Drug Repositioning Using Computer-aided Drug Design (CADD)

  • Authors: Roy A.1, Nagaprasad N.2, Aruna M.3, Tesfaye J.4, Badassa B.5, Krishnaraj R.6, Rawat S.7, Subramaniam K.8, Subramanian S.9, Subbarayan S.10, Dhanabalan S.11, Chidambaram S.K.12, Stalin B.13
  • Affiliations:
    1. Department of Biotechnology, School of Engineering & Technology,, Sharda University
    2. Department of Mechanical Engineering, ULTRA College of Engineering and Technology
    3. College of Engineering and Computing, Al Ghurair University, Academic Cit
    4. College of Natural and Computational Science, Department of Physics, Dambi Dollo University,
    5. Department of Mechanical Engineering,, Dambi Dollo University
    6. Centre for Excellence-Indigenous Knowledge, Innovative Technology Transfer and Entrepreneurship, Dambi Dollo University
    7. School of Life Sciences, Jaipur National University
    8. Department of Civil Engineering, KPR Institute of Engineering and Technology
    9. Department of Sciences, Amrita School of Engineering
    10. Department of Civil Engineering,, National Institute of Technology
    11. Department of Mechanical Engineering, M. Kumarasamy College of Engineering
    12. Centre for Water Resources, Department of Civil Engineering, Anna University
    13. Department of Mechanical Engineering, Anna University
  • Issue: Vol 25, No 3 (2024)
  • Pages: 301-312
  • Section: Biotechnology
  • URL: https://rjeid.com/1389-2010/article/view/644771
  • DOI: https://doi.org/10.2174/1389201024666230821103601
  • ID: 644771

Cite item

Full Text

Abstract

Drug repositioning is a method of using authorized drugs for other unusually complex diseases. Compared to new drug development, this method is fast, low in cost, and effective. Through the use of outstanding bioinformatics tools, such as computer-aided drug design (CADD), computer strategies play a vital role in the re-transformation of drugs. The use of CADD's special strategy for target-based drug reuse is the most promising method, and its realization rate is high. In this review article, we have particularly focused on understanding the various technologies of CADD and the use of computer-aided drug design for target-based drug reuse, taking COVID-19 and cancer as examples. Finally, it is concluded that CADD technology is accelerating the development of repurposed drugs due to its many advantages, and there are many facts to prove that the new ligand-targeting strategy is a beneficial method and that it will gain momentum with the development of technology.

About the authors

Arpita Roy

Department of Biotechnology, School of Engineering & Technology,, Sharda University

Email: info@benthamscience.net

Nagaraj Nagaprasad

Department of Mechanical Engineering, ULTRA College of Engineering and Technology

Email: info@benthamscience.net

Mahalingam Aruna

College of Engineering and Computing, Al Ghurair University, Academic Cit

Email: info@benthamscience.net

Jule Tesfaye

College of Natural and Computational Science, Department of Physics, Dambi Dollo University,

Email: info@benthamscience.net

Bayissa Badassa

Department of Mechanical Engineering,, Dambi Dollo University

Email: info@benthamscience.net

Ramaswamy Krishnaraj

Centre for Excellence-Indigenous Knowledge, Innovative Technology Transfer and Entrepreneurship, Dambi Dollo University

