Identification of Novel STAT3 Dimerization Inhibitor Through Structure-Based Virtual Screening for Cancer Management

Full Length Research Article 

Identification of Novel STAT3 Dimerization Inhibitor Through Structure-Based Virtual Screening for Cancer Management

Akram Ahmed Aloqbi*

Adv. life sci., vol. 11, no. 2, pp. 508-512, May 2024
*Corresponding Author: Akram Ahmed Aloqbi (
Authors' Affiliations

 Department of Biology, Faculty of Science, University of Jeddah, Jeddah – Saudi Arabia 
[Date Received: 09/12/2023; Date Revised: 10/01/2024; Date Available Online: 18/04/2024]

Abstractaa download_button



Background: Signal transducer and activator of transcription 3 (STAT3) is a transcription factor that controls cell proliferation, differentiation, angiogenesis, and immunological responses. In many human malignancies, abnormal STAT3 activation promotes tumor growth via oncogenic gene expression, resulting in tumor malignancy. Many drugs with clinically authorized analogues that are used as STAT3 inhibitors for cancer therapy have several drawbacks in terms of stability and toxicity.

Methods: This study used PyRx 0.8 tool to screen the Traditional Chinese Medicine (TCM) database of about 32,364 commercially available natural compounds in order to identify new STAT3 inhibitors. Physicochemical and ADME properties of selected compounds were estimated using Datawarrior and SwissADME.

Result: The top 20 compounds were initially chosen based on their strong binding affinities with STAT3. Lipinski and Vaber tools were used to filter the top 20 compounds, yielding the top 6 compounds. The compounds ZINC85542844, ZINC4098720, ZINC85543599, ZINC85593523, ZINC85593528, and ZINC85593537 were passed through these filters. These compounds were found to interact with active site STAT3 residues and have several amino acid interactions in common with the control compound (STX0119).

Conclusion: This study suggests that the compounds ZINC85542844, ZINC4098720, ZINC85543599, ZINC85593523, ZINC85593528, and ZINC85593537 could be used as a lead for the development of novel STAT3 inhibitors. However, further experimental validation is required to optimized them as STAT3 inhibitors.

Keywords: STAT3; Transcription factor; Natural compounds; Cancer; Virtual screening  

Introduction6th button-01

The characteristics of cancer include sustaining proliferative signaling, improper replication with a lack of growth suppressors, initiating angiogenesis, and increasing invasion and metastasis in carcinogenesis [1]. Transcription factors (TFs) are signaling nodes and are among the most commonly affected genes in cancer [2,3]. Signal transducer and activator of transcription (STAT) is a cytoplasmic TF family (STAT1, STAT2, STAT3, STAT4, STAT5a, STAT5b, and STAT6) that is responsible for the transfer of signals from various receptors and non-receptor related kinases to the nucleus [4]. STAT3 activity has been found to be increased and dysregulated in a variety of cancer cell lines [5], indicating that this protein might be a prospective target for anticancer drug development [6-8]. STAT3 controls a number of genes involved in cell proliferation, differentiation, metastasis, and immunity [9,10]. In addition, STAT3 overexpression has been linked to a poor prognosis in some malignancies [11]. Conferring the murine STAT3β crystal structure, pTyr705, located at the boundary of the SH2 and transactivation domains of one STAT3 monomer, binds to the SH2 domain of the other [12]. Because dimerization via reciprocal phosphotyrosine-SH2 interactions is a critical step in STATs activation, small compounds that impair dimer formation make the protein incapable of forming dimers, binding DNA, and activating gene transcription [13]. Disrupting, for example, STAT3 dimer formation provides an effective therapeutic strategy in cancer by inhibiting its abnormal signaling hyperactivity and pro-oncogenic consequences [14].

The time and cost of researching new pharmaceuticals has increased dramatically in past decades. In typically, it takes 15 years and up to $800 million to turn a promising novel molecule into a commercially available drug [15]. Various in silico methods for virtual screening (VS) compounds from virtual chemical spaces, as well as structure and ligand-based methodologies, allow for improved profile analysis, faster removal of nonlead compounds, and therapeutic molecule selection at lower costs [16]. This work employed bioinformatics tools to uncover new possible hits from the Traditional Chinese Medicine (TCM) database that may be used to suppress STAT3 in order to fight cancer.

