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Identification of allosteric inhibitor against AKT1 through structure-based virtual screening

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Abstract

AKT (serine/threonine protein kinase) is a potential therapeutic target for many types of cancer as it plays a vital role in cancer progression. Many AKT inhibitors are already in practice under single and combinatorial therapy. However, most of these inhibitors are orthosteric / pan-AKT that are non-selective and non-specific to AKT kinase and their isoforms. Hence, researchers are searching for novel allosteric inhibitors that bind in the interface between pH and kinase domain. In this study, we performed structure-based virtual screening from the afroDB (a diverse natural compounds library) to find the potential inhibitor targeting the AKT1. These compounds were filtered through Lipinski, ADMET properties, combined with a molecular docking approach to obtain the 8 best compounds. Then we performed molecular dynamics simulation for apoprotein, AKT1 with 8 complexes, and AKT1 with the positive control (Miransertib). Molecular docking and simulation analysis revealed that Bianthracene III (hit 1), 10-acetonyl Knipholonecyclooxanthrone (hit 2), Abyssinoflavanone VII (hit 5) and 8-c-p-hydroxybenzyldiosmetin (hit 6) had a better binding affinity, stability, and compactness than the reference compound. Notably, hit 1, hit 2 and hit 5 had molecular features required for allosteric inhibition.

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References

  1. Bhullar KS, Lagarón NO, McGowan EM et al (2018) Kinase-targeted cancer therapies: Progress, challenges and future directions. Mol Cancer 17:1–20

    Article  Google Scholar 

  2. Changqing Xu, Yang Liu GZ (2022) The development of 3-substituted indolin-2-one derivatives as kinase inhibitors for cancer therapy. Curr Med Chem 29:1891–1919. https://doi.org/10.2174/0929867328666210831142311

    Article  CAS  Google Scholar 

  3. Martina veit Acousta WF de AJ, (2022) Computational prediction of binding affinity for CDK2-ligand complexes. a protein target for cancer drug discovery. Curr Med Chem 29:2438–2455. https://doi.org/10.2174/0929867328666210806105810

    Article  CAS  Google Scholar 

  4. Zhen li, Fang liu, Shuang Wu, Shi Ding, Ye chen JL, (2022) Research progress on the drug resistance of ALK kinase inhibitors. Curr Med Chem 29:2456–2475. https://doi.org/10.2174/0929867328666210806120347

    Article  CAS  Google Scholar 

  5. Revathidevi S, Munirajan AK (2019). Seminars in Cancer Biology Akt in cancer: Mediator and more. https://doi.org/10.1016/j.semcancer.2019.06.002

    Article  Google Scholar 

  6. Uko NE, Güner OF, Phillip Bowen J, Matesic DF (2019) Akt Pathway Inhibition of the Solenopsin Analog, 2-Dodecylsulfanyl-1,-4,-5,-6-tetrahydropyrimidine. Anticancer Res 39:5329–5338. https://doi.org/10.21873/anticanres.13725

  7. Xu F, Na L, Li Y, Chen L (2020) Roles of the PI3K / AKT / mTOR signalling pathways in neurodegenerative diseases and tumours. Cell Biosci. https://doi.org/10.1186/s13578-020-00416-0

    Article  PubMed  PubMed Central  Google Scholar 

  8. Rehan M, Beg MA, Parveen S et al (2014) Computational insights into the inhibitory mechanism of human AKT1 by an orally active inhibitor, MK-2206. PLoS ONE 9:18–22. https://doi.org/10.1371/journal.pone.0109705

    Article  CAS  Google Scholar 

  9. West KA, Castillo SS, Dennis PA (2002) Activation of the PI3K/Akt pathway and chemotherapeutic resistance. Drug Resist Updates 5:234–248. https://doi.org/10.1016/S1368-7646(02)00120-6

    Article  CAS  Google Scholar 

  10. Zhang LL, Zhang J, Shen L et al (2013) Overexpression of AKT decreases the chemosensitivity of gastric cancer cells to cisplatin in vitro and in vivo. Mol Med Rep 7:1387–1390. https://doi.org/10.3892/mmr.2013.1400