Author for correspondence.
Email: info@benthamscience.net

Sona Rawat

School of Life Sciences, Jaipur National University

Email: info@benthamscience.net

Kanmani Subramaniam

Department of Civil Engineering, KPR Institute of Engineering and Technology

Email: info@benthamscience.net

Selva Subramanian

Department of Sciences, Amrita School of Engineering

Email: info@benthamscience.net

Saravanan Subbarayan

Department of Civil Engineering,, National Institute of Technology

Email: info@benthamscience.net

Subramanian Dhanabalan

Department of Mechanical Engineering, M. Kumarasamy College of Engineering

Email: info@benthamscience.net

Sashik Kumar Chidambaram

Centre for Water Resources, Department of Civil Engineering, Anna University

Email: info@benthamscience.net

Balasubramaniam Stalin

Department of Mechanical Engineering, Anna University

Email: info@benthamscience.net

References

  1. Rester, U. From virtuality to reality-virtual screening in lead discovery and lead optimization: A medicinal chemistry perspective. Curr. Opin. Drug Discov. Devel., 2008, 11(4), 559-568. PMID: 18600572
  2. Andrews, K.T.; Fisher, G.; Skinner-Adams, T.S. Drug repurposing and human parasitic protozoan diseases. Int. J. Parasitol. Drugs Drug Resist., 2014, 4(2), 95-111. doi: 10.1016/j.ijpddr.2014.02.002 PMID: 25057459
  3. Parvathaneni, V.; Kulkarni, N.S.; Muth, A.; Gupta, V. Drug repurposing: A promising tool to accelerate the drug discovery process. Drug Discov. Today, 2019, 24(10), 2076-2085. doi: 10.1016/j.drudis.2019.06.014 PMID: 31238113
  4. Sabe, V.T.; Ntombela, T.; Jhamba, L.A.; Maguire, G.E.M.; Govender, T.; Naicker, T.; Kruger, H.G. Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review. Eur. J. Med. Chem., 2021, 224, 113705. doi: 10.1016/j.ejmech.2021.113705 PMID: 34303871
  5. Koes, D.R.; Camacho, C.J. ZINCPharmer: Pharmacophore search of the ZINC database. Nucleic Acids Res., 2012, 40(W1), W409-W414. doi: 10.1093/nar/gks378 PMID: 22553363
  6. Berman, H.; Henrick, K.; Nakamura, H.; Markley, J.L. The worldwide Protein Data Bank (wwPDB): Ensuring a single, uniform archive of PDB data. Nucleic Acids Res., 2007, 35(Database), D301-D303. doi: 10.1093/nar/gkl971 PMID: 17142228
  7. Schneider, G.; Geppert, T.; Hartenfeller, M.; Reisen, F.; Klenner, A.; Reutlinger, M.; Hähnke, V.; Hiss, J.A.; Zettl, H.; Keppner, S.; Spänkuch, B.; Schneider, P. Reaction-driven de novo design, synthesis and testing of potential type II kinase inhibitors. Future Med. Chem., 2011, 3(4), 415-424. doi: 10.4155/fmc.11.8 PMID: 21452978
  8. Johnson, D.K.; Karanicolas, J. Computational screening and design for compounds that disrupt protein-protein interactions. Curr. Top. Med. Chem., 2017, 17(23), 2703-2714. doi: 10.2174/1568026617666170508153904 PMID: 28482793
  9. Kitchen, D.B.; Decornez, H.; Furr, J.R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat. Rev. Drug Discov., 2004, 3(11), 935-949. doi: 10.1038/nrd1549 PMID: 15520816
  10. Dudley, J.T.; Deshpande, T.; Butte, A.J. Exploiting drug-disease relationships for computational drug repositioning. Brief. Bioinform., 2011, 12(4), 303-311. doi: 10.1093/bib/bbr013 PMID: 21690101
