Introduction

According to the World Health Organization (WHO), depression is one of the most common mental health disorders worldwide. Here, considering an alarming number of mental health patients, which may be three patients behind 20 persons or 5.3% people of all ages suffer from depression globally. ¹ It is characterized by a protracted period of depression, loss of pleasure, or lack of interest in activities. ² All depressive disorders are characterized by feelings of melancholy, emptiness, or irritability, together with physical and cognitive abnormalities that seriously impair the sufferer’s ability to function. ³

The etiology of major depressive disorder is multifactorial, viz. biological, environmental, and genetic, such as changes in brain chemistry, family history of depression, shifting social norms, stress or trauma exposure, social isolation, and family conflicts. ^4,5 Despite these obstacles, a literature survey has found several factors linked to depression in young Indian adults. ⁶

The prime aim of treatment is antidepressants, a popular choice for depression, to relieve the symptoms of severe depression. ⁷ They are also used to stop suicidal thoughts and to relieve symptoms like anxiety, restlessness, and insomnia. ⁸ The way symptoms worsen over time and the likelihood of a relapse of depression will also determine how long treatment takes. Some people use antidepressants for a long time. ⁹

Figure 1: Gatifloxacin structureClick here to view Figure

Hence, researchers worldwide are actively seeking new antidepressant agents. One approach involves modifying existing medications chemically and analyzing medicated substances to identify potential antidepressant properties. ¹⁰ This strategy aims to enhance drug effectiveness and address unmet medical needs in treating depression. ¹¹ Therefore, researchers pointed out, fluoroquinolone compound that is non-opthalmic gatifloxacin, which was banned due to an adverse-effects, namely dysglycemia. The new targeted gatifloxacin derivatives may act as good antidepressant drugs. ¹²

Gatifloxacin is a monocarboxylic acid that is 4-oxo-1,4-dihydroquinoline-3-carboxylic acid. Cyclopropyl groups are substituted on the nitrogen of the compound, and fluoro, 3-methylpiperazin-1-yl, and methoxy groups are substituted at positions 6, 7, and 8, respectively. ^13,14 As an antibiotic belonging to the fourth-generation fluoroquinolone family, gatifloxacin inhibits the bacterial topoisomerase type-II enzymes, just like other antibiotics in the family. It functions as an antimicrobial, an anti-infective. ¹⁵

Notably, serotonin in the brain regulates the mood. It’s often called the body’s natural good or happy chemical. When serotonin is in the normal range, you feel more focused, emotionally stable, happier, and calmer. Serotonin is synthesized from a protein called tryptophan. Hence, serotonin is termed 5-HT (5-hydroxy tryptophan). Low serotonin levels in the brain cause depression, which is worse for health. ¹⁶

At the molecular level, the presynaptic membrane synthesizes and secretes serotonin, reaching into the synapse where available serotonin binds to the serotonin receptor present on the synaptic membrane. Under no more conditions, SLC6A4 allows reabsorption of serotonin in the presynaptic membrane and also brings the Cl- and K+ into extracellular space. The selective serotonin reuptake inhibitors inhibit the reuptake by blocking the SLC6A4 transporter, resulting in more serotonin available to bind the post-synaptic membrane. ^17,18

Protein profile: PDB ID:8FSB

Figure 2: PDB ID: 8FSBClick here to view Figure

Table 1: Macromolecular content¹⁹

Table 2: Chemical composition of 8FSB

PDB ID: 8FSB contain two reference ligands. These two molecules complexed with protein termed as Ligand.

Figure 3: NAG – acetamido-2-deoxy-beta-D-glucopyranose: (C8H15NO6)Click here to view Figure

Figure 4: SRO – SEROTONIN: (C10H12N2O)Click here to view Figure

Table 3: Structure of Gatifloxacin derivativesClick here to view Table

These modifications in gatifloxacin include the conversion of the third carboxylic group of gatifloxacin into an ester group. Further, this ester group is fused with quercetin ²¹, β-sitosterol ²², stigmasterol ²³, lanosterol, kaempherol, and bergenin ²⁴, which reflects anti-depessant activity as per the literature survey.