    Article  CAS  PubMed  Google Scholar 

  11. Yee PS, Gan CP, Zainal NS et al (2017) Resistance to dasatinib is associated with the activation of Akt in oral squamous cell carcinoma. Translational Research in Oral Oncology. https://doi.org/10.1177/2057178x17702920

    Article  Google Scholar 

  12. Revathidevi S, Munirajan AK (2019) Akt in cancer: Mediator and more. Semin Cancer Biol 59:80–91. https://doi.org/10.1016/j.semcancer.2019.06.002

    Article  CAS  PubMed  Google Scholar 

  13. Mundi PS, Sachdev J, McCourt C, Kalinsky K (2016) AKT in cancer: new molecular insights and advances in drug development. Br J Clin Pharmacol. https://doi.org/10.1111/bcp.13021

    Article  PubMed  PubMed Central  Google Scholar 

  14. Kumar CC, Madison V (2005) AKT crystal structure and AKT-specific inhibitors. Oncogene 24:7493–7501. https://doi.org/10.1038/sj.onc.1209087

    Article  CAS  PubMed  Google Scholar 

  15. Mattmann ME, Stoops SL, Lindsley CW (2011) Inhibition of Akt with small molecules and biologics: Historical perspective and current status of the patent landscape. Expert Opin Ther Pat 21:1309–1338. https://doi.org/10.1517/13543776.2011.587959

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Toker A, Marmiroli S (2014) Signaling specificity in the Akt pathway in biology and disease. Adv Biol Regul 55:28–38. https://doi.org/10.1016/j.jbior.2014.04.001

    Article  CAS  PubMed  Google Scholar 

  17. Easton RM, Cho H, Roovers K et al (2005) Role for Akt3/protein kinase bγ in attainment of normal brain size. Mol Cell Biol 25:1869–1878. https://doi.org/10.1128/mcb.25.5.1869-1878.2005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Nitulescu GM, Margina D, Juzenas P et al (2016) Akt inhibitors in cancer treatment: The long journey from drug discovery to clinical use (Review). Int J Oncol 48:869–885. https://doi.org/10.3892/ijo.2015.3306

    Article  CAS  PubMed  Google Scholar 

  19. Wenthur CJ, Gentry PR, Mathews TP, Lindsley CW (2014) Drugs for allosteric sites on receptors. Annu Rev Pharmacol Toxicol 54:165–184. https://doi.org/10.1146/annurev-pharmtox-010611-134525

    Article  CAS  PubMed  Google Scholar 

  20. Vivanco I, Chen ZC, Tanos B et al (2014) A kinase-independent function of AKT promotes cancer cell survival. Elife 3:1–13. https://doi.org/10.7554/eLife.03751

    Article  Google Scholar 

  21. Hirai H, Sootome H, Nakatsuru Y et al (2010) MK-2206, an allosteric akt inhibitor, enhances antitumor efficacy by standard chemotherapeutic agents or molecular targeted drugs in vitro and in vivo. Mol Cancer Ther 9:1956–1967. https://doi.org/10.1158/1535-7163.MCT-09-1012

    Article  CAS  PubMed  Google Scholar 

  22. Brown JS, Banerji U (2017) Maximising the potential of AKT inhibitors as anti-cancer treatments. Pharmacol Ther 172:101–115. https://doi.org/10.1016/j.pharmthera.2016.12.001

    Article  CAS  PubMed  Google Scholar 

  23. Calleja V, Laguerre M, Parker PJ, Larijani B (2009) Role of a novel PH-kinase domain interface in PKB/Akt regulation: Structural mechanism for allosteric inhibition. PLoS Biol. https://doi.org/10.1371/journal.pbio.1000017

    Article  PubMed  PubMed Central  Google Scholar 

  24. ClinicalTrials.gov. https://clinicaltrials.gov/.

  25. Coleman N, Moyers JT, Harbery A et al (2021) Clinical development of AKT inhibitors and associated predictive biomarkers to guide patient treatment in cancer medicine. Pharmgenomics Pers Med 14:1517–1535. https://doi.org/10.2147/PGPM.S305068