Methods6th button-01

Retrieval and preparation of the 3-D structure of STAT3

The RCSB protein data bank was queried to acquire the three-dimensional structure of the target protein STAT3 (PDB ID: 1BG1). Since 1BG1 is a homodimer attached to DNA, the DNA and other heteroatoms were deleted, leaving a clean STAT3 for docking experiments. Energy minimization and geometry optimization were performed using Dock Prep, a built-in tool in UCSF Chimera for creating protein 3D structures. The energy was minimized for the 1000 steepest descent steps using a root mean square gradient of 0.02, and an Amber ff12SB force field, and the prepared protein was saved in .pdb format.

Natural compounds library preparation

ZINC database was utilized to construct a collection of natural compounds, integrating information from the Traditional Chinese Medicine (TCM) database, which encompasses 32,364 commercially accessible natural compounds. This library comprises TCM compounds represented in 3D file formats, specifically .sdf, that accurately depict the processed chemical structures of these compounds.

Virtual screening

VS sorts of compound databases using computer technology and drug design theory-based software. Potentially valuable compounds are screened in large numbers in order to find novel leads for further experimental evaluation. The prepared compound library was screened against the STAT3 using PyRx 0.8 tool [17].

In silico Physicochemical and ADME Prediction

To estimate the physicochemical and ADME properties of the selected compounds, Datawarrior and SwissADME ( were employed. These software tools facilitated the assessment and calculation of crucial characteristics of their performance in biological systems and overall pharmacokinetic profiles.

Results6th button-01

This study used molecular docking-based VS to screen 32,364 TCM compounds for possible hits with significant STAT3 binding scores. The top 20 compounds were initially chosen based on their strong binding affinities with STAT3 (Table 1). The process of filtration to the initial set of top 20 compounds was applied using Lipinski and Veber's tools. As a result, the top six compounds were identified and chosen: ZINC85542844, ZINC4098720, ZINC85543599, ZINC85593523, ZINC85593528, and ZINC85593537. These six compounds passed Lipinski and Veber's rigorous criteria, suggesting their potential as prospective candidates for drug development. Table 2 presents a brief assessment of the physicochemical and drug-likeness values of these compounds. Based on the estimated values, these compounds exhibit a favorable drug-likeness and drug score, suggesting their potential as lead compounds in pharmaceutical development.

An intra-molecular interaction analysis was conducted on the top six compounds. ZINC85593528 interacted with Thr456, His457, Asp237, Asp242, Arg245, Ala241, Lys244, Asn485, Pro487, Gln248, Leu459, Gln247, Trp243, Gln326, Val322, and Val323 residues of STAT3 (Figure 1a); while Glu455, Lys318, His457, Ser319, Thr456, Val322, Phe321, Gln247, Val323, Trp243, Gln248, Arg245, Asn485, Ala241, Leu459, Pro487, Gln326, Ser458, and Lys244 residues of STAT3 interacted with ZINC85593537 (Figure 1b). ZINC85542844 was found to interact with Glu455, Lys318, Ser319, Thr236, Asp237, Ala241, Lys244, Asn485, Asn486, Pro487, His457, Lys488, Leu438, Thr440, Asp371, Lys370, and Ser372 residues of STAT3 (Figure 1c). ZINC4098720 interacted with Pro487, Lys244, His457, Phe321, Thr456, Glu455, Leu454, Lys318, Ser319, Val322, Val323, Trp243, Gln247, Gln326, and Leu459 residues of STAT3 (Figure 1d). ZINC85543599 interacted with Gln247, His457, Lys244, Thr456, Phe321, Glu455, Asp237, Ala241, Arg245, Asn485, Leu459, Pro487, Val322, Val323, Trp243, and Gln326 residues of STAT3 (Figure 1e). Furthermore, ZINC85593523 interacted with Asp237, Glu455, Ser319, Lys318, Val322, Trp243, Gln326, Val323, Gln247, Leu459, Thr456, His457, Pro487, Lys244, Thr236, Ala241, Asn486, and Asn485 residues of STAT3 (Figure 1f).