  11. Jin, Z.; Du, X.; Xu, Y.; Deng, Y.; Liu, M.; Zhao, Y.; Zhang, B.; Li, X.; Zhang, L.; Peng, C.; Duan, Y.; Yu, J.; Wang, L.; Yang, K.; Liu, F.; Jiang, R.; Yang, X.; You, T.; Liu, X.; Yang, X.; Bai, F.; Liu, H.; Liu, X.; Guddat, L.W.; Xu, W.; Xiao, G.; Qin, C.; Shi, Z.; Jiang, H.; Rao, Z.; Yang, H. Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors. Nature, 2020, 582(7811), 289-293. doi: 10.1038/s41586-020-2223-y PMID: 32272481
  12. Reichle, A. Evolution-adjusted Tumor Pathophysiology; Springer, 2013. doi: 10.1007/978-94-007-6866-6
  13. Pieper, U.; Webb, B.M.; Barkan, D.T.; Schneidman-Duhovny, D.; Schlessinger, A.; Braberg, H.; Yang, Z.; Meng, E.C.; Pettersen, E.F.; Huang, C.C.; Datta, R.S.; Sampathkumar, P.; Madhusudhan, M.S.; Sjölander, K.; Ferrin, T.E.; Burley, S.K.; Sali, A. ModBase, a database of annotated comparative protein structure models, and associated resources. Nucleic Acids Res., 2011, 39(Database), D465-D474. doi: 10.1093/nar/gkq1091 PMID: 21097780
  14. Sarvagalla, S.; Syed, S.B.; Coumar, M.S. An overview of computational methods, tools, servers, and databases for drug repurposing; Silico Drug Des, 2019, pp. 743-780. doi: 10.1016/B978-0-12-816125-8.00025-0
  15. Kazandjian, D.; Suzman, D.L.; Blumenthal, G.; Mushti, S.; He, K.; Libeg, M.; Keegan, P.; Pazdur, R. FDA approval summary: Nivolumab for the treatment of metastatic non-small cell lung cancer with progression on or after platinum-based chemotherapy. Oncologist, 2016, 21(5), 634-642. doi: 10.1634/theoncologist.2015-0507 PMID: 26984449
  16. Wang, Z.; Lachmann, A.; Keenan, A.B.; Ma’ayan, A. L1000FWD: Fireworks visualization of drug-induced transcriptomic signatures. Bioinformatics, 2018, 34(12), 2150-2152. doi: 10.1093/bioinformatics/bty060 PMID: 29420694
  17. Hood, L.E.; Omenn, G.S.; Moritz, R.L.; Aebersold, R.; Yamamoto, K.R.; Amos, M.; Hunter-Cevera, J.; Locascio, L. New and improved proteomics technologies for understanding complex biological systems: Addressing a grand challenge in the life sciences. Proteomics, 2012, 12(18), 2773-2783. doi: 10.1002/pmic.201270086 PMID: 22807061
  18. Talele, T.; Khedkar, S.; Rigby, A. Successful applications of computer aided drug discovery: Moving drugs from concept to the clinic. Curr. Top. Med. Chem., 2010, 10(1), 127-141. doi: 10.2174/156802610790232251 PMID: 19929824
  19. Jin, G.; Wong, S.T.C. Toward better drug repositioning: Prioritizing and integrating existing methods into efficient pipelines. Drug Discov. Today, 2014, 19(5), 637-644. doi: 10.1016/j.drudis.2013.11.005 PMID: 24239728
  20. Himmelstein, D.S.; Lizee, A.; Hessler, C.; Brueggeman, L.; Chen, S.L.; Hadley, D.; Green, A.; Khankhanian, P.; Baranzini, S.E. Systematic integration of biomedical knowledge prioritizes drugs for repurposing. eLife, 2017, 6, e26726. doi: 10.7554/eLife.26726 PMID: 28936969
  21. Spengler, D. Aurora-C-T191D is a hyperactive Aurora-C mutant. Cell Cycle, 2007, 6(14), 1803-1804. doi: 10.4161/cc.6.14.4479 PMID: 17637569
  22. Basak, S.C. Chemobioinformatics: The advancing frontier of computer-aided drug design in the post-genomic era. Curr. Computeraided Drug Des., 2012, 8(1), 1-2. doi: 10.2174/18756697MTEz9MjEaz PMID: 22320162
  23. Li, Y.H.; Yu, C.Y.; Li, X.X.; Zhang, P.; Tang, J.; Yang, Q.; Fu, T.; Zhang, X.; Cui, X.; Tu, G.; Zhang, Y.; Li, S.; Yang, F.; Sun, Q.; Qin, C.; Zeng, X.; Chen, Z.; Chen, Y.Z.; Zhu, F. Therapeutic target database update 2018: Enriched resource for facilitating bench-toclinic research of targeted therapeutics. Nucleic Acids Res., 2018, 46(D1), D1121-D1127. doi: 10.1093/nar/gkx1076 PMID: 29140520
