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Computational Fragment-Based Drug Design: Current Trends, Strategies, and Applications

  • Review Article
  • Theme: Pharmaceutical Sciences in the Era of Big Data Computing
  • Published:
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Abstract

Fragment-based drug design (FBDD) has become an effective methodology for drug development for decades. Successful applications of this strategy brought both opportunities and challenges to the field of Pharmaceutical Science. Recent progress in the computational fragment-based drug design provide an additional approach for future research in a time- and labor-efficient manner. Combining multiple in silico methodologies, computational FBDD possesses flexibilities on fragment library selection, protein model generation, and fragments/compounds docking mode prediction. These characteristics provide computational FBDD superiority in designing novel and potential compounds for a certain target. The purpose of this review is to discuss the latest advances, ranging from commonly used strategies to novel concepts and technologies in computational fragment-based drug design. Particularly, in this review, specifications and advantages are compared between experimental and computational FBDD, and additionally, limitations and future prospective are discussed and emphasized.

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References

  1. Mayr LM, Bojanic D. Novel trends in high-throughput screening. Curr Opin Pharmacol. 2009;9(5):580–8.

    Article  PubMed  CAS  Google Scholar 

  2. Hajduk PJ, Greer J. A decade of fragment-based drug design: strategic advances and lessons learned. Nat Rev Drug Discov. 2007;6(3):211–9.

    Article  PubMed  CAS  Google Scholar 

  3. Sheng C, Zhang W. Fragment informatics and computational fragment-based drug design: an overview and update. Med Res Rev. 2013;33(3):554–98.

    Article  PubMed  CAS  Google Scholar 

  4. Congreve M, Carr R, Murray C, Jhoti H. A ‘rule of three’ for fragment-based lead discovery? Drug Discov Today. 2003;8(19):876–7.

    Article  PubMed  Google Scholar 

  5. Jhoti H, Williams G, Rees DC, Murray CW. The ‘rule of three’ for fragment-based drug discovery: where are we now? Nat Rev Drug Discov. 2013;12(8):644–5.

    Article  PubMed  CAS  Google Scholar 

  6. Koster H, Craan T, Brass S, Herhaus C, Zentgraf M, Neumann L, et al. A small nonrule of 3 compatible fragment library provides high hit rate of endothiapepsin crystal structures with various fragment chemotypes. J Med Chem. 2011;54(22):7784–96.

    Article  PubMed  CAS  Google Scholar 

  7. Congreve M, Chessari G, Tisi D, Woodhead AJ. Recent developments in fragment-based drug discovery. J Med Chem. 2008;51(13):3661–80.

    Article  PubMed  CAS  Google Scholar 

  8. Murray CW, Callaghan O, Chessari G, Cleasby A, Congreve M, Frederickson M, et al. Application of fragment screening by X-ray crystallography to beta-secretase. J Med Chem. 2007;50(6):1116–23.

    Article  PubMed  CAS  Google Scholar 

  9. Haydon DJ, Stokes NR, Ure R, Galbraith G, Bennett JM, Brown DR, et al. An inhibitor of FtsZ with potent and selective anti-staphylococcal activity. Science. 2008;321(5896):1673–5.

    Article  PubMed  CAS  Google Scholar 

  10. Card GL, Blasdel L, England BP, Zhang C, Suzuki Y, Gillette S, et al. A family of phosphodiesterase inhibitors discovered by cocrystallography and scaffold-based drug design. Nat Biotechnol. 2005;23(2):201–7.

    Article  PubMed  CAS  Google Scholar 

  11. Chen Y, Shoichet BK. Molecular docking and ligand specificity in fragment-based inhibitor discovery. Nat Chem Biol. 2009;5(5):358–64.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Hashem IAT, Yaqoob I, Anuar NB, Mokhtar S, Gani A, Khan SU. The rise of “big data” on cloud computing: review and open research issues. Inf Syst. 2015;47:98–115.

    Article  Google Scholar 

  13. Ray PC, Kiczun M, Huggett M, Lim A, Prati F, Gilbert IH, et al. Fragment library design, synthesis and expansion: nurturing a synthesis and training platform. Drug Discov Today. 2017;22(1):43–56.

    Article  PubMed  CAS  Google Scholar 

  14. Keserű GM, Erlanson DA, Ferenczy GG, Hann MM, Murray CW, Pickett SD. Design principles for fragment libraries: maximizing the value of learnings from pharma fragment-based drug discovery (FBDD) programs for use in academia. J Med Chem. 2016;59(18):8189–206.

    Article  PubMed  CAS  Google Scholar 

  15. Yuriev E, Holien J, Ramsland PA. Improvements, trends, and new ideas in molecular docking: 2012–2013 in review. J Mol Recognit. 2015;28(10):581–604.

    Article  PubMed  CAS  Google Scholar 

  16. Yuriev E, Ramsland PA. Latest developments in molecular docking: 2010–2011 in review. J Mol Recognit. 2013;26(5):215–39.

