein 3 (PBP3, PDB 6I1I, using an E. coli surrogate) for Shigella spp., and Sterol 14 $\alpha$ -demethylase (CYP51, PDB 5FSA) for C. albicans. Docking was executed using AutoDock Vina with an exhaustiveness of 16.
In Vitro Evaluation: Antibacterial and antifungal activities were assessed via disc diffusion and serial two-fold broth microdilution to determine Minimum Inhibitory Concentrations (MIC) and Minimum Bactericidal/Fungicidal Concentrations (MBC).

Key Results

Structural Validation: The DFT-optimized geometry closely reproduced XRD-derived bond lengths and angles. The largest deviation (+0.107 Å) occurred at the flexible S–O(ester) bond, attributed to the difference between gas-phase optimization and solid-state crystal packing effects. The crystal packing is stabilized by a network of C–H···O hydrogen bonds and weak $\pi$ – $\pi$ stacking.
Electronic Properties: PTN exhibits a HOMO–LUMO gap of 3.53 eV and a dipole moment of 8.28 D. The electron-withdrawing dinitro and sulfonyl groups stabilize the LUMO, enhancing the molecule's electrophilic character. Mulliken analysis identifies the sulfonyl sulfur as the most positively charged site and the sulfonyl/nitro oxygens as the most negative, marking them as primary sites for hydrogen-bonding interactions.
Docking Performance: PTN demonstrated favorable binding energies across all targets, consistently outperforming reference drugs in mechanistically valid comparisons:
- CYP51 (C. albicans): PTN (-10.26 kcal/mol) showed significantly stronger predicted binding than fluconazole (-8.11 kcal/mol).
- PBP3 (Shigella spp.): PTN (-9.08 kcal/mol) outperformed carbenicillin (-8.08 kcal/mol).
- DHFR (S. aureus): PTN achieved a binding energy of -8.02 kcal/mol.
In Vitro Activity: PTN exhibited concentration-dependent activity against all tested organisms with a uniform MIC of 1.171 $\mu$ M. Notably, PTN retained antifungal activity against a C. albicans strain that was completely unresponsive to fluconazole (which showed no zone of inhibition).

Correlation of Theory and Experiment
The study explicitly correlates theoretical and experimental data. The superior docking score of PTN at the CYP51 target aligns with the experimental observation that PTN is effective against a fluconazole-resistant strain, whereas fluconazole is not. Similarly, the favorable PBP3 docking score correlates with activity against Shigella species. The authors caution that docking scores across different protein folds (e.g., DHFR vs. CYP51) cannot be directly compared to rank relative potency across different organisms due to variations in pocket size and scoring function calibration; however, within a specific target, the theoretical ranking supports the experimental efficacy.

Significance and Outlook
The paper establishes a complete, reproducible dataset linking the crystal structure, electronic properties, and target-binding affinity of PTN to its biological activity. The authors position this work as a foundation for future structure–activity relationship (SAR) studies on this compound class.

Regarding Artificial Intelligence (AI), the paper outlines specific, modest opportunities for future extension rather than claiming current AI superiority:

Machine-Learned Interatomic Potentials (MLIPs): Suggested as a near-term solution to replace the two-stage DFT optimization protocol, potentially reducing computational costs while maintaining accuracy for organic molecules.
Deep-Learning Docking: Identified as a complementary approach to empirical scoring functions, though the authors note current limitations in generalizing to under-represented chemotypes (like hypervalent sulfonates) and protein targets.
QSAR and Generative Design: Proposed as logical next steps for prioritizing analogues and screening ADMET properties once an initial validated hit (PTN) is established.

The authors emphasize that for the current study, the validated physics-based DFT and empirical docking protocols remain the most appropriate primary methods, particularly given the specific functional groups in PTN that may challenge current AI training distributions.

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