  24. Ke, Y.Y.; Peng, T.T.; Yeh, T.K.; Huang, W.Z.; Chang, S.E.; Wu, S.H.; Hung, H.C.; Hsu, T.A.; Lee, S.J.; Song, J.S.; Lin, W.H.; Chiang, T.J.; Lin, J.H.; Sytwu, H.K.; Chen, C.T. Artificial intelligence approach fighting COVID-19 with repurposing drugs. Biomed. J., 2020, 43(4), 355-362. doi: 10.1016/j.bj.2020.05.001 PMID: 32426387
  25. Cai, R.; Zhang, Y.; Simmering, J.E.; Schultz, J.L.; Li, Y.; Fernandez-Carasa, I.; Consiglio, A.; Raya, A.; Polgreen, P.M.; Narayanan, N.S.; Yuan, Y.; Chen, Z.; Su, W.; Han, Y.; Zhao, C.; Gao, L.; Ji, X.; Welsh, M.J.; Liu, L. Enhancing glycolysis attenuates Parkinson’s disease progression in models and clinical databases. J. Clin. Invest., 2019, 129(10), 4539-4549. doi: 10.1172/JCI129987 PMID: 31524631
  26. Wang, Y.; Chen, S.; Deng, N.; Wang, Y. Drug repositioning by kernel-based integration of molecular structure, molecular activity, and phenotype data. PLoS One, 2013, 8(11), e78518. doi: 10.1371/journal.pone.0078518 PMID: 24244318
  27. Jorgensen, W.L. Pulled from a protein’s embrace. Nature, 2010, 466(7302), 42-43. doi: 10.1038/466042a PMID: 20596009
  28. Mullard, A. 2017 FDA drug approvals. Nat. Rev. Drug Discov., 2018, 17(2), 81-85. doi: 10.1038/nrd.2018.4 PMID: 29348678
  29. Quartuccio, S.M.; Schindler, K. Functions of Aurora kinase C in meiosis and cancer. Front. Cell Dev. Biol., 2015, 3, 50. doi: 10.3389/fcell.2015.00050 PMID: 26347867
  30. Liu, T.; Lin, Y.; Wen, X.; Jorissen, R.N.; Gilson, M.K. BindingDB: A web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res., 2007, 35(Database), D198-D201. doi: 10.1093/nar/gkl999 PMID: 17145705
  31. Zhang, D.; Wu, K.; Zhang, X.; Deng, S.; Peng, B. In silico screening of Chinese herbal medicines with the potential to directly inhibit 2019 novel coronavirus. J. Integr. Med., 2020, 18(2), 152-158. doi: 10.1016/j.joim.2020.02.005 PMID: 32113846
  32. Schenone, M.; Dančík, V.; Wagner, B.K.; Clemons, P.A. Target identification and mechanism of action in chemical biology and drug discovery. Nat. Chem. Biol., 2013, 9(4), 232-240. doi: 10.1038/nchembio.1199 PMID: 23508189
  33. Kumar, R.; Harilal, S.; Gupta, S.V.; Jose, J.; Thomas Parambi, D.G.; Uddin, M.S.; Shah, M.A.; Mathew, B. Exploring the new horizons of drug repurposing: A vital tool for turning hard work into smart work. Eur. J. Med. Chem., 2019, 182, 111602. doi: 10.1016/j.ejmech.2019.111602 PMID: 31421629
  34. Koes, D.R.; Dömling, A.; Camacho, C.J. AnchorQuery: Rapid online virtual screening for small-molecule protein-protein interaction inhibitors. Protein Sci., 2018, 27(1), 229-232. doi: 10.1002/pro.3303 PMID: 28921842
  35. Zhao, S.; Nishimura, T.; Chen, Y.; Azeloglu, E.U.; Gottesman, O.; Giannarelli, C. Systems pharmacology of adverse event mitigation by drug combinations. Sci. Transl. Med., 2013, 5(206), 206ra140. doi: 10.1126/scitranslmed.3006548
  36. Dallakyan, S.; Olson, A.J. Small-molecule library screening by docking with PyRx. Methods Mol. Biol., 2015, 1263, 243-250. doi: 10.1007/978-1-4939-2269-7_19 PMID: 25618350
  37. Firoz, A.D.; Rahman, M.M. Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein. Sci. Rep., 2021, 11(1), 4049. doi: 10.1038/s41598-021-83626-x
  38. Khan, J.; Khan, S.; Attaullah, S.; Ali, I.; Khan, S.N. Aurora kinase-C-T191D is constitutively active mutant. BMC Cell Biol., 2012, 13, 1-9.
