Identifying potential ligand molecules EGFR mediated TNBC targeting the kinase domain-identification of customized drugs through in silico methods

Hima Vyshnavi , Krishnan Namboori

Abstract


Background and Purpose: Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer in which three hormone receptors are negative. This work aimed at identifying customized potential molecules inhibiting epidermal growth factor receptor (EGFR) by exploring variants using the pharmacogenomics approaches.

Experimental approach: The pharmacogenomics approach has been followed to identify the genetic variants across the 1000 genomes continental population. Model proteins for the populations have been designed by including genetic variants in the reported positions. The 3D structures of the mutated proteins have been generated through homology modeling. The kinase domain present in the parent and the model protein molecules has been investigated. The docking study has been performed with the protein molecules against the kinase inhibitors evaluated by the molecular dynamic simulation studies. Molecular evolution has been performed to generate the potential derivatives of these kinase inhibitors suitable for the conserved region of the kinase domain. This study considered variants within the kinase domain as the sensitive region and remaining residues as the conserved region.

Findings/Results: The results reveal that few kinase inhibitors interact with the sensitive region. Among the derivatives of these kinase inhibitors molecules, the potential kinase inhibitor that interacts with the different population models has been identified

Conclusions and implications: This study encompasses the importance of genetic variants in drug action as well as in the design of customized drugs. This research gives way to designing customized potential molecules inhibiting EGFR by exploring variants using the pharmacogenomics approaches.


Keywords


Conserved region; EGFR; Kinase domain; Sensitive region; TNBC.

Full Text:

PDF

References


Alkabban FM, Ferguson T. Breast Cancer. Treasure Island (FL): StatPearls Publishing; 2022. pp. 1-29. Available from: https://www.ncbi.nlm.nih.gov/books /NBK482286/.

Selase A, Cynthia AD, Newman O, Williams A, Michael O. Palmatine sensitizes chemoresistant triple negative breast cancer cells via efflux inhibition of Multidrug resistant protein 1. Sci Afr. 2021;14:(e01022),1-8. DOI: 10.1016/j.sciaf.2021.e01022.

Al-Mahmood S, Sapiezynski J, Garbuzenko OB, Minko T. Metastatic and triple-negative breast cancer: challenges and treatment options. Drug Deliv Transl Res. 2018;8(5):1483-1507.DOI: 10.1007/s13346-018-0551-3.

Sepahdar Z, Miroliaei M, Bouzari S, Khalaj V, Salimi M. Surface engineering of Escherichia coli-derived OMVs as promising nano-carriers to target EGFR-overexpressing breast cancer cells. Front Pharmacol. 2021;12:719289,1-16. DOI: 10.3389/fphar.2021.719289.

You KS, Yi YW, Cho J, Park JS, Seong YS. Potentiating therapeutic effects of epidermal growth factor receptor inhibition in triple-negative breast cancer. Pharmaceuticals (Basel). 2021;14(6): 589,1-76. DOI: 10.3390/ph14060589.

Sebastiani P, Timofeev N, Dworkis DA, Perls TT, Steinberg MH. Genome-wide association studies and the genetic dissection of complex traits. Am J Hematol. 2009;84(8):504-515. DOI: 10.1002/ajh.21440.

Fragomeni SM, Sciallis A, Jeruss JS. Molecular subtypes and local-regional control of breast cancer. Surg Oncol Clin N Am. 2018;27(1):95-120. DOI: 10.1016/j.soc.2017.08.005.

Riera C, Padilla N, de la Cruz X. The complementarity between protein-specific and general pathogenicity predictors for amino acid substitutions. Hum Mutat. 2016;37(10):1013-1024. DOI: 10.1002/humu.23048.

Vaser R, Adusumalli S, Leng SN, Sikic M, Ng PC. SIFT missense predictions for genomes. Nat Protoc. 2016;11(1):1-9. DOI: 10.1038/nprot.2015.123.