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Newman DJ, Cragg GM (2020) Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019. J Nat Prod 83:770–803. https://doi.org/10.1021/acs.jnatprod.9b01285

    Article  CAS  PubMed  Google Scholar 

  27. Ntie-Kang F, Zofou D, Babiaka SB et al (2013) AfroDb: a select highly potent and diverse natural product library from African medicinal plants. PLoS ONE 8:1–15. https://doi.org/10.1371/journal.pone.0078085

    Article  CAS  Google Scholar 

  28. Kumar V, Krishna S, Imran M (2014) Virtual screening strategies : Recent advances in the identification and design of anti-cancer agents. Methods. https://doi.org/10.1016/j.ymeth.2014.08.010

    Article  PubMed  PubMed Central  Google Scholar 

  29. Gagic Z, Ruzic D, Djokovic N et al (2020) In silico methods for design of kinase inhibitors as anticancer drugs. Front Chem 7:1–25. https://doi.org/10.3389/fchem.2019.00873

    Article  CAS  Google Scholar 

  30. Lazaro G, Kostaras E, Vivanco I (2020) Inhibitors in AKTion: ATP-competitive vs allosteric. Biochem Soc Trans 48:933–943. https://doi.org/10.1042/BST20190777

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Wu WI, Voegtli WC, Sturgis HL et al (2010) Crystal structure of human AKT1 with an allosteric inhibitor reveals a new mode of kinase inhibition. PLoS ONE. https://doi.org/10.1371/journal.pone.0012913

    Article  PubMed  PubMed Central  Google Scholar 

  32. Lapierre JM, Eathiraj S, Vensel D et al (2016) Discovery of 3-(3-(4-(1-Aminocyclobutyl)phenyl)-5-phenyl-3H-imidazo[4,5-b]pyridin-2-yl)pyridin-2-amine (ARQ 092): an orally bioavailable, selective, and potent allosteric AKT inhibitor. J Med Chem 59:6455–6469. https://doi.org/10.1021/acs.jmedchem.6b00619

    Article  CAS  PubMed  Google Scholar 

  33. Ashwell MA, Lapierre J-M, Brassard C, Karen Bresciano CB, Cornell-Kennon S, Eathiraj S, France DS, Hall T, Jason Hill EK, Khanapurkar S, Kizer D, Koerner S, Link J, Liu Y, Sapna Makhija MM et al (2012) Discovery and optimization of a series of 3-(3-Phenyl-3Himidazo[4,5-b]pyridin-2-yl)pyridin-2-amines: orally bioavailable, selective, and potent ATP-Independent Akt inhibitors. J Med Chem 55:5291–5310. https://doi.org/10.1021/jm300276x

    Article  CAS  PubMed  Google Scholar 

  34. Webb B, Sali A (2014) Comparative protein structure modeling using MODELLER

  35. Lee C, Su B-H, Tseng YJ (2022) Comparative studies of AlphaFold, RoseTTAFold and Modeller: a case study involving the use of G-protein-coupled receptors. Brief Bioinform 23:1–7. https://doi.org/10.1093/bib/bbac308

    Article  CAS  Google Scholar 

  36. Makarov V (2002) Computer programs for eukaryotic gene prediction. Brief Bioinform 3:195–199. https://doi.org/10.1093/bib/3.2.195

    Article  PubMed  Google Scholar 

  37. SAVESv6.0 - Structure Validation Server. https://saves.mbi.ucla.edu/. Accessed 20 Mar 2022

  38. Wallner B, Elofsson A (2003) Can correct protein models be identified? Protein Sci 12:1073–1086. https://doi.org/10.1110/ps.0236803

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Boyle NMO, Banck M, James CA, et al (2011) Open Babel : An open chemical toolbox. 1–14

  40. Ntie-Kang F, Telukunta KK, Döring K et al (2017) NANPDB: A resource for natural products from Northern African sources. J Nat Prod 80:2067–2076. https://doi.org/10.1021/acs.jnatprod.7b00283