  39. Wiederstein, M.; Sippl, M.J. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res., 2007, 35(Web Server), W407-W410. doi: 10.1093/nar/gkm290 PMID: 17517781
  40. Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31(2), 455-461. PMID: 19499576
  41. Berman, H.; Westbrook, M.; Feng, Z.; Gilliland, G. The protein data bank. Nucleic Acids Res., 2000, 28(1), 235-42.
  42. Guilhot-Gaudeffroy, A.; Froidevaux, C.; Azé, J.; Bernauer, J. Protein-RNA complexes and efficient automatic docking: expanding RosettaDock possibilities. PLoS One, 2014, 9(9), e108928. doi: 10.1371/journal.pone.0108928 PMID: 25268579
  43. Iwakiri, J.; Hamada, M.; Asai, K.; Kameda, T. Improved accuracy in RNA–Protein rigid body docking by incorporating force field for molecular dynamics simulation into the scoring function. J. Chem. Theory Comput., 2016, 12(9), 4688-4697. doi: 10.1021/acs.jctc.6b00254 PMID: 27494732
  44. Rose, P.W.; Beran, B.; Bi, C.; Bluhm, W.F.; Dimitropoulos, D.; Goodsell, D.S.; Prlic, A.; Quesada, M.; Quinn, G.B.; Westbrook, J.D.; Young, J.; Yukich, B.; Zardecki, C.; Berman, H.M.; Bourne, P.E. The RCSB Protein Data Bank: Redesigned web site and web services. Nucleic Acids Res., 2011, 39(Database), D392-D401. doi: 10.1093/nar/gkq1021 PMID: 21036868
  45. Parisi, D.; Adasme, M.F.; Sveshnikova, A.; Bolz, S.N.; Moreau, Y.; Schroeder, M. Drug repositioning or target repositioning: A structural perspective of drug-target-indication relationship for available repurposed drugs. Comput. Struct. Biotechnol. J., 2020, 18, 1043-1055. doi: 10.1016/j.csbj.2020.04.004 PMID: 32419905
  46. Corsello, S.M.; Bittker, J.A.; Liu, Z.; Gould, J.; McCarren, P.; Hirschman, J.E.; Johnston, S.E.; Vrcic, A.; Wong, B.; Khan, M.; Asiedu, J.; Narayan, R.; Mader, C.C.; Subramanian, A.; Golub, T.R. The drug repurposing hub: A next-generation drug library and information re-source. Nat. Med., 2017, 23(4), 405-408. doi: 10.1038/nm.4306 PMID: 28388612
  47. Markley, J.L.; Ulrich, E.L.; Berman, H.M.; Henrick, K.; Nakamura, H.; Akutsu, H. BioMagResBank (BMRB) as a partner in the Worldwide Protein Data Bank (wwPDB): New policies affecting biomolecular NMR depositions. J. Biomol. NMR, 2008, 40(3), 153-155. doi: 10.1007/s10858-008-9221-y PMID: 18288446
  48. Pérot, S.; Sperandio, O.; Miteva, M.A.; Camproux, A.C.; Villoutreix, B.O. Druggable pockets and binding site centric chemical space: A paradigm shift in drug discovery. Drug Discov. Today, 2010, 15(15-16), 656-667. doi: 10.1016/j.drudis.2010.05.015 PMID: 20685398
  49. Falchi, F.; Caporuscio, F.; Recanatini, M. Structure-based design of small-molecule protein–protein interaction modulators: The story so far. Future Med. Chem., 2014, 6(3), 343-357. doi: 10.4155/fmc.13.204 PMID: 24575969
  50. Awasthi, M.; Singh, S.; Tiwari, S.; Pandey, V.P.; Dwivedi, U.N. Computational approaches for therapeutic application of natural products in Alzheimer’s disease; Comput Model Drugs Against Alzheimer’s Dis, 2018, pp. 483-511.
  51. Takian, A.; Raoofi, A.; Kazempour-Ardebili, S. COVID-19 battle during the toughest sanctions against Iran. Lancet, 2020, 395(10229), 1035-1036. doi: 10.1016/S0140-6736(20)30668-1 PMID: 32199073
  52. Yoo, M.; Shin, J.; Kim, J.; Ryall, K.A.; Lee, K.; Lee, S.; Jeon, M.; Kang, J.; Tan, A.C. DSigDB: Drug signatures database for gene set analysis. Bioinformatics, 2015, 31(18), 3069-3071. doi: 10.1093/bioinformatics/btv313 PMID: 25990557