So, the rationale of hypothesized derivatives was screened for anti-depressant activity through molecular docking simulation. Therefore, it will be beneficial to synthesize particular derivatives that showed the best binding energy with respective proteins also based on drug likeliness property. ²⁵ Thus, hypothesized drug entities are claimed for anti-depressant activity. This research highlights the potential effect of developing more effective antidepressants, which could significantly improve treatment outcomes for individuals suffering from depression. Also, higher binding energies of these derivatives suggest enhanced antidepressant potential compared to gatifloxacin, warranting further exploration for clinical development.

Material and Methods

Molecular docking allows the availability of three-dimensional macromolecular structures, which enables a diligent inspection of the binding site topology.  This computational approach indicated sequence alignment, residue network interaction, binding energy. This different docking softwares were downloaded like

ACDLabs202121_ChemSketchFree_Install

Avogadro-1.2.0n-win32

DS2021Client

com-PerkinElmer_ChemOffice_Suite_v21.0.0.28

5.7_Setup

PYMOL-2.5.2-Windows-x86_64

This fundamental process was initiated by targeting desirable protein from data bank.

Protein selection

Prior to docking, researcher identified appropriate proteins from the Protein Data Bank, based on lower A° value. The initial phase involved by considering the completeness of their data and their correlation with relevant enzymes to produce pharmacological effects. The interested protein was downloaded from protein data bank, PDB ID: 8FSB relevant to anti-depressive activity. ²⁶

Initialize the ligand for docking

Chemdraw

The ligand structure was generated by using ChemDraw software. We selected the ‘structure’ option from the toolbar, performed a ‘check structure’ to identify and then applied ‘3D cleanup’. The resulting structure was saved as ‘Ligand’ in MDL Molfile (*mol) format.”

Pymol

This process involved opening the saved ligand, selecting the ‘file’ option, and choosing ‘Export structure’ as ‘Export molecule.’ This action led to an output window where the file was saved once more, this time in the ‘BIOVIA discovery file’ format.²⁷

Protein Design

The process involved opening the downloaded protein in BIOVIA. From the ‘Chemistry’ option on the toolbar, polar hydrogen groups were added. As a result, it showed strong binding affinity, specificity and binding topology with ligand. Simultaneously, ligand groups and heteroatoms were removed from side windows. By right-clicking, amino acid attributes were copied to the configuration file. Finally, the modified protein was saved as ‘Protein’ in the BIOVIA discovery file format, specifically in the ‘Protein Data Bank File’ format. ²⁸

Docking

AutoDock 1.5.7 is a widely used in receptor-ligand docking simulation program. The docked protein was consequently saved as PDBQT files. The PDBQT files of protein and ligand were formed to showcase protein -ligand docking score. It explores different interaction positions. Specifically, it identifies up to 9 distinct ligand-protein interaction sites by analyzing the main docking folder using specific commands. The first command was vina.exe -help, then second command was vina.exe –config config.txt –log log.txt, it gave binding affinity score. The last command was vina_split.exe –input ligand.out.pdbqt which unveiled 9 possible interactions of protein-ligand complex.

View docking result

Finally, affinity was recorded in the form of kcal/mol and displayed in AutoDock window. The table showed binding energy, lower bound and upper bound root mean square deviation of different 9 positions. So, these 9 positions were dragged into BIOVIA and positions were assessed for H bond interactions. It exhibited depicted conformations with 2 D and 3 D images. The 2D images displayed the pi-pi stacking, covalent bond, vander wall forces, H bond, electrostatic bond and hydrophobic interactions. ²⁹

Result and Discussion

The emphasis on identifying promising therapeutic candidates underscores the interdisciplinary approach, can facilitate faster progress through methodologies such as in silico docking simulations and ADME. The use of in silico molecular docking simulations is a powerful tool in providing insights into the binding affinities of gatifloxacin derivatives to the target protein 8FSB. All derivatives showed a free binding energy score by binding with amino acids of targeted proteins at a specific distance. The reported binding energies in Table 5 indicate that these derivatives have significantly higher binding affinities than gatifloxacin itself, suggesting enhanced potential for therapeutic efficacy.

The link between low serotonin levels and depression is well-established, supporting the rationale for targeting serotonin availability in therapeutic strategies. The role of the serotonin reuptake inhibitor further justifies the investigation of compounds that can inhibit its binding of serotonin at the synaptic nerve, leading to increased serotonin levels in the brain. The promising in silico results highlight the need for further exploration, including in vitro and in vivo studies to assess the efficacy, safety, and pharmacokinetics of gatifloxacin derivatives. This progression is essential to open avenues for whether these compounds can translate from laboratory findings to clinical success.

