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Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK

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KRAS

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2797))

Abstract

Molecular docking is a popular computational tool in drug discovery. Leveraging structural information, docking software predicts binding poses of small molecules to cavities on the surfaces of proteins. Virtual screening for ligand discovery is a useful application of docking software. In this chapter, using the enigmatic KRAS protein as an example system, we endeavor to teach the reader about best practices for performing molecular docking with UCSF DOCK. We discuss methods for virtual screening and docking molecules on KRAS. We present the following six points to optimize our docking setup for prosecuting a virtual screen: protein structure choice, pocket selection, optimization of the scoring function, modification of sampling spheres and sampling procedures, choosing an appropriate portion of chemical space to dock, and the choice of which top scoring molecules to pick for purchase.

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References

  1. Kuntz ID, Blaney JM, Oatley SJ et al (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161:269–288

    Article  CAS  PubMed  Google Scholar 

  2. Balius TE, Tan YS, Chakrabarti M (2024) DOCK 6: incorporating hierarchical traversal through precomputed ligand conformations to enable large-scale docking. J Comput Chem 45:47–63. https://doi.org/10.1002/jcc.27218

  3. London N, Miller RM, Krishnan S et al (2014) Covalent docking of large libraries for the discovery of chemical probes. Nat Chem Biol 10:1066–1072

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Bender BJ, Gahbauer S, Luttens A et al (2021) A practical guide to large-scale docking. Nat Protoc 16:4799–4832

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Kamal IM, Chakrabarti S (2023) MetaDOCK: a combinatorial molecular docking approach. ACS Omega 8:5850–5860

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Pagadala NS, Syed K, Tuszynski J (2017) Software for molecular docking: a review. Biophys Rev 9:91–102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Zev S, Raz K, Schwartz R et al (2021) Benchmarking the ability of common docking programs to correctly reproduce and score binding modes in SARS-CoV-2 protease Mpro. J Chem Inf Model 61:2957–2966

    Article  CAS  PubMed  Google Scholar 

  8. Shuya N, Yoshiharu M, Shigenori T (2022) End-to-end protein-ligand complex structure generation with diffusion-based generative models. bioRxiv. https://doi.org/10.1101/2022.12.20.521309

  9. Dara S, Dhamercherla S, Jadav SS et al (2022) Machine learning in drug discovery: a review. Artif Intell Rev 55:1947–1999

    Article  PubMed  Google Scholar 

  10. Vamathevan J, Clark D, Czodrowski P et al (2019) Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 18:463–477

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Kitchen DB, Decornez H, Furr JR et al (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935–949

    Article  CAS  PubMed  Google Scholar 

  12. Chen P, Ke Y, Lu Y et al (2019) DLIGAND2: an improved knowledge-based energy function for protein-ligand interactions using the distance-scaled, finite, ideal-gas reference state. J Cheminform 11:52

    Article  PubMed  PubMed Central  Google Scholar 

  13. Guedes IA, Pereira FSS, Dardenne LE (2018) Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges. Front Pharmacol 9:1089

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Liu J, Wang R (2015) Classification of current scoring functions. J Chem Inf Model 55:475–482

    Article  CAS  PubMed  Google Scholar 

  15. Meli R, Morris GM, Biggin PC (2022) Scoring functions for protein-ligand binding affinity prediction using structure-based deep learning: a review. Front Bioinform 2:885983

    Article  PubMed  PubMed Central  Google Scholar 

  16. Torres PHM, Sodero ACR, Jofily P et al (2019) Key topics in molecular docking for drug design. Int J Mol Sci 20:4574

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. McNutt AT, Francoeur P, Aggarwal R et al (2021) GNINA 1.0: molecular docking with deep learning. J Cheminform 13:43

    Article  PubMed  PubMed Central  Google Scholar 

  18. Jiang H, Fan M, Wang J et al (2020) Guiding conventional protein-ligand docking software with convolutional neural networks. J Chem Inf Model 60:4594–4602

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Pereira JC, Caffarena ER, Dos Santos CN (2016) Boosting docking-based virtual screening with deep learning. J Chem Inf Model 56:2495–2506

    Article  CAS  PubMed  Google Scholar 

  20. Mathieu et al., KRAS G12C fragment screening renders new binding pockets, Small GTPases, 13:1, 225–238, https://doi.org/10.1080/21541248.2021.1979360