    Article  PubMed  CAS  Google Scholar 

  17. Wang L, Xie Z, Wipf P, Xie X-Q. Residue preference mapping of ligand fragments in the protein data bank. J Chem Inf Model. 2011;51(4):807–15.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Myint K, Ma C, Wang L, Xie X-Q. Fragment-similarity-based QSAR (FS-QSAR) algorithm for ligand biological activity predictions. SAR QSAR Environ Res. 2011;22(3–4):385–410.

    Article  PubMed  CAS  Google Scholar 

  19. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem. 2004;47(7):1739–49.

    Article  PubMed  CAS  Google Scholar 

  20. Jones G, Willett P, Glen RC. Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J Mol Biol. 1995;245(1):43–53.

    Article  PubMed  CAS  Google Scholar 

  21. Jones G, Willett P, Glen RC, Leach AR, Taylor R. Development and validation of a genetic algorithm for flexible docking. J Mol Biol. 1997;267(3):727–48.

    Article  PubMed  CAS  Google Scholar 

  22. Jain AN. Surflex-dock 2.1: robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search. J Comput Aided Mol Des. 2007;21(5):281–306.

    Article  PubMed  CAS  Google Scholar 

  23. Bohnuud T, Luo L, Wodak SJ, Bonvin AM, Weng Z, Vajda S, et al. A benchmark testing ground for integrating homology modeling and protein docking. Proteins: Structure, Function, and Bioinformatics. 2017;85(1):10–6.

    Article  CAS  Google Scholar 

  24. Cavasotto CN, Phatak SS. Homology modeling in drug discovery: current trends and applications. Drug Discov Today. 2009;14(13):676–83.

    Article  PubMed  CAS  Google Scholar 

  25. Webb B, Sali A. Protein structure modeling with MODELLER. Protein Structure Prediction. 2014:1–15.

  26. Schwede T, Kopp J, Guex N, Peitsch MC. SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res. 2003;31(13):3381–5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Chung S, Parker JB, Bianchet M, Amzel LM, Stivers JT. Impact of linker strain and flexibility in the design of a fragment-based inhibitor. Nat Chem Biol. 2009;5(6):407–13.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Kawai K, Nagata N, Takahashi Y. De novo design of drug-like molecules by a fragment-based molecular evolutionary approach. J Chem Inf Model. 2014;54(1):49–56.

    Article  PubMed  CAS  Google Scholar 

  29. Guvench O. Computational functional group mapping for drug discovery. Drug Discov Today. 2016;21(12):1928–31.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Chen H, Zhou X, Wang A, Zheng Y, Gao Y, Zhou J. Evolutions in fragment-based drug design: the deconstruction-reconstruction approach. Drug Discov Today. 2015;20(1):105–13.

    Article  PubMed  CAS  Google Scholar 

  31. Speck-Planche A, Cordeiro MND. Fragment-based in silico modeling of multi-target inhibitors against breast cancer-related proteins. Mol Divers. 2017:1–13.

  32. Garcia-Jacas CR, Marrero-Ponce Y, Acevedo-Martinez L, Barigye SJ, Valdes-Martini JR, Contreras-Torres E. QuBiLS-MIDAS: a parallel free-software for molecular descriptors computation based on multilinear algebraic maps. J Comput Chem. 2014;35(18):1395–409.

    Article  PubMed  CAS  Google Scholar 

  33. Hao GF, Jiang W, Ye YN, Wu FX, Zhu XL, Guo FB, et al. ACFIS: a web server for fragment-based drug discovery. Nucleic Acids Res. 2016;44(W1):W550–6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Li Y, Zhao Z, Liu Z, Su M, Wang R. AutoT&T v.2: an efficient and versatile tool for lead structure generation and optimization. J Chem Inf Model. 2016;56(2):435–53.

    Article  PubMed  CAS  Google Scholar 

  35. Pevzner Y, Frugier E, Schalk V, Caflisch A, Woodcock HL. Fragment-based docking: development of the CHARMMing Web user interface as a platform for computer-aided drug design. J Chem Inf Model. 2014;54(9):2612–20.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Douguet D. e-LEA3D: a computational-aided drug design web server. Nucleic Acids Res. 2010;38(Web Server):W615–21.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Liu T, Naderi M, Alvin C, Mukhopadhyay S, Brylinski M. Break down in order to build up: decomposing small molecules for fragment-based drug design with e MolFrag. J Chem Inf Model. 2017;57(4):627–31.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Naderi M, Alvin C, Ding Y, Mukhopadhyay S, Brylinski M. A graph-based approach to construct target-focused libraries for virtual screening. J Cheminformatics. 2016;8(1):14.

    Article  CAS  Google Scholar 

  39. Fechner U, Schneider G. Flux (1): a virtual synthesis scheme for fragment-based de novo design. J Chem Inf Model. 2006;46(2):699–707.