  53. Pillaiyar, T.; Meenakshisundaram, S.; Manickam, M.; Sankaranarayanan, M. A medicinal chemistry perspective of drug repositioning: Recent advances and challenges in drug discovery. Eur. J. Med. Chem., 2020, 195, 112275. doi: 10.1016/j.ejmech.2020.112275 PMID: 32283298
  54. Janson, G.; Zhang, C.; Prado, M.G.; Paiardini, A. PyMod 2.0: Improvements in protein sequence-structure analysis and homology modeling within PyMOL. Bioinformatics, 2017, 33(3), 444-446. doi: 10.1093/bioinformatics/btw638 PMID: 28158668
  55. Khan, J.; Ezan, F.; Crémet, J.Y.; Fautrel, A.; Gilot, D.; Lambert, M.; Benaud, C.; Troadec, M.B.; Prigent, C. Overexpression of active Aurora-C kinase results in cell transformation and tumour formation. PLoS One, 2011, 6(10), e26512. doi: 10.1371/journal.pone.0026512 PMID: 22046298
  56. Lindsay, M.A. Target discovery. Nat. Rev. Drug Discov., 2003, 2(10), 831-838. doi: 10.1038/nrd1202 PMID: 14526386
  57. Ursu, O.; Holmes, J.; Knockel, J.; Bologa, C.G.; Yang, J.J.; Mathias, S.L. DrugCentral: Online drug compendium. Nucleic Acids Res., 2017, 45(D1), D932-D939. PMID: 27789690
  58. Sunseri, J.; Koes, D.R. Pharmit: Interactive exploration of chemical space. Nucleic Acids Res., 2016, 44(W1), W442-W448. doi: 10.1093/nar/gkw287 PMID: 27095195
  59. Simossis, V.A.; Heringa, J. PRALINE: A multiple sequence alignment toolbox that integrates homology-extended and secondary structure information. Nucleic Acids Res., 2005, 33(Web Server), W289-W294. doi: 10.1093/nar/gki390 PMID: 15980472
  60. Guney, E.; Menche, J.; Vidal, M.; Barábasi, A.L. Network-based in silico drug efficacy screening. Nat. Commun., 2016, 7(1), 10331. doi: 10.1038/ncomms10331 PMID: 26831545
  61. Tang, Y.; Zhu, W.; Chen, K.; Jiang, H. New technologies in computer-aided drug design: Toward target identification and new chemical entity discovery. Drug Discov. Today. Technol., 2006, 3(3), 307-313. doi: 10.1016/j.ddtec.2006.09.004 PMID: 24980533
  62. Chen, V.B.; Arendall, W.B., III; Headd, J.J.; Keedy, D.A.; Immormino, R.M.; Kapral, G.J.; Murray, L.W.; Richardson, J.S.; Richardson, D.C. MolProbity: All-atom structure validation for macromolecular crystallography. Acta Crystallogr. D Biol. Crystallogr., 2010, 66(1), 12-21. doi: 10.1107/S0907444909042073 PMID: 20057044
  63. Bartuzi, D.; Kaczor, A.; Targowska-Duda, K.; Matosiuk, D. Recent advances and applications of molecular docking to G protein-coupled receptors. Molecules, 2017, 22(2), 340. doi: 10.3390/molecules22020340 PMID: 28241450
  64. Krieger, E.; Joo, K.; Lee, J.; Lee, J.; Raman, S.; Thompson, J.; Tyka, M.; Baker, D.; Karplus, K. Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8. Proteins, 2009, 77(Suppl. 9), 114-122. doi: 10.1002/prot.22570 PMID: 19768677
  65. Peng, C.; Chen, J.; Hu, P.; Wang, H. Molecular Adsorption Kinetics: Nonlinear entropy–enthalpy loss quantified by constrained AIMD and insights into the adsorption-site determination on metal oxides. J. Phys. Chem. C, 2021, 125(20), 10974-10982. doi: 10.1021/acs.jpcc.1c02537
  66. Gaulton, A.; Bellis, L.J.; Bento, A.P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; Overington, J.P. ChEMBL: A large-scale bioactivity database for drug discovery. Nucleic Acids Res., 2012, 40(D1), D1100-D1107. doi: 10.1093/nar/gkr777 PMID: 21948594