  21. Liao J, Shima F, Araki M et al (2008) Two conformational states of Ras GTPase exhibit differential GTP-binding kinetics. Biochem Biophys Res Commun 369:327–332

    Article  CAS  PubMed  Google Scholar 

  22. Spoerner M, Hozsa C, Poetzl JA et al (2010) Conformational states of human rat sarcoma (Ras) protein complexed with its natural ligand GTP and their role for effector interaction and GTP hydrolysis. J Biol Chem 285:39768–39778

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ye M, Shima F, Muraoka S et al (2005) Crystal structure of M-Ras reveals a GTP-bound “off” state conformation of Ras family small GTPases. J Biol Chem 280:31267–31275

    Article  CAS  PubMed  Google Scholar 

  24. Shima F, Ijiri Y, Muraoka S et al (2010) Structural basis for conformational dynamics of GTP-bound Ras protein. J Biol Chem 285:22696–22705

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kalbitzer HR, Spoerner M, Ganser P et al (2009) Fundamental link between folding states and functional states of proteins. J Am Chem Soc 131:16714–16719

    Article  CAS  PubMed  Google Scholar 

  26. Chao F-A, Chan AH, Dharmaiah S et al (2023) Reduced dynamic complexity allows structure elucidation of an excited state of KRASG13D. Commun Biol 6:594

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Shima F, Yoshikawa Y, Ye M et al (2013) In silico discovery of small-molecule Ras inhibitors that display antitumor activity by blocking the Ras-effector interaction. Proc Natl Acad Sci USA 110:8182–8187

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Rosnizeck IC, Filchtinski D, Lopes RP et al (2014) Elucidating the mode of action of a typical Ras state 1(T) inhibitor. Biochemistry 53:3867–3878

    Article  CAS  PubMed  Google Scholar 

  29. Rosnizeck IC, Graf T, Spoerner M et al (2010) Stabilizing a weak binding state for effectors in the human ras protein by cyclen complexes. Angew Chem Int Ed Engl 49:3830–3833

    Article  CAS  PubMed  Google Scholar 

  30. Rosnizeck IC, Spoerner M, Harsch T et al (2012) Metal-bis(2-picolyl)amine complexes as state 1(T) inhibitors of activated Ras protein. Angew Chem Int Ed Engl 51:10647–10651

    Article  CAS  PubMed  Google Scholar 

  31. Ostrem JM, Peters U, Sos ML et al (2013) K-Ras(G12C) inhibitors allosterically control GTP affinity and effector interactions. Nature 503:548–551

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. McCormick F (2020) Sticking it to KRAS: covalent inhibitors enter the clinic. Cancer Cell 37:3–4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Broker J, Waterson AG, Smethurst C et al (2022) Fragment optimization of reversible binding to the switch II pocket on KRAS leads to a potent, in vivo active KRAS(G12C) inhibitor. J Med Chem 65:14614–14629

    Article  PubMed  PubMed Central  Google Scholar 

  34. Wang X, Allen S, Blake JF et al (2022) Identification of MRTX1133, a noncovalent, potent, and selective KRAS(G12D) inhibitor. J Med Chem 65:3123–3133

    Article  CAS  PubMed  Google Scholar 

  35. Kim D, Herdeis L, Rudolph D et al (2023) Pan-KRAS inhibitor disables oncogenic signalling and tumour growth. Nature 619:160

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Kwan AK, Piazza GA, Keeton AB et al (2022) The path to the clinic: a comprehensive review on direct KRAS(G12C) inhibitors. J Exp Clin Cancer Res 41:27

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Punekar SR, Velcheti V, Neel BG et al (2022) The current state of the art and future trends in RAS-targeted cancer therapies. Nat Rev Clin Oncol 19:637–655

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Rosen JC, Sacher A, Tsao MS (2023) Direct GDP-KRAS(G12C) inhibitors and mechanisms of resistance: the tip of the iceberg. Ther Adv Med Oncol 15:17588359231160141

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Dong L, Qu X, Zhao Y et al (2021) Prediction of binding free energy of protein-ligand complexes with a hybrid molecular mechanics/generalized born surface area and machine learning method. ACS Omega 6:32938–32947

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Kollman PA, Massova I, Reyes C et al (2000) Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res 33:889–897

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Lorber DM, Shoichet BK (1998) Flexible ligand docking using conformational ensembles. Protein Sci 7:938–950