    Article  PubMed  CAS  Google Scholar 

  40. Kozakov D, Grove LE, Hall DR, Bohnuud T, Mottarella SE, Luo L, et al. The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins. Nat Protoc. 2015;10(5):733–55.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Tsai TY, Chang KW, Chen CY. iScreen: world’s first cloud-computing web server for virtual screening and de novo drug design based on TCM database@Taiwan. J Comput Aided Mol Des. 2011;25(6):525–31.

    Article  PubMed  CAS  Google Scholar 

  42. Yuan Y, Pei J, Lai L. LigBuilder 2: a practical de novo drug design approach. J Chem Inf Model. 2011;51(5):1083–91.

    Article  PubMed  CAS  Google Scholar 

  43. Damewood JR, Jr., Lerman CL, Masek BB. NovoFLAP: a ligand-based de novo design approach for the generation of medicinally relevant ideas. J Chem Inf Model 2010;50(7):1296–1303.

  44. Moriaud F, Richard SB, Adcock SA, Chanas-Martin L, Surgand JS, Ben Jelloul M, et al. Identify drug repurposing candidates by mining the protein data bank. Brief Bioinform. 2011;12(4):336–40.

    Article  PubMed  CAS  Google Scholar 

  45. Ghersi D, Singh M. molBLOCKS: decomposing small molecule sets and uncovering enriched fragments. Bioinformatics. 2014;30(14):2081–3.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Hoffer L, Chira C, Marcou G, Varnek A, Horvath D. S4MPLE—sampler for multiple protein-ligand entities: methodology and rigid-site docking benchmarking. Molecules. 2015;20(5):8997–9028.

    Article  PubMed  CAS  Google Scholar 

  47. Liu X, Jiang H, Li H. SHAFTS: a hybrid approach for 3D molecular similarity calculation. 1. Method and assessment of virtual screening. J Chem Inf Model. 2011;51(9):2372–85.

    Article  PubMed  CAS  Google Scholar 

  48. Yu W, Xiao H, Lin J, Li C. Discovery of novel STAT3 small molecule inhibitors via in silico site-directed fragment-based drug design. J Med Chem. 2013;56(11):4402–12.

    Article  PubMed  CAS  Google Scholar 

  49. Wu P, Wu D, Zhao L, Huang L, Shen G, Huang J, et al. Prognostic role of STAT3 in solid tumors: a systematic review and meta-analysis. Oncotarget. 2016;7(15):19863–83.

    PubMed  PubMed Central  Google Scholar 

  50. Banerjee K, Resat H. Constitutive activation of STAT3 in breast cancer cells: a review. Int J Cancer. 2016;138(11):2570–8.

    Article  PubMed  CAS  Google Scholar 

  51. Bian Y, Feng Z, Yang P, Xie XQ. Integrated in silico fragment-based drug design: case study with allosteric modulators on metabotropic glutamate receptor 5. AAPS J. 2017;19(4):1235–48.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  52. Wenthur CJ, Gentry PR, Mathews TP, Lindsley CW. Drugs for allosteric sites on receptors. Annu Rev Pharmacol Toxicol. 2014;54:165–84.

    Article  PubMed  CAS  Google Scholar 

  53. Bonnet R. Growing group of extended-spectrum beta-lactamases: the CTX-M enzymes. Antimicrob Agents Chemother. 2004;48(1):1–14.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Hubbard RE, Chen I, Davis B. Informatics and modeling challenges in fragment-based drug discovery. Curr Opin Drug Discov Devel. 2007;10(3):289–97.

    PubMed  CAS  Google Scholar 

  55. Ferreira LG, Dos Santos RN, Oliva G, Andricopulo AD. Molecular docking and structure-based drug design strategies. Molecules. 2015;20(7):13384–421.

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

The authors would like to acknowledge the funding supports to the Xie laboratory from the NIH NIDA (P30 DA035778A1) and DOD (W81XWH-16-1-0490). The first author would like to thank Jie in particular, for the consistant support, love, and the memorable and solemn wedding. How lucky the first author is to have Jie as his bride!

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Authors and Affiliations

  1. Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA

    Yuemin Bian & Xiang-Qun (Sean) Xie

  2. NIH National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA

    Yuemin Bian & Xiang-Qun (Sean) Xie

  3. Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA

    Yuemin Bian & Xiang-Qun (Sean) Xie

  4. Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA

    Xiang-Qun (Sean) Xie

  5. Department of Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA

    Xiang-Qun (Sean) Xie

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  1. Yuemin Bian
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Correspondence to Xiang-Qun (Sean) Xie.

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The authors declare that they have no competing interest.

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Guest Editor: Xiang-Qun Xie

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Bian, Y., Xie, XQ.(. Computational Fragment-Based Drug Design: Current Trends, Strategies, and Applications. AAPS J 20, 59 (2018). https://doi.org/10.1208/s12248-018-0216-7

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  • DOI: https://doi.org/10.1208/s12248-018-0216-7

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