  67. Takahashi, T.; Zhou, S.Y.; Nakamura, K.; Tanino, R.; Furuichi, A.; Kido, M.; Kawasaki, Y.; Noguchi, K.; Seto, H.; Kurachi, M.; Suzuki, M. A follow-up MRI study of the fusiform gyrus and middle and inferior temporal gyri in schizophrenia spectrum. Prog. Neuropsychopharmacol. Biol. Psychiatry, 2011, 35(8), 1957-1964. doi: 10.1016/j.pnpbp.2011.07.009 PMID: 21820482
  68. Sievers, F.; Higgins, D.G. Clustal Omega for making accurate alignments of many protein sequences. Protein Sci., 2018, 27(1), 135-145. doi: 10.1002/pro.3290 PMID: 28884485
  69. Kalyaanamoorthy, S.; Chen, Y.P.P. Structure-based drug design to augment hit discovery. Drug Discov. Today, 2011, 16(17-18), 831-839. doi: 10.1016/j.drudis.2011.07.006 PMID: 21810482
  70. Lavecchia, A.; Giovanni, C. Virtual screening strategies in drug discovery: A critical review. Curr. Med. Chem., 2013, 20(23), 2839-2860. doi: 10.2174/09298673113209990001 PMID: 23651302
  71. Andreeva, A.; Howorth, D.; Chandonia, J-M.; Brenner, S.E.; Hubbard, T.J.P.; Chothia, C.; Murzin, A.G. Data growth and its impact on the SCOP database: New developments. Nucleic Acids Res., 2008, 36(Database issue), D419-D425. PMID: 18000004
  72. Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem., 2009, 30(16), 2785-2791. doi: 10.1002/jcc.21256 PMID: 19399780
  73. Shekhar, C. In silico pharmacology: Computer-aided methods could transform drug development. Chem. Biol., 2008, 15(5), 413-4. doi: 10.1016/j.chembiol.2008.05.001
  74. Paez Espinosa, E.V.; Murad, J.P.; Khasawneh, F.T. Aspirin: Pharmacology and clinical applications. Thrombosis, 2012, 2012, 173124. doi: 10.1155/2012/173124
  75. Vyas, V.K.; Ukawala, R.D.; Chintha, C.; Ghate, M. Homology modeling a fast tool for drug discovery: Current perspectives. Indian J. Pharm. Sci., 2012, 74(1), 1-17. doi: 10.4103/0250-474X.102537 PMID: 23204616
  76. Guo, Z.; Li, B.; Cheng, L.T.; Zhou, S.; McCammon, J.A.; Che, J. Identification of protein-ligand binding sites by the level-set variational implicit-solvent approach. J. Chem. Theory Comput., 2015, 11(2), 753-765. doi: 10.1021/ct500867u PMID: 25941465
  77. Kanehisa, M.; Goto, S.; Furumichi, M.; Tanabe, M.; Hirakawa, M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res., 2010, 38(Suppl. 1), D355-D360. doi: 10.1093/nar/gkp896 PMID: 19880382
  78. Hu, B.; Lill, M.A. PharmDock: A pharmacophore-based docking program. J. Cheminform., 2014, 6(1), 14. doi: 10.1186/1758-2946-6-14 PMID: 24739488
  79. Subramanian, A.; Narayan, R.; Corsello, S.M.; Peck, D.D.; Natoli, T.E.; Lu, X.; Gould, J.; Davis, J.F.; Tubelli, A.A.; Asiedu, J.K.; Lahr, D.L.; Hirschman, J.E.; Liu, Z.; Donahue, M.; Julian, B.; Khan, M.; Wadden, D.; Smith, I.C.; Lam, D.; Liberzon, A.; Toder, C.; Bagul, M.; Orzechowski, M.; Enache, O.M.; Piccioni, F.; Johnson, S.A.; Lyons, N.J.; Berger, A.H.; Shamji, A.F.; Brooks, A.N.; Vrcic, A.; Flynn, C.; Rosains, J.; Takeda, D.Y.; Hu, R.; Davison, D.; Lamb, J.; Ardlie, K.; Hogstrom, L.; Greenside, P.; Gray, N.S.; Clemons, P.A.; Silver, S.; Wu, X.; Zhao, W.N.; Read-Button, W.; Wu, X.; Haggarty, S.J.; Ronco, L.V.; Boehm, J.S.; Schreiber, S.L.; Doench, J.G.; Bittker, J.A.; Root, D.E.; Wong, B.; Golub, T.R. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell, 2017, 171(6), 1437-1452.e17. doi: 10.1016/j.cell.2017.10.049 PMID: 29195078
