Skip to main content
Log in

Designing of disruptor molecules to restrain the protein–protein interaction network of VANG1/SCRIB/NOS1AP using fragment-based drug discovery techniques

  • Original Article
  • Published:
Molecular Diversity Aims and scope Submit manuscript

Abstract

Governing protein–protein interaction networks are the cynosure of cell signaling and oncogenic networks. Multifarious processes when aligned with one another can result in a dysregulated output which can result in cancer progression. In the current research, one such network of proteins comprising VANG1/SCRIB/NOS1AP, which is responsible for cell migration, is targeted. The proteins are modeled using in-silico approaches, and the interaction is visualized utilizing protein–protein docking. Designing drugs for the convoluted protein network can serve as a challenging task that can be overcome by fragment-based drug designing, a recent game-changer in the computational drug discovery strategy for protein interaction networks. The model is exposed to the extraction of hotspots, also known as the restrained regions for small molecular hits. The hotspot regions are subjected to a library of generated fragments, which are then recombined and rejoined to develop small molecular disruptors of the macromolecular assemblage. Rapid screening methods using pharmacokinetic tools and 2D interaction studies resulted in four molecules that could serve the purpose of a disruptor. The final validation is executed by long-range simulations of 100 ns and exploring the stability of the complex using several parameters leading to the emergence of two novel molecules VNS003 and VNS005 that could be used as the disruptors of the protein assembly VANG1/SCRIB/NOS1AP. Also, the molecules were explored as single protein targets approbated via molecular docking and 100 ns molecular dynamics simulation. This concluded VNS003 as the most suitable inhibitor module capable of acting as a disruptor of a macromolecular assembly as well as acting on individual protein chains, thus leading to the primary hindrance in the formation of the protein interaction complex.

Graphic abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Boriack-Sjodin PA, Margarit SM, Bar-Sagi D, Kuriyan J (1998) The structural basis of the activation of Ras by Sos. Nature 394(6691):337–343. https://doi.org/10.1038/28548

    Article  CAS  PubMed  Google Scholar 

  2. Iii FA, Ladurner AG, Inouye C, Tjian R, Nogales E (1999) Three-dimensional structure of the human TFIID-IIA-IIB complex. Science (80-). 286:2153–2157

    Article  Google Scholar 

  3. Lu H et al (2020) Recent advances in the development of protein–protein interactions modulators: mechanisms and clinical trials. Signal Transduct Target Ther. https://doi.org/10.1038/s41392-020-00315-3

    Article  PubMed  PubMed Central  Google Scholar 

  4. Cruz DL et al (2014) Condição bucal e estado nutricional de pacientes de clínicas oral. Rev Univap São José dos Campos-SP-Brasil 21(37):21–30. https://doi.org/10.1016/j.tips.2013.04.007.Targeting

    Article  Google Scholar 

  5. Wells JA, McClendon CL (2007) Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature 450(7172):1001–1009. https://doi.org/10.1038/nature06526

    Article  CAS  PubMed  Google Scholar 

  6. Goll J, Uetz P (2008) Analyzing protein interaction networks. Bioinform From Genom Ther 3:1121–1177. https://doi.org/10.1002/9783527619368.ch31

    Article  Google Scholar 

  7. Hennessy BT, Smith DL, Ram PT, Lu Y, Mills GB (2005) Exploiting the PI3K/AKT pathway for cancer drug discovery. Nat Rev Drug Discov 4(12):988–1004. https://doi.org/10.1038/nrd1902

    Article  CAS  PubMed  Google Scholar 

  8. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144(5):646–674. https://doi.org/10.1016/j.cell.2011.02.013

    Article  CAS  PubMed  Google Scholar 

  9. Fu J et al (2011) The TWIST/Mi2/NuRD protein complex and its essential role in cancer metastasis. Cell Res 21(2):275–289. https://doi.org/10.1038/cr.2010.118

    Article  CAS  PubMed  Google Scholar 

  10. Anastas JN et al (2012) A protein complex of SCRIB, NOS1AP and VANGL1 regulates cell polarity and migration, and is associated with breast cancer progression. Oncogene 31(32):3696–3708. https://doi.org/10.1038/onc.2011.528

    Article  CAS  PubMed  Google Scholar 

  11. Bonello TT, Peifer M (2019) Scribble: a master scaffold in polarity, adhesion, synaptogenesis, and proliferation. J Cell Biol 218(3):742–756. https://doi.org/10.1083/jcb.201810103

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Wang J, Jin L, Zhu Y, Zhou X, Yu R, Gao S (2016) Research progress in NOS1AP in neurological and psychiatric diseases. Brain Res Bull 125:99–105. https://doi.org/10.1016/j.brainresbull.2016.05.014