    Article  CAS  PubMed  Google Scholar 

  41. Joshi T, Sharma P, Joshi T et al (2021) Structure-based screening of novel lichen compounds against SARS Coronavirus main protease (Mpro) as potentials inhibitors of COVID-19. Mol Divers 25:1665–1677. https://doi.org/10.1007/s11030-020-10118-x

    Article  CAS  PubMed  Google Scholar 

  42. Morris GM, Ruth H, Lindstrom W et al (2009) Software news and updates AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791. https://doi.org/10.1002/jcc.21256

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Adasme MF, Linnemann KL, Bolz SN et al (2021) PLIP 2021: Expanding the scope of the protein-ligand interaction profiler to DNA and RNA. Nucleic Acids Res 49:W530–W534. https://doi.org/10.1093/nar/gkab294

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Ra L, MB S, (2011) LigPlot+: multiple ligand-protein interaction diagrams for drug discovery. J Chem Inf Model 51:2778–2786

    Article  Google Scholar 

  45. Pires DEV, Blundell TL, Ascher DB (2015) pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem 58:4066–4072. https://doi.org/10.1021/acs.jmedchem.5b00104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Drwal MN, Banerjee P, Dunkel M et al (2014) ProTox: A web server for the in silico prediction of rodent oral toxicity. Nucleic Acids Res 42:53–58. https://doi.org/10.1093/nar/gku401

    Article  CAS  Google Scholar 

  47. Daina A, Michielin O, Zoete V (2017) SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7:1–13. https://doi.org/10.1038/srep42717

    Article  Google Scholar 

  48. Kohnke B, Kutzner C, Grubmüller H (2020) A GPU-accelerated fast multipole method for GROMACS: performance and accuracy. J Chem Theory Comput 16:6938–6949. https://doi.org/10.1021/acs.jctc.0c00744

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Zoete V, Cuendet MA, Aurélien Grosdidier OM (2012) SwissParam: A fast force field generation tool for small organic molecules. J Comput Chem 32:2359

    Article  Google Scholar 

  50. Humphrey W, Dalke A, KST, (1996) VMD: Visual Molecular Dynamics. J Mol Graph 14:33–38. https://doi.org/10.1016/0263-7855(96)00018-5

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Li SG, Yang KS, Blankenship LR et al (2022) An enhanced hybrid screening approach to identify potent inhibitors for the SARS-CoV-2 main protease from the NCI compound library. Front Chem 10:1–9. https://doi.org/10.3389/fchem.2022.816576

    Article  CAS  Google Scholar 

  53. Sharma S, Deep S (2022) In-silico drug repurposing for targeting SARS-CoV-2 main protease (Mpro). J Biomol Struct Dyn 40:3003–3010. https://doi.org/10.1080/07391102.2020.1844058

    Article  CAS  PubMed  Google Scholar 

  54. Hazafa A, Rehman KU, Jahan N, Jabeen Z (2020) The role of polyphenol (flavonoids) compounds in the treatment of cancer cells. Nutr Cancer 72:386–397. https://doi.org/10.1080/01635581.2019.1637006

    Article  CAS  PubMed  Google Scholar 

  55. Wang Y, Ma W, ZHENG W, (2013) Deguelin, a novel anti-tumorigenic agent targeting apoptosis, cell cycle arrest and anti-angiogenesis for cancer chemoprevention. Mol Clin Oncol 1:215–219. https://doi.org/10.3892/mco.2012.36

    Article  PubMed  Google Scholar 

  56. Yan W, Yang J, Tang H et al (2019) Flavonoids from the stems of Millettia pachyloba Drake mediate cytotoxic activity through apoptosis and autophagy in cancer cells. J Adv Res 20:117–127. https://doi.org/10.1016/j.jare.2019.06.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Lee CJ, Jang JH, Lee JY et al (2015) Aschantin targeting on the kinase domain of mammalian target of rapamycin suppresses epidermal growth factor-induced neoplastic cell transformation. Carcinogenesis 36:1223–1234. https://doi.org/10.1093/carcin/bgv113