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Wang Z, Sun H, Yao X et al (2016) Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power. Phys Chem Chem Phys 18:12964–12975

    Article  CAS  PubMed  Google Scholar 

  44. Warren GL, Andrews CW, Capelli AM et al (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912–5931

    Article  CAS  PubMed  Google Scholar 

  45. Xu M, Shen C, Yang J et al (2022) Systematic investigation of docking failures in large-scale structure-based virtual screening. ACS Omega 7:39417–39428

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Meng EC, Shoichet BK, Kuntz ID (1992) Automated docking with grid-based energy evaluation. J Comput Chem 13:505–524

    Article  CAS  Google Scholar 

  47. Coleman RG, Carchia M, Sterling T et al (2013) Ligand pose and orientational sampling in molecular docking. PLoS One 8:e75992

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Mysinger MM, Shoichet BK (2010) Rapid context-dependent ligand desolvation in molecular docking. J Chem Inf Model 50:1561–1573

    Article  CAS  PubMed  Google Scholar 

  49. Shoichet BK, Leach AR, Kuntz ID (1999) Ligand solvation in molecular docking. Proteins 34:4–16

    Article  CAS  PubMed  Google Scholar 

  50. Balius TE, Fischer M, Stein RM et al (2017) Testing inhomogeneous solvation theory in structure-based ligand discovery. Proc Natl Acad Sci USA 114:E6839–E6846

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Gu S, Smith MS, Yang Y et al (2021) Ligand strain energy in large library docking. J Chem Inf Model 61:4331–4341

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Fischer M, Coleman RG, Fraser JS et al (2014) Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery. Nat Chem 6:575–583

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Balius TE, Mukherjee S, Rizzo RC (2011) Implementation and evaluation of a docking-rescoring method using molecular footprint comparisons. J Comput Chem 32:2273–2289

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Lang PT, Brozell SR, Mukherjee S et al (2009) DOCK 6: combining techniques to model RNA-small molecule complexes. RNA 15:1219–1230

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Allen WJ, Balius T, Bickel J et al (2023) DOCK 6.10 users manual. [cited May 3, 2023]. Available from https://dock.compbio.ucsf.edu/DOCK_6/dock6_manual.htm

  56. Mukherjee S, Balius TE, Rizzo RC (2010) Docking validation resources: protein family and ligand flexibility experiments. J Chem Inf Model 50:1986–2000

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Allen WJ, Rizzo RC (2014) Implementation of the Hungarian algorithm to account for ligand symmetry and similarity in structure-based design. J Chem Inf Model 54:518–529

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Knight IS, Naprienko S, Irwin JJ (2022) Enrichment Score: a better quantitative metric for evaluating the enrichment capacity of molecular docking models. arXiv:2210.10905. https://doi.org/10.48550/arXiv.2210.10905

  59. Knight IS, Mailhot O, Tang KG, Irwin JJ, (2024) DockOpt: A tool for automatic optimization of docking models. J Chem Inf Model Article ASAP. https://doi.org/10.1021/acs.jcim.3c01406

  60. Hawkins PC, Warren GL, Skillman AG et al (2008) How to do an evaluation: pitfalls and traps. J Comput Aided Mol Des 22:179–190

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Amezcua M, Setiadi J, Ge Y et al (2022) An overview of the SAMPL8 host-guest binding challenge. J Comput Aided Mol Des 36:707–734

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Graves AP, Shivakumar DM, Boyce SE et al (2008) Rescoring docking hit lists for model cavity sites: predictions and experimental testing. J Mol Biol 377:914–934

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Kamenik AS, Singh I, Lak P et al (2021) Energy penalties enhance flexible receptor docking in a model cavity. Proc Natl Acad Sci USA 118:e2106195118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF Chimera--a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612

    Article  CAS  PubMed  Google Scholar 

  65. Word JM, Lovell SC, Richardson JS et al (1999) Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. J Mol Biol 285:1735–1747

    Article  CAS  PubMed  Google Scholar 

  66. Case DA, Ben-Shalom IY, Brozell SR et al (2019) AMBERTools 2019. University of California, San Francisco

    Google Scholar 

  67. Tingle BI, Tang KG, Castanon M et al (2023) ZINC-22 – a free multi-billion-scale database of tangible compounds for ligand discovery. J Chem Inf Model 63:1166–1176

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Balius TE (2023) teb_scripts_programs. Available from https://github.com/tbalius/teb_scripts_programs/tree/master/zzz.scripts/