  80. Sliwoski, G.; Kothiwale, S.; Meiler, J.; Lowe, E.W., Jr Computational methods in drug discovery. Pharmacol. Rev., 2014, 66(1), 334-395. doi: 10.1124/pr.112.007336 PMID: 24381236
  81. Wishart, D.S.; Feunang, Y.D.; Guo, A.C.; Lo, E.J.; Marcu, A.; Grant, J.R.; Sajed, T.; Johnson, D.; Li, C.; Sayeeda, Z.; Assempour, N.; Iynkkaran, I.; Liu, Y.; Maciejewski, A.; Gale, N.; Wilson, A.; Chin, L.; Cummings, R.; Le, D.; Pon, A.; Knox, C.; Wilson, M. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res., 2018, 46(D1), D1074-D1082. doi: 10.1093/nar/gkx1037 PMID: 29126136
  82. Sawyer, J.S.; Anderson, B.D.; Beight, D.W.; Campbell, R.M.; Jones, M.L.; Herron, D.K.; Lampe, J.W.; McCowan, J.R.; McMillen, W.T.; Mort, N.; Parsons, S.; Smith, E.C.R.; Vieth, M.; Weir, L.C.; Yan, L.; Zhang, F.; Yingling, J.M. Synthesis and activity of new aryl- and heteroaryl-substituted pyrazole inhibitors of the transforming growth factor-β type I receptor kinase domain. J. Med. Chem., 2003, 46(19), 3953-3956. doi: 10.1021/jm0205705 PMID: 12954047
  83. Siramshetty, V.B.; Nickel, J.; Omieczynski, C.; Gohlke, B.O.; Drwal, M.N.; Preissner, R. WITHDRAWN—a resource for withdrawn and discontinued drugs. Nucleic Acids Res., 2016, 44(D1), D1080-D1086. doi: 10.1093/nar/gkv1192 PMID: 26553801
  84. Allen, W.J.; Balius, T.E.; Mukherjee, S.; Brozell, S.R.; Moustakas, D.T.; Lang, P.T.; Case, D.A.; Kuntz, I.D.; Rizzo, R.C. DOCK 6: Impact of new features and current docking performance. J. Comput. Chem., 2015, 36(15), 1132-1156. doi: 10.1002/jcc.23905 PMID: 25914306
  85. Bhattacharya, D.; Nowotny, J.; Cao, R.; Cheng, J. 3Drefine: An interactive web server for efficient protein structure refinement. Nucleic Acids Res., 2016, 44(W1), W406-W409. doi: 10.1093/nar/gkw336 PMID: 27131371
  86. Corona virus SARS-CoV-2 disease COVID-19: infection, prevention and clinical advances of the prospective chemical drug therapeutics. Chem. Biol. Lett., 2020, 7, 63-72.
  87. Hamada, A.; Nauertz, A. In vitro antiviral activity of clove and ginger aqueous extracts against feline calicivirus, a surrogate for human norovirus. J. Food Prot., 2016, 79(6), 1001-12. doi: 10.4315/0362-028X.JFP-15-593
  88. Verma, J.; Khedkar, V.; Coutinho, E. 3D-QSAR in drug design-a review. Curr. Top. Med. Chem., 2010, 10(1), 95-115. doi: 10.2174/156802610790232260 PMID: 19929826
  89. UniProt. The universal protein knowledgebase. Nucleic Acids Res., 2018, 46(5), 2699. doi: 10.1093/nar/gky092 PMID: 29425356
  90. Tsou, J.H.; Chang, K.C.; Chang-Liao, P.Y.; Yang, S.T.; Lee, C.T.; Chen, Y.P.; Lee, Y.C.; Lin, B.W.; Lee, J.C.; Shen, M.R.; Chuang, C.K.; Chang, W.C.; Wang, J.M.; Hung, L.Y. Aberrantly expressed AURKC enhances the transformation and tumourigenicity of epithelial cells. J. Pathol., 2011, 225(2), 243-254. doi: 10.1002/path.2934 PMID: 21710690
  91. Webb, B.; Sali, A. Comparative protein structure modeling using MODELLER. Curr. Protoc. Bioinformat., 2016, 54(1), 6.1-37. doi: 10.1002/cpbi.3 PMID: 27322406
  92. Vasaikar, S.; Bhatia, P.; Bhatia, P.; Chu Yaiw, K. Complementary approaches to existing target based drug discovery for identifying novel drug targets. Biomedicines, 2016, 4(4), 27. doi: 10.3390/biomedicines4040027 PMID: 28536394
  93. Biasini, M.; Bienert, S.; Waterhouse, A.; Arnold, K.; Studer, G.; Schmidt, T.; Kiefer, F.; Cassarino, T.G.; Bertoni, M.; Bordoli, L.; Schwede, T. SWISS-MODEL: Modