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
Similar content being viewed by others
References
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
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
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
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
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
Goll J, Uetz P (2008) Analyzing protein interaction networks. Bioinform From Genom Ther 3:1121–1177. https://doi.org/10.1002/9783527619368.ch31
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
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
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
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
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
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
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
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
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
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
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
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
Turnbull AP, Boyd SM, Walse B (2014) Fragment-based drug discovery and protein – protein interactions. Dovepress. https://doi.org/10.2147/RRBC.S28428
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Dannenberg JJ (1998) Book reviews. 123(39), 1009–1011
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
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
Contributions
All the author(s) listed mentioned have made a direct, significant, and conceptual contribution to the work, approving it for publication.
Corresponding author
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.
Rights and permissions
About this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11030-022-10462-0