  69. Hert J, Irwin JJ, Laggner C et al (2009) Quantifying biogenic bias in screening libraries. Nat Chem Biol 5:479–483

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Kaplan AL, Confair DN, Kim K et al (2022) Bespoke library docking for 5-HT(2A) receptor agonists with antidepressant activity. Nature 610:582–591

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Irwin JJ, Shoichet BK, Mysinger MM et al (2009) Automated docking screens: a feasibility study. J Med Chem 52:5712–5720

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Mysinger MM, Carchia M, Irwin JJ et al (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55:6582–6594

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Beroza P, Crawford JJ, Ganichkin O et al (2022) Chemical space docking enables large-scale structure-based virtual screening to discover ROCK1 kinase inhibitors. Nat Commun 13:6447

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Tingle BI, Irwin JJ (2023) Large-scale docking in the cloud. J Chem Inf Model 63:2735–2741

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Lu H, Wei Z, Wang C et al (2021) Redesigning Vina@QNLM for ultra-large-scale molecular docking and screening on a Sunway supercomputer. Front Chem 9:750325

    Article  PubMed  PubMed Central  Google Scholar 

  76. Lyu J, Irwin JJ, Shoichet BK (2023) Modeling the expansion of virtual screening libraries. Nat Chem Biol 19:712–718

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Sadybekov AA, Sadybekov AV, Liu Y et al (2022) Synthon-based ligand discovery in virtual libraries of over 11 billion compounds. Nature 601:452–459

    Article  CAS  PubMed  Google Scholar 

  78. Sadybekov AV, Katritch V (2023) Computational approaches streamlining drug discovery. Nature 616:673–685

    Article  CAS  PubMed  Google Scholar 

  79. Clyde A, Liu X, Brettin T et al (2023) AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection. Sci Rep 13:2105

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Gentile F, Yaacoub JC, Gleave J et al (2022) Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking. Nat Protoc 17:672–697

    Article  CAS  PubMed  Google Scholar 

  81. Graff DE, Shakhnovich EI, Coley CW (2021) Accelerating high-throughput virtual screening through molecular pool-based active learning. Chem Sci 12:7866–7881

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Yang Y, Yao K, Repasky MP et al (2021) Efficient exploration of chemical space with docking and deep learning. J Chem Theory Comput 17:7106–7119

    Article  CAS  PubMed  Google Scholar 

  83. Christoph G, AkshatKumar N, Matt K et al (2023) VirtualFlow 2.0 – the next generation drug discovery platform enabling adaptive screens of 69 billion molecules. bioRxiv. https://doi.org/10.1101/2023.04.25.537981

  84. Yu Y, Cai C, Wang J et al (2023) Uni-Dock: GPU-accelerated docking enables ultralarge virtual screening. J Chem Theory Comput 19:3336–3345

    Article  CAS  PubMed  Google Scholar 

  85. Zhang B, Li H, Yu K et al (2022) Molecular docking-based computational platform for high-throughput virtual screening. CCF Trans High Perform Comput 4:63–74

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

We thank Alyssa Klein, Muyan Zhou, Dillon Chu, Maryam Abdur-Rahman, Yilin Liang, and Yunpeng Wang for contributions to the tutorials. We thank colleagues at the NCI RAS Initiative, including Dwight Nissley and Frank McCormick, and the Frederick Research Computing Environment (FRCE). This project was funded in whole or in part with federal funds from the National Cancer Institute, NIH Contract 75N91019D00024. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, and the mention of trade names, commercial products, or organizations does not imply endorsement by the US government.

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Author notes

  1. Mayukh Chakrabarti and Y. Stanley Tan contributed equally.

Authors and Affiliations

  1. NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA

    Mayukh Chakrabarti, Y. Stanley Tan & Trent E. Balius

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  1. Mayukh Chakrabarti
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  2. Y. Stanley Tan
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  3. Trent E. Balius
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Correspondence to Trent E. Balius .

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Editors and Affiliations

  1. NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA

    Andrew G. Stephen

  2. NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA

    Dominic Esposito

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Chakrabarti, M., Tan, Y.S., Balius, T.E. (2024). Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK. In: Stephen, A.G., Esposito, D. (eds) KRAS. Methods in Molecular Biology, vol 2797. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3822-4_6

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  • DOI: https://doi.org/10.1007/978-1-0716-3822-4_6

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