STX-0119 interacted with Val323, Trp243, Gln326, Gln247, Val322, Leu459, Pro487, Thr236, Ser319, Lys244, Phe321, Asp453, Lys318, Leu454, Glu455, Thr456, and His457 residues of STAT3 (Figure 2).

The superimposition of representative structures revealed that the binding pattern and conformational alignment of hits (ZINC85542844, ZINC4098720, ZINC85543599, ZINC85593523, ZINC85593528, and ZINC85593537) in the STAT3 active site were identical to that of STX-0119 (Figure 3).

Figures & Tables



Discussion6th button-01

VS is a popular computational tool for identifying potential compounds against pre-determined biological targets [18]. The study focused on screening TCM compounds against the STAT3 protein. Initially, the selection of the top 20 compounds was based on their robust binding affinities with STAT3. Subsequently, SMARTS tools were utilized to filter this group, resulting in the identification of the top 6 compounds: ZINC85542844, ZINC4098720, ZINC85543599, ZINC85593523, ZINC85593528, and ZINC85593537. These compounds adhered to Lipinski and Veber’s criteria and display potential for future drug development. Moreover, further analysis was conducted on these compounds for an in-depth analysis of their interactions, revealing their binding capabilities with crucial residues of the STAT3 protein. The active site residues of STAT3 have been determined as Lys244, Asp369, Arg382, Arg423, Leu438, Gly442, Thr443, Glu444, Thr456 and His457 [19]. Consistent with this the hits (ZINC85542844, ZINC4098720, ZINC85543599, ZINC85593523, ZINC85593528, and ZINC85593537) were found to interact with these residues of STAT3 protein.

STAT3 protein residues, suggesting that these compounds bind within the same binding pocket of the STAT3 protein, similar to STX-0119.

Computational drug discovery is an effective strategy for speeding up and streamlining the drug discovery and development pipeline, demonstrating efficacy in increasing efficiency and cost-effectiveness [21-23]. In docking analysis, if a chemical has a lower binding affinity than the control, it indicates that the molecule is more active [24,25]. Interestingly, hits (ZINC85542844, ZINC4098720, ZINC85543599, ZINC85593523, ZINC85593528, and ZINC85593537) had higher negative binding affinity values relative to the control (STX-0119), indicating that these hits resulted in tighter binding and could be used as STAT3 inhibitors.

Natural products and structural counterparts have long played important roles in pharmacotherapy, particularly in the treatment of cancer and infectious disorders. With rising health risks, there is an urgent need to investigate preventive and treatment options, emphasizing the critical role of natural products in generating novel medicines and reducing disease spread and mortality. Plant-derived chemicals' therapeutic value has been recognized for decades, resulting in their widespread use in both households and clinics to treat a variety of illnesses [26].

This work used a structure-based drug discovery approach to identify natural compound-like inhibitors of STAT3. The hit compounds ZINC85542844, ZINC4098720, ZINC85543599, ZINC85593523, ZINC85593528, and ZINC85593537 demonstrated strong binding and stability with STAT3 and may serve as a lead for STAT3 inhibition to combat the cancers. However, further research is needed to optimize them as STAT3 inhibitors.

Conflict of Interest

The author declare that there is no conflict of interest.

6th button-01References

  1. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell, (2011); 144(5): 646-674.
  2. Darnell JE, Jr. Transcription factors as targets for cancer therapy. Nature Reviews Cancer, (2002); 2(10): 740-749.
  3. Koppikar P, Bhagwat N, Kilpivaara O, Manshouri T, Adli M, et al. Heterodimeric JAK-STAT activation as a mechanism of persistence to JAK2 inhibitor therapy. Nature, (2012); 489(7414): 155-159.
  4. Yu H, Kortylewski M, Pardoll D. Crosstalk between cancer and immune cells: role of STAT3 in the tumour microenvironment. Nature Reviews Immunology, (2007); 7(1): 41-51.
  5. Avalle L, Camporeale A, Camperi A, Poli V. STAT3 in cancer: A double edged sword. Cytokine, (2017); 98: 42-50.
  6. Masciocchi D, Gelain A, Villa S, Meneghetti F, Barlocco D. Signal transducer and activator of transcription 3 (STAT3): a promising target for anticancer therapy. Future Medicinal Chemistry, (2011); 3(5): 567-597.
  7. Lavecchia A, Di Giovanni C, Cerchia C. Novel inhibitors of signal transducer and activator of transcription 3 signaling pathway: an update on the recent patent literature. Expert Opinion on Therapeutic Patents, (2014); 24(4): 383-400.
  8. Lavecchia A, Di Giovanni C, Novellino E. STAT-3 inhibitors: state of the art and new horizons for cancer treatment. Current Medicinal Chemistry, (2011); 18(16): 2359-2375.
  9. Frank DA. STAT3 as a central mediator of neoplastic cellular transformation. Cancer Letters, (2007); 251(2): 199-210.
  10. Aggarwal BB, Kunnumakkara AB, Harikumar KB, Gupta SR, Tharakan ST, et al. Signal transducer and activator of transcription-3, inflammation, and cancer: how intimate is the relationship? Annals of the New York Academy of Sciences, (2009); 1171: 59-76.
  11. Johnston PA, Grandis JR. STAT3 signaling: anticancer strategies and challenges. Molecular Interventions, (2011); 11(1): 18-26.
  12. Becker S, Groner B, Muller CW. Three-dimensional structure of the Stat3beta homodimer bound to DNA. Nature, (1998); 394(6689): 145-151.
  13. Bromberg JF, Horvath CM, Besser D, Lathem WW, Darnell JE, Jr. Stat3 activation is required for cellular transformation by v-src. Molecular and Cellular Biology, (1998); 18(5): 2553-2558.
  14. Turkson J, Jove R. STAT proteins: novel molecular targets for cancer drug discovery. Oncogene, (2000); 19(56): 6613-6626.
  15. DiMasi JA, Hansen RW, Grabowski HG. The price of innovation: new estimates of drug development costs. Journal of Health Economics, (2003); 22(2): 151-185.
  16. Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today, (2019); 24(3): 773-780.
  17. Dallakyan S, Olson AJ. Small-molecule library screening by docking with PyRx. Methods in Molecular Biology, (2015); 1263: 243-250.
  18. Yadav DK, Kumar S, Choi EH, Chaudhary S, Kim MH. Computational Modeling on Aquaporin-3 as Skin Cancer Target: A Virtual Screening Study. Frontiers in Chemistry, (2020); 8: 250.
  19. Khanam R, Hejazi, II, Shahabuddin S, Bhat AR, Athar F. Pharmacokinetic evaluation, molecular docking and in vitro biological evaluation of 1, 3, 4-oxadiazole derivatives as potent antioxidants and STAT3 inhibitors. Journal of Pharmaceutical Analysis, (2019); 9(2): 133-141.
  20. Matsuno K, Masuda Y, Uehara Y, Sato H, Muroya A, et al. Identification of a New Series of STAT3 Inhibitors by Virtual Screening. ACS Medicinal Chemistry Letters, (2010); 1(8): 371-375.
  21. Tarique M, Ahmad S, Malik A, Ahmad I, Saeed M, et al. Correction to: Novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) and Other Coronaviruses: A Genome-wide Comparative Annotation and Analysis. Molecular and Cellular Biochemistry, (2022); 477(2): 645.
  22. Stanzione F, Giangreco I, Cole JC. Use of molecular docking computational tools in drug discovery. Progress in Medicinal Chemistry, (2021); 60: 273-343.
  23. Brogi S. Computational Approaches for Drug Discovery. Molecules, (2019); 24(17): 3061.
  24. Chang MW, Ayeni C, Breuer S, Torbett BE. Virtual screening for HIV protease inhibitors: a comparison of AutoDock 4 and Vina. PLoS One, (2010); 5(8): e11955.
  25. Sayed Murad HA, M MR, Alqahtani SM, B SR, Alghamdi S, et al. Molecular docking analysis of AGTR1 antagonists. Bioinformation, (2023); 19(3): 284-289.
  26. Atanasov AG, Zotchev SB, Dirsch VM; International Natural Product Sciences Taskforce; Supuran CT. Natural products in drug discovery: advances and opportunities. Nature Reviews Drug Discovery, (2021); 20(3): 200-216.

This work is licensed under a Creative Commons Attribution-Non Commercial 4.0 International License. To read the copy of this license please visit:

6th button-01