    Article  CAS  PubMed  Google Scholar 

  58. Bhat SA, Henry RJ, Blanchard AC et al (2021) Enhanced Akt/GSK-3β/CREB signaling mediates the anti-inflammatory actions of mGluR5 positive allosteric modulators in microglia and following traumatic brain injury in male mice. J Neurochem 156:225–248. https://doi.org/10.1111/jnc.14954

    Article  CAS  PubMed  Google Scholar 

  59. Esubalew ST, Belete A, Lulekal E et al (2017) Review of ethnobotanical and ethnopharmacological evidences of some ethiopian medicinal plants traditionally used for the treatment of cancer. Ethiopian J Health Develop 31:161–187

    Google Scholar 

  60. Kumar K, Woo SM, Siu T et al (2018) Cation-π interactions in protein-ligand binding: Theory and data-mining reveal different roles for lysine and arginine. Chem Sci 9:2655–2665. https://doi.org/10.1039/c7sc04905f

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Koltai T, Fliegel L (2022) Role of silymarin in cancer treatment: facts, hypotheses, and questions. J Evid Based Integr Med 27:1–38. https://doi.org/10.1177/2515690X211068826

    Article  Google Scholar 

  62. Wu Y, Xu J, Liu Y et al (2020) A review on anti-tumor mechanisms of coumarins. Front Oncol 10:1–11. https://doi.org/10.3389/fonc.2020.592853

    Article  Google Scholar 

  63. Garg M, Chaudhary SK, Goyal A et al (2022) Comprehensive review on therapeutic and phytochemical exploration of diosmetin: A promising moiety. Phytomedicine Plus 2:100179. https://doi.org/10.1016/j.phyplu.2021.100179

    Article  Google Scholar 

  64. Sarfraz A, Javeed M, Shah MA et al (2020) Biochanin A: A novel bioactive multifunctional compound from nature. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2020.137907

    Article  PubMed  Google Scholar 

  65. Lu S, Zhang J (2017) Allosteric modulators. Comprehensive. Med Chem III 2:276–296. https://doi.org/10.1016/b978-0-12-409547-2.12324-8

    Article  CAS  Google Scholar 

  66. Li X, Chen Y, Lu S et al (2013) Toward an understanding of the sequence and structural basis of allosteric proteins. J Mol Graph Model 40:30–39. https://doi.org/10.1016/j.jmgm.2012.12.011

    Article  CAS  PubMed  Google Scholar 

  67. Smith RD, Lu J, Carlson HA (2017) Are there physicochemical differences between allosteric and competitive ligands? PLoS Comput Biol 13:1–18. https://doi.org/10.1371/journal.pcbi.1005813

    Article  CAS  Google Scholar 

  68. Pragna Lakshmi T, Kumar A, Vijaykumar V et al (2017) Identification of natural allosteric inhibitor for Akt1 protein through computational approaches and in vitro evaluation. Int J Biol Macromol 96:200–213. https://doi.org/10.1016/j.ijbiomac.2016.12.025

    Article  CAS  PubMed  Google Scholar 

  69. Gu X, Wang Y, Wang M, et al (2021) Computational investigation of imidazopyridine analogs as protein kinase B (Akt1) allosteric inhibitors by using 3D-QSAR, molecular docking and molecular dynamics simulations

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  1. School of Biosciences and Technology, Vellore Institute of Technology, Vellore, 632014, India

    Keerthana Karunakaran & Rajiniraja Muniyan

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  1. Keerthana Karunakaran
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Study conceptualization, design, data acquisition, data analysis, data interpretation, manuscript preparation and editing was done by Keerthana Karunakaran. Supervision, data analysis and interpretation, manuscript editing and revision was done by Rajiniraja muniyan.

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Correspondence to Rajiniraja Muniyan.

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Karunakaran, K., Muniyan, R. Identification of allosteric inhibitor against AKT1 through structure-based virtual screening. Mol Divers 27, 2803–2822 (2023). https://doi.org/10.1007/s11030-022-10582-7

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