Review on Computer-Aided Drug Design in RAS Inhibitor Discovery

Year : 2024 | Volume : 11 | Issue : 03 | Page : 7 14
    By

    Mohd. Wasiullah,

  • Piyush Yadav,

  • Alqama Zehra,

  • Satish kumar Yadav,

  • Sushil Yadav,

  1. Principal, Department of Pharmacy, Prasad Institute of Technology, Uttar Pradesh, India
  2. Academic Head, Department of Pharmacy, Prasad Institute of Technology, Uttar Pradesh, India
  3. Research Scholar, Department of Pharmacy, Prasad Institute of Technology, Uttar Pradesh, India
  4. Associate Professor, Department of Pharmacy, Prasad Institute of Technology, Uttar Pradesh, India
  5. Lecturer, Department of Pharmacy, Prasad Institute of Technology, Uttar Pradesh, India

Abstract

This review explores the utilization of computer-aided drug design (CADD) methodologies in the discovery of inhibitors targeting the RAS pathway, a pivotal signaling cascade implicated in various cancers. Through an extensive examination of computational tools, methodologies, challenges, and recent advancements, this review aims to provide insights into the role of CADD in accelerating RAS inhibitor discovery. The discovery of effective inhibitors targeting the RAS pathway is of paramount importance in cancer therapeutics, given the critical role of RAS proteins in oncogenesis. Computer-aided drug design (CADD) methodologies have emerged as indispensable tools in accelerating the identification and optimization of RAS inhibitors. This review provides a comprehensive overview of the applications of CADD in RAS inhibitor discovery, encompassing molecular modeling, virtual screening, machine learning, and structural biology techniques. Through an exploration of recent advancements, challenges, and opportunities in the field, this review aims to elucidate the potential of CADD in driving innovation and accelerating the development of novel therapeutics for RAS- driven cancers.

Keywords: RAS pathway, computer-aided drug design (CADD), RAS inhibitors, molecular modelling, virtual screening, machine learning, structural biology, cancer therapeutics, precision oncology, drug discovery

[This article belongs to Research & Reviews: A Journal of Drug Design & Discovery ]

How to cite this article:
Mohd. Wasiullah, Piyush Yadav, Alqama Zehra, Satish kumar Yadav, Sushil Yadav. Review on Computer-Aided Drug Design in RAS Inhibitor Discovery. Research & Reviews: A Journal of Drug Design & Discovery. 2024; 11(03):7-14.
How to cite this URL:
Mohd. Wasiullah, Piyush Yadav, Alqama Zehra, Satish kumar Yadav, Sushil Yadav. Review on Computer-Aided Drug Design in RAS Inhibitor Discovery. Research & Reviews: A Journal of Drug Design & Discovery. 2024; 11(03):7-14. Available from: https://journals.stmjournals.com/rrjoddd/article=2024/view=185508


References

  1. Kanwal S, Jamil F, Ali A, Sehgal SA. Comparative modeling, molecular docking, and revealing of potential binding pockets of RASSF2; a candidate cancer gene. Interdiscip Sci Comput Life Sci. 2017;9:214–223. doi:10.1007/s12539-016-0145-z.
  2. Tuncbag N, Gursoy A, Nussinov R, Keskin O. Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM. Nat Protoc. 2011;6(9):1341–1354. doi:10.1038/nprot.2011.367.
  3. Collier TA, Piggot TJ, Allison JR. Molecular Dynamics Simulation of Proteins. In: Gerrard J, Domigan L, editors. Protein Nanotechnology. Methods in Molecular Biology. New York: Humana; 2020. 311–327. doi:10.1007/978-1-4939-9869-2_17.
  4. Li L, Meyer C, Zhou ZW, Elmezayen A, Westover K. Therapeutic targeting the allosteric cysteinome of RAS and kinase families. J Mol Biol. 2022;434(17):167626. doi:10.1016/j.jmb.2022.167626.
  5. Prakash P, Sayyed-Ahmad A, Cho KJ, Dolino DM, Chen W, Li H, et al. Computational and biochemical characterization of two partially overlapping interfaces and multiple weak-affinity K-Ras dimers. Sci Rep. 2017;7(1):40109. doi:10.1038/srep40109.
  6. Muratcioglu S, Chavan TS, Freed BC, Jang H, Khavrutskii L, Freed RN, et al. GTP-dependent K-Ras dimerization. Struct. 2015;23(7):1325–1335. doi:10.1016/j.str.2015.04.019.
  7. Jisna VA, Jayaraj PB. Protein structure prediction: conventional and deep learning perspectives. Protein J. 2021;40(4):522–544. doi:10.1007/s10930-021-10003-y.
  8. Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, et al. Improved protein structure prediction using potentials from deep learning. Nat. 2020;577(7792):706–710. doi:10.1038/s41586-019-1923-7.
  9. Bhaskar BV, Rammohan A, Babu TM, Zheng GY, Chen W, Rajendra W, et al. Molecular insight into isoform specific inhibition of PI3K-α and PKC-η with dietary agents through an ensemble pharmacophore and docking studies. Sci Rep. 2021;11(1):12150. doi:10.1038/s41598-021-90287-3.
  10. Parca L, Mangone I, Gherardini PF, Ausiello G, Helmer-Citterich M. Phosfinder: a web server for the identification of phosphate-binding sites on protein structures. Nucleic acids research. 2011;39(suppl_2):W278–282. doi:10.1093/nar/gkr389.
  11. Singh H, Srivastava HK, Raghava GP. A web server for analysis, comparison and prediction of protein ligand binding sites. Biol Direct. 2016;11:1–4. doi:10.1186/s13062-016-0118-5.
  12. Cox AD, Der CJ. Ras history: The saga continues. Small GTPases. 2010;1(1):2–27. doi:10.4161/sgtp.1.1.12178.
  13. Konc J, Skrlj B, Erzen N, Kunej T, Janezic D. GenProBiS: web server for mapping of sequence variants to protein binding sites. Nucleic Acids Res. 2017;45(W1):W253–259. doi:10.1093/nar/gkx420.
  14. Wang Y, Lupala CS, Liu H, Lin X. Identification of drug binding sites and action mechanisms with molecular dynamics simulations. Curr Top Med Chem. 2018;18(27):2268–2277. doi:10.2174/1568026619666181212102856.
  15. Zimmermann G, Papke B, Ismail S, Vartak N, Chandra A, Hoffmann M, et al. Small molecule inhibition of the KRAS–PDEδ interaction impairs oncogenic KRAS signalling. Nat. 2013;497(7451):638–642. doi:10.1038/nature12205.
  16. Ostrem JM, Peters U, Sos ML, Wells JA, Shokat KM. K-Ras (G12C) inhibitors allosterically control GTP affinity and effector interactions. Nat. 2013;503(7477):548–551. doi:10.1038/nature12796.
  17. Canon J, Rex K, Saiki AY, Mohr C, Cooke K, Bagal D, et al. The clinical KRAS (G12C) inhibitor AMG 510 drives anti-tumour immunity. Nat. 2019;575(7781):217–223. doi:10.1038/s41586-019-1694-1.
  18. Hernandez M, Ghersi D, Sanchez R. SITEHOUND-web: a server for ligand binding site identification in protein structures. Nucleic Acids Res. 2009;37(suppl_2):W413–416. doi:10.1093/nar/gkp281.
  19. Davis MI, Gross S, Shen M, Straley KS, Pragani R, Lea WA, et al. Biochemical, cellular, and biophysical characterization of a potent inhibitor of mutant isocitrate dehydrogenase IDH1. J Biol Chem. 2014;289(20):13717–13125. doi:10.1074/jbc.M113.511030.
  20. Zhu M, Gribskov M. MiPepid: MicroPeptide identification tool using machine learning. BMC Bioinform. 2019;20:1–1. doi:10.1186/s12859-019-3033-9.
  21. Langer MF, Goeßmann A, Rupp M. Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning. Comput Mater. 2022;8(1):41. doi:10.1038/s41524-022-00721-x.
  22. Keith JA, Vassilev-Galindo V, Cheng B, Chmiela S, Gastegger M, Muller KR, et al. Combining machine learning and computational chemistry for predictive insights into chemical systems. Chem Rev. 2021;121(16):9816–98172. doi:10.1021/acs.chemrev.1c00107.
  23. Nguyen TH, Thai QM, Pham MQ, Minh PT, Phung HT. Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds. Mol Divers. 2024;28(2):553–561. doi:10.1007/s11030-023-10601-1.

Regular Issue Subscription Review Article
Volume 11
Issue 03
Received 10/05/2024
Accepted 22/10/2024
Published 02/11/2024


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