Table 4: Docking score

If gatifloxacin derivatives demonstrate the desired antidepressant effects in clinical trials, their introduction to the market could provide a new treatment option for depression, addressing an urgent public health need. This potential is especially relevant in light of the rising rates of depression globally, underscoring the importance of innovative therapeutic strategies.

Figure 5: Gati I: 8FSBClick here to view Figure

Gati I exhibit a robust affinity for the protein with PDB ID: 8FSB for antidepressant activity. Specifically, Gati I demonstrated a free binding energy of -11.4 kcal/mol for first binding pose when interacting with 8FSB. The second pose gave -11.4 kcal/mol binding energy had lower bound root-mean-square deviation (RSMD) value is 16.724, while the upper bound RSMD value is 20.444. Notably, Gati I binds to the receptor protein by interacting with the ILE C:268 amino acid residue.

Figure 6: Gati II: 8FSBClick here to view Figure

Here, Gati II displayed -11.1 kcal/mol (first binding pose) binding energy with potential targeted protein as Mono amino oxidase inhibitor. Also, second binding pose showed -11.0 kcal/mol binding affinity recorded lower bound and upper bound root square mean deviation is 2.3222, 3.614 respectively.

Figure 7: Gati III: 8FSBClick here to view Figure

Third derivative of Gatifoxacin namely Gati III indicated -8.2 kcal/mol (first binding pose), -7.9 kcal/mol (second binding pose) binding affinity which had -2.866, 4.471 distance from lower bound and upper bound root square mean deviation. The binding affinity was showed by key protein target with ligand through amino acids namely SER C:170, GLU C:173, ARG C:169, TRP C:168, ARG: C:65

Figure 8: Gati IV: 8FSBClick here to view Figure

The free binding energy was estimated by interlinking of 8FRX protein with Gati IV. The docking score was -8.5 kcal/mol for first binding pose and -7.9 kcal/mol for second binding pose interpreted with lower bound 8.084 and 24.268 upper bound root mean square deviation. The ligand was perfectly docked into sites of protein by PRO C:10, ARG D:31.

Figure 9: Gati V: 8FSBClick here to view Figure

The Gati V showed -10.0 kcal/mol binding affinity for first pose and -9.5 kcal/mol having 1.720 and 2.970 lower and upper root mean square deviation. The possible interlinked amino acid – VAL D: 95.

Figure 10: Gati VI: 8FSBClick here to view Figure

The Gati VI revealed -9.7 kcal/mol for first binding pose as well as -9.6 kcal/mol binding energy displayed root mean square deviation. (lower bound 7.205, upper bound 12.642). The linked amino acids – LYS C: 211.

Figure 11: Gatifloxacin: 8FSBClick here to view Figure

 The Gatifloxacin exhibited -6.9 kcal/mol binding energy for first and second binding pose. Simultaneously second pose recorded root mean square deviation. (lower bound-5.127, upper bound-7.338).

Conclusion

The current circumstances provided, lead identification and optimization have been only possible by computer-aided drug design. This high throughput screening tool is achieved successful insights for exploring the interaction of ligands with key protein. The docked derivatives stood out for their favorable binding with PDB ID: 8FSB via blocking the reuptake of 5HT at synapse. Gatifloxacin exhibits -6.9 kcal/mol binding energy with protein 8FSB. In stark contrast, all derivatives of gatifloxacin showed greater binding affinity than gatifloxacin with targeted protein. Among that, Gati I and Gati II showed -11.4 kcal/mol and -11.1Kcal/mol respectively, which displayed greater protein binding affinity.  Besides, remaining derivatives showed docking score namely Gati III -8.2 kcal/mol, Gati IV 7.9 kcal/mol, Gati V -9.5 kcal/mol and Gati VI has -9.7 kcal/mol. Hence, the predicted better anti-depressant activity of derivatives is perfectly ascribed H bond interactions, amino acid networking and binding affinity score. The above proposed chemical entity of gatifloxacin contains attached drug entity which is already proven for antidepressant activity.

Acknowledgement

I extend my gratitude to research center “Appasaheb Birnale College of Pharmacy, Sangli” for providing AUTODOCK software for molecular docking. 

Funding Sources

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Conflict of Interest

The authors do not have any conflict of interest.

Data Availability Statement

All data generated or analysed during this study are included in this published article and will be made available on reasonable request to the corresponding author.

Ethics Statement

This research did not involve human participants, animal subjects, or any material that requires ethical approval.

Informed Consent Statement

This study did not involve human participants, and therefore, informed consent was not required. 

Author contribution’s statement

PPM (Priyanka Prakash Majalekar) : has done docking study, collected required data and written manuscript as per author instructions.

PJS (Pramodkumar Jaykumar Shirote) : has designed the hypothesis of research work, checked and reviewed thoroughly.

References

1. Michael N, Taren S, Daniel G, Taylor P, Cruz B, Hoek D, Jordan J S, Johan M, Jemima S, Mark M, Rebecca P, Lisa P, Roberta V, Hugh Ar, Benjamin V, Philip P, Stuart B and Chris Lo Effect of exercise for depression: systematic review and network meta-analysis of randomized controlled trials. Journal of British Medical. 2024;384:e075847. Available from: https://doi.org/1136/bmj-2023-075847
   CrossRef
2. Li Z, Ruan M, Chen J, Fang Y. Major Depressive Disorder: Advances in Neuroscience Research and Translational Applications. Neuroscience Bulletin. 2021;37: 863-880. Available from: https://doi.org/10.1007/s12264-021-00638-3
   CrossRef
3. Moncrieff J, Copoper R, Stockmann T, Amendola S, Hengartner M, Horowitz M. The serotonin theory of depression: a systemic umbrella review of the evidence. Molecular Psychiatry. 2023;28:3243-3256. Available from: 1038/s41380-022-01661-0
   CrossRef
4. Lei X, Ji W, Guo J, Wu X, Wang H, Zhu L, et. al. Research on the method depression detection by single – channel electroencephalography sensor. Front Psychiatry. 2022;13:1-11. Available from: https://doi.org?10.3389/fpsyg.2022.850159
   CrossRef
5. Copper K, Gin L, Barnes E, Brownell S, Gardner S. An exploratory study of students with depression in undergraduate research experiences. CBE-Life Sciences Education. 2020;19:1-17. doi: 10.1187/cbe.19-11-0217. Available from: https://doi.org/10.1187/cbe.19-11-0217
   CrossRef
6. Blanco V, Salmeron M, Otero P, Vazquez F. Symptoms of depression, anxiety, and stress and prevalence of major depression and its predictors in female university students. International Journal of Environ Public health. 2021;18: 5845-5857. Available from: 3390/ijerph18115845
   CrossRef
7. Singh B, Olds T, Curtis R, Dumuid D, Watson A, Szeto K, et. al. Effectiveness of physical activity interventions for improving depression, anxiety and distress: an overview of systematic reviews. British Journal of Sports Medicine. 2023;57:1203–1209. Available from: doi:10.1136/bjsports-2022-106195
   CrossRef
8. Wang Y, Ren X, Liu X, Zhu T. Examining the correlation between depression and social behavior on smartphones through usage metadata: empirical study. Advancing digital health and open sciences 2021;9: e19046. Available from: 2196/19046
   CrossRef
9. Wilkowska A, Cubala W. The Downstaging Concept in Treatment-Resistant Depression: Spotlight on Ketamine. International Journal of Molecular Science. 2022;23:14605. Available from: 3390/ijms232314605
   CrossRef
10. Wang H, Tian X, Wang X, Wang Y. Evolution and emerging trends in depression research from 2004 to 2019; A literature visualization analysis. Front Psychiatry. 2021;12:1-20. Available from:      https://doi.org/10.3389/fpsyt.2021.705749
    CrossRef
11. Sheffler Z, Patel P, Abdijadid S. Antidepressants. StatPearls 2023;49: 1-6.
12. VerhoevenJ, Han L, Milligen B, Hu M,  Hoogendoorn H, Batelaan N, et. al. Antidepressants or running therapy: Comparing effects on mental and physical health in patients with depression and anxiety disorders. Journals of Affective Disorders. 2010;329:19-29. https://doi.org/10.1016/j.jad.2023.02.064
    CrossRef
13. Alemi F, Min H, Yousefi M, Becker L, Hane C, Nori Vand Wojtusaik J Effectiveness of common antidepressants: a post market release study. EClinical Medicine. 2021;41;1-8. Available from: https://doi.org/10.1016/j.eclinm.2021.101171
    CrossRef
14. Kesavan K, Nath G, Pandit J. Preparation and in vitro antibacterial evaluation of gatifloxacin mucoadhesive gellan system. Daru 2010;18:237–246.
15. Nie Q, Tao L, Li Y, Chen N, Chen H, Zhou Y, et.al. High-dose gatifloxacin-based shorter treatment regimens for MDR/RR-TB. International Journal of Infectious Disease. 2022;115:142-148. https://doi.org/10.1016/j.ijid.2021.11.037
    CrossRef
16. Edinoff A, Akuly H, Ochoa C, Patil S, Ghaffar Y, Kaye A, Vishhwanat O, Urits I, Boyer G. A, Cornett M. E and Kaye M. Selective Serotonin Reuptake Inhibitors and Adverse Effects: A Narrative Review. International Journal of Neurology. 2021;13:387-401. Available from: 3390/neurolint13030038
    CrossRef
17. Chen-chang J, Zamparelli, Netts M, Pariante C. Antidepressant Drugs: Mechanisms of Action and Side Effects. Antidepressant Drugs: Mechanisms of Action and Side Effects. Encyclopedia Behavioral Neurosciences. 2020;613-626. Available from: 1016/B978-0-12-819641-0.00105-5
    CrossRef
18. Felt K, Stauffer M, Salas-Estrada L, Guzzo PR, Xie D, Huang J, et. al. Structural basis for partial agonism in 5-HT3A receptors. Natural Structure of Molecular Biology. 2024;31:598-609. Available from: 1038/s41594-023-01140-2
    CrossRef
19. https://www.wwpdb.org/pdb?id=pdb_00008fsb
20. https://www.ebi.ac.uk/pdbe/entry/pdb/8fsb/index
21. Chen S, Tang Y, Gao Y, Nie K, Wang H, Su Hao, et. al. Antidepressant potential of quercetin and it’s glycoside derivatives: Acomprehensive review and update. Front Pharmacology. 2022;13: 865376. Available from: 3389/fphar.2022.865376
    CrossRef
22. Yin Y, Liu X, Liu J, Enbo C, Li H, Zhang L, et. al. The effect of beta-sitosterol and it’s derivatives on depression by the modification of 5-HT, DA and GABA system in mice. RCS Advances. 2018;2:671-680. Available from: https://doi.org/10.1039/C7RA11364A
    CrossRef
23. Karim N, Khan I, Abdelhalim A, Halim S, Khan A, Al-Harrasi A. Stigmasterol can be new steroidal drug for neurological disorders: Evidence of the GABAergic mechanism via receptor modulation. Phytomedicine. 2021;90: 153646. Available from: 1016/j.phymed.2021.153646
    CrossRef
24. Mehta S, Kadian V, Dalal S, Dalal P, Kumar S, Garg M, et. al.A Fresh Look on Bergenin: Vision of Its Novel Drug Delivery Systems and Pharmacological Activities. Future Pharmacology. 2022;11:64-91. Available from: https://doi.org/10.3390/futurepharmacol2010006
    CrossRef
25. Torres P, Sodero A, Jofily P, Silva F. Key topics in molecular docking for drug design. Internal Journal of Molecular Sciences. 2019; 20: 4574 –4580. Available from: 3390/ijms20184574
    CrossRef
26. https://www.rcsb.org/structure/8FSB
27. Meng X, Zhang H, Mezei M, Cui M. Recent Advances in Molecular Docking for the Research and Discovery of Potential Marine Drugs. Marine Drugs. 2023;13:13398. Available from: https;//doi.org/10.1038/s41598-023-40160-2.
28. Ruyck J, Brysbaert G,Blossey R, Lensink M. Molecular docking as a popular tool in drug design, an in silico travel. Advances and Application in Bioinformatics and Chemistry. 2016;24:1-11. Available from: 2147/AABC.S105289
    CrossRef
29. Bhagat R, Butle S, Khobragade D, Wankhede S, Prasad C, Mahure D, et. al. Molecular docking in drug discovery. International Journal of Pharmaceutical Research. 2021;33:46-58. Available from: 9734/jpri/2021/v33i30B31639
    CrossRef