    Article  CAS  PubMed  Google Scholar 

  13. Bilder D, Li M, Perrimon N (2000) Cooperative regulation of cell polarity and growth by Drosophila tumor suppressors. Science (80-). 289(5476):113–116. https://doi.org/10.1126/science.289.5476.113

    Article  CAS  Google Scholar 

  14. Hatakeyama J, Wald JH, Printsev I, Ho HYH, Carraway KL (2014) Vangl1 and Vangl2: Planar cell polarity components with a developing role in cancer. Endocr Relat Cancer 21(5):345–356. https://doi.org/10.1530/ERC-14-0141

    Article  CAS  Google Scholar 

  15. Hussein UK et al (2021) Scrib is involved in the progression of ovarian carcinomas in association with the factors linked to epithelial-to-mesenchymal transition and predicts shorter survival of diagnosed patients. Biomolecules 11(3):1–18. https://doi.org/10.3390/biom11030405

    Article  CAS  Google Scholar 

  16. Peyravian N et al (2021) Increased expression of vangl1 is predictive of lymph node metastasis in colorectal cancer: Results from a 20-gene expression signature. J Pers Med 11(2):1–22. https://doi.org/10.3390/jpm11020126

    Article  Google Scholar 

  17. Wang X, Ni D, Liu Y, Lu S (2021) Rational Design of Peptide-Based Inhibitors Disrupting Protein-Protein Interactions. Front Chem 9(May):1–15. https://doi.org/10.3389/fchem.2021.682675

    Article  CAS  Google Scholar 

  18. Laraia L, McKenzie G, Spring DR, Venkitaraman AR, Huggins DJ (2015) Overcoming Chemical, Biological, and Computational Challenges in the Development of Inhibitors Targeting Protein-Protein Interactions. Chem Biol 22(6):689–703. https://doi.org/10.1016/j.chembiol.2015.04.019

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Turnbull AP, Boyd SM, Walse B (2014) Fragment-based drug discovery and protein – protein interactions. Dovepress. https://doi.org/10.2147/RRBC.S28428

    Article  Google Scholar 

  20. Doak BC, Norton RS, Scanlon MJ (2016) The ways and means of fragment-based drug design. Pharmacol Ther 167:28–37. https://doi.org/10.1016/j.pharmthera.2016.07.003

    Article  CAS  PubMed  Google Scholar 

  21. Li Q (2020) Application of fragment-based drug discovery to versatile targets. Front Mol Biosci 7(August):1–13. https://doi.org/10.3389/fmolb.2020.00180

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kidd SL, Osberger TJ, Mateu N, Sore HF, Spring DR (2018) Recent applications of diversity-oriented synthesis Toward novel, 3-dimensional fragment collections. Front Chem. https://doi.org/10.3389/fchem.2018.00460

    Article  PubMed  PubMed Central  Google Scholar 

  23. Vargas C et al (2014) Small-molecule inhibitors of AF6 PDZ-mediated protein-protein interactions. ChemMedChem 9(7):1458–1462. https://doi.org/10.1002/cmdc.201300553

    Article  CAS  PubMed  Google Scholar 

  24. Colovos C, Yeates TO (1993) Verification of protein structures: Patterns of nonbonded atomic interactions. Protein Sci 2(9):1511–1519. https://doi.org/10.1002/pro.5560020916

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Laskowski RA, Rullmann JAC, MacArthur MW, Kaptein R, Thornton JM (1996) AQUA and PROCHECK-NMR: Programs for checking the quality of protein structures solved by NMR. J Biomol NMR 8(4):477–486. https://doi.org/10.1007/BF00228148

    Article  CAS  PubMed  Google Scholar 

  26. Wiederstein M, Sippl MJ (2007) ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 35:407–410. https://doi.org/10.1093/nar/gkm290

    Article  Google Scholar 

  27. Chen VB et al (2010) MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr Sect D Biol Crystallogr 66(1):12–21. https://doi.org/10.1107/S0907444909042073

    Article  CAS  Google Scholar 

  28. Krieger E et al (2009) Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8. Proteins Struct Funct Bioinforma 77(SUPPL. 9):114–122. https://doi.org/10.1002/prot.22570

    Article  CAS  Google Scholar 

  29. González-Alemán R, Hernández-Castillo D, Caballero J, Montero-Cabrera LA (2020) Quality threshold clustering of molecular dynamics: a word of caution. J Chem Inf Model 60(2):467–472. https://doi.org/10.1021/acs.jcim.9b00558

    Article  CAS  PubMed  Google Scholar 

  30. De Vries SJ, Van Dijk M, Bonvin AMJJ (2010) The HADDOCK web server for data-driven biomolecular docking. Nat Protoc 5(5):883–897. https://doi.org/10.1038/nprot.2010.32

    Article  CAS  PubMed  Google Scholar 

  31. Darnell SJ, LeGault L, Mitchell JC (2008) KFC server: interactive forecasting of protein interaction hot spots. Nucleic Acids Res. 36:265–269. https://doi.org/10.1093/nar/gkn346

    Article  CAS  Google Scholar 

  32. Kozakov D et al (2015) The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins. Nat Protoc 10(5):733–755. https://doi.org/10.1038/nprot.2015.043

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Chi CN, Bach A, Strømgaard K, Gianni S, Jemth P (2012) Ligand binding by PDZ domains. BioFactors 38(5):338–348. https://doi.org/10.1002/biof.1031

    Article  CAS  PubMed  Google Scholar 

  34. Wang NX, Lee HJ, Zheng JJ (2008) Therapeutic use of PDZ protein-protein interaction antagonism. Drug News Perspect 21(3):137–141. https://doi.org/10.1358/dnp.2008.21.3.1203409

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Abraham MJ et al (2015) Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25. https://doi.org/10.1016/j.softx.2015.06.001

    Article  Google Scholar 

  36. Gajula M, Kumar A, Ijaq J (2016) Protocol for molecular dynamics simulations of proteins. Bio-Protoc 6(23):1–11. https://doi.org/10.21769/bioprotoc.2051

    Article  CAS  Google Scholar 

  37. Lemkul J (2019) From proteins to perturbed hamiltonians: a suite of tutorials for the GROMACS-2018 molecular simulation package [Article v1.0]. Living J Comput Mol Sci 1(1):1–53. https://doi.org/10.33011/livecoms.1.1.5068

    Article  Google Scholar 

  38. Roy A, Kucukural A, Zhang Y (2010) I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 5(4):725–738. https://doi.org/10.1038/nprot.2010.5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Vakser IA (2014) Protein-protein docking: from interaction to interactome. Biophys J 107(8):1785–1793. https://doi.org/10.1016/j.bpj.2014.08.033

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Dennis S, Kortvelyesi T, Vajda S (2002) Computational mapping identifies the binding sites of organic solvents on proteins. Proc Natl Acad Sci U S A 99(7):4290–4295. https://doi.org/10.1073/pnas.062398499

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Pierce AC, Rao G, Bemis GW (2004) BREED: generating novel inhibitors through hybridization of known ligands. Application to CDK2, P38, and HIV protease. J Med Chem 47(11):2768–2775. https://doi.org/10.1021/jm030543u

    Article  CAS  PubMed  Google Scholar 

  42. Vuppala PK (2013) Importance of ADME and bioanalysis in the drug discovery. J Bioequiv Availab 05(04):4–5. https://doi.org/10.4172/jbb.10000e31

    Article  Google Scholar 

  43. Någren K (2003) PET and knockout mice in drug discovery. Drug Discov Today 8(19):876. https://doi.org/10.1016/S1359-6446(03)02765-X

    Article  PubMed  Google Scholar 

  44. Kufareva I, Abagyan R (2012) Methods of protein structure comparison. Methods Mol Biol 857:231–257. https://doi.org/10.1007/978-1-61779-588-6_10

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Dannenberg JJ (1998) Book reviews. 123(39), 1009–1011

  46. Reif MM, Oostenbrink C (2014) Net charge changes in the calculation of relative ligand-binding free energies via classical atomistic molecular dynamics simulation. J Comput Chem 35(3):227–243. https://doi.org/10.1002/jcc.23490

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to dedicate our acknowledgment to the Department of Biosciences and Bioengineering, along with the Centre for Nanotechnology at IIT Guwahati. We would like to thank the School of Health Science and Technology, Param-Ishan, and BSL facilities of IIT Guwahati. Also, a sincere acknowledgment is directed toward Schrodinger and Senior Scientist Dr. Prajwal Nandekar, for licensing the use of the software.

Author information

Authors and Affiliations

Authors

Contributions

All the author(s) listed mentioned have made a direct, significant, and conceptual contribution to the work, approving it for publication.

Corresponding author

Correspondence to Siddhartha Sankar Ghosh.

Ethics declarations

Conflict of interest

The author(s) declare that there was no potential conflict of interest that could influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (DOCX 2980 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Acharyya, S.R., Sen, P., Kandasamy, T. et al. Designing of disruptor molecules to restrain the protein–protein interaction network of VANG1/SCRIB/NOS1AP using fragment-based drug discovery techniques. Mol Divers 27, 989–1010 (2023). https://doi.org/10.1007/s11030-022-10462-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11030-022-10462-0

Keywords

Navigation