Lu Guanting, Ma Liya, Xu Pei, Xian Binqiang, Wu Lianying, Ding Jianying, et al. A de novo ZMIZ1 pathogenic variant for neurodevelopmental disorder with dysmorphic facies and distal skeletal anomalies. Front Genet. 2022;13:840577,1-14. DOI: 10.3389/fgene.2022.840577.

Rentzsch P, Schubach M, Shendure J, Kircher M. CADD-splice-improving genome-wide variant effect prediction using deep learning-derived splice scores. Genome Med. 2021;13(1):31,1-12. DOI: 10.1186/s13073-021-00835-9.

McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, et al. The ensembl variant effect predictor. Genome Biol. 2016;17(1):122,1-14. DOI: 10.1186/s13059-016-0974-4.

Bhattacharya R, Rose PW, Burley SK, Prlić A. Impact of genetic variation on three-dimensional structure and function of proteins. PLoS One. 2017;12(3):e0171355,1-22. DOI: 10.1371/journal.pone.0171355.

Bhullar KS, Lagarón NO, McGowan EM, Parmar I, Jha A, Hubbard BP, et al. Kinase-targeted cancer therapies: progress, challenges and future directions. Mol Cancer. 2018;17(1):48,1-20. DOI: 10.1186/s12943-018-0804-2.

Szklarczyk D, Santos A, von Mering C, Jensen LJ, Bork P, Kuhn M. STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res. 2016;44(D1):D380-D384. DOI: 10.1093/nar/gkv1277

Muhseen ZT, Kadhim S, Yahiya YI, Alatawi EA, Aba Alkhayl FF, Almatroudi A. Insights into the binding of receptor-binding domain (RBD) of SARS-CoV-2 wild type and B.1.620 variant with hACE2 using molecular docking and simulation approaches. Biology (Basel). 2021;10(12):1310,1-15. DOI: 10.3390/biology10121310.

Yousuf Z, Iman K, Iftikhar N, Mirza MU. Structure-based virtual screening and molecular docking for the identification of potential multi-targeted inhibitors against breast cancer. Breast cancer (Dove Med Press). 2017;9:447-459. DOI: 10.2147/BCTT.S132074.

Acharya, R, Chacko, S, Bose P, Lapenna A, Pattanayak SP. Structure based multitargeted molecular docking analysis of selected furanocoumarins against breast cancer. Sci Rep. 2019;9(1):15743,1-13. DOI: 10.1038/s41598-019-52162-0.

Maffucci I, Hu X, Fumagalli V, Contini A. An efficient implementation of the Nwat-MMGBSA method to rescore docking results in medium-throughput virtual screenings. Front Chem. 2018;6:43,1-14. DOI: 10.3389/fchem.2018.00043.

Fatriansyah JF, Rizqillah RK, Yandi MY, Fadilah, Sahlan M. Molecular docking and dynamics studies on propolis sulabiroin-A as a potential inhibitor of SARS-CoV-2. J King Saud Univ Sci. 2022;34(1):101707,1-9. DOI: 10.1016/j.jksus.2021.101707.

Zafar F, Gupta A, Thangavel K, Khatana K, Sani AA, Ghosal A, et al. Physicochemical and pharmacokinetic analysis of anacardic acid derivatives. ACS Omega. 2020;5(11):6021-6030.DOI: 10.1021/acsomega.9b04398.

PK. Design and development of a pharmacogenomic model for breast cancer to study the variation in drug action and side effects. Int J Appl Pharm. 2022;14(3):61-68. DOI: 10.22159/ijap.2022v14i3.44356.

National Center for Biotechnology Information (NCBI). Bethesda (MD): National Library of Medicine (US). Updated to 2022. Available from: https://www.ncbi.nlm.nih.gov/gene/1956.

The UniProt Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 2021;49(D1):D480-D489.DOI: 10.1093/nar/gkaa1100.

Safran M, Rosen N, Twik M, BarShir R, Iny Stein T, Dahary D, et al. The genecards suite. In: Abugessaisa I, Kasukawa T, editors. Practical guide to life science databases. Springer;2022. pp: 27-56. DOI: 10.1007/978-981-16-5812-9_2.

Sherry ST, Ward MH, Kholodov, M Baker, J Phan, L Smigielski, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29(1):308-311. DOI: 10.1093/nar/29.1.308.

Martina M, Acquadro A, Barchi L, Gulino D, Brusco F, Rabaglio M, et al. Genome-wide survey and development of the first microsatellite Markers database (AnCorDB) in Anemone coronaria L. Int J Mol Sci. 2022;23(6):3126,1-17. DOI: 10.3390/ijms23063126.

Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The protein data bank. Nucleic Acids Res. 2000;28(1):235-242. DOI: 10.1093/nar/28.1.235.

Bienert S, Waterhouse A, de Beer TA, Tauriello G, Studer G, Bordoli L, et al. The SWISS-MODEL repository-new features and functionality. Nucleic Acids Res. 2017;45(D1):D313-D319. DOI: 10.1093/nar/gkw1132.

Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci. 1993;2(9):1511-1519. DOI: 10.1002/pro.5560020916.

National Institute of Mental Health. (2011). Borderline personality. DHHS Publication No. 11-7790. Washington, DC: U.S. Government Printing Office. Available from: www.cancer.gov/about-cancer/treatment/drugs/breast

National Comprehensive Cancer Network (NCCN) guidelines. Genetic/familial high-risk assessment: breast, ovarian and pancreatic. Version 2.2022. 2022. Available from: https://www.nccn.org/guidelines/ guidelines-detail?category=1&id=1419.

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498-2504. DOI: 10.1101/gr.1239303.

Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, et al. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem. 2006;49(21):6177-6196. DOI: 10.1021/jm051256o.

Kim S, Thiessen PA, Bolton EE, Chen, J, Fu G, Gindulyte A, et al. PubChem substance and compound databases. Nucleic Acids Res. 2016;44(D1):D1202-D1213. DOI: 10.1093/nar/gkv951.

Phillips JC, Hardy DJ, Maia JDC, Stone JE, Ribeiro JV, Bernardi RC, et al. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J Chem Phys. 2020;153(4):044130,1-34. DOI: 10.1063/5.0014475.

Genheden S, Ryde U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov. 2015;10(5): 449-461.

DOI: 10.1517/17460441.2015.1032936.

Brai A, Riva V, Clementi L, Falsitta L, Zamperini C, Sinigiani V, et al. Targeting DDX3X helicase activity with BA103 shows promising therapeutic effects in preclinical glioblastoma models. Cancers. 2021;13(21):5569,1-26. DOI: 10.3390/cancers13215569.

Alluri P, Newman LA. Basal-like and triple-negative breast cancers: searching for positives among many negatives. Surg Oncol Clin N Am. 2014;23(3):567-577. DOI: 10.1016/j.soc.2014.03.003.

Petrelli F, Cabiddu M, Ghilardi M, Barni S. Current data of targeted therapies for the treatment of triple-negative advanced breast cancer: empiricism or evidence-based? Expert Opin Investig Drugs. 2009;18(10):1467-1477. DOI: 10.1517/13543780903222268.

Chakrabarty A, Chakraborty S, Bhattacharya R, Chowdhury G. Senescence-induced chemoresistance in triple negative breast cancer and evolution-based treatment strategies. Front Oncol. 2021;11:674354,1-14. DOI: 10.3389/fonc.2021.674354.

Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27-30.DOI: 10.1093/nar/28.1.27.

MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, et al. The new NHGRI-EBI catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 2017;45(D1): D896-D901. DOI: 10.1093/nar/gkw1133.


Refbacks

  • There are currently no refbacks.


Creative Commons LicenseThis work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License which allows users to read, copy, distribute and make derivative works for non-commercial purposes from the material, as long as the author of the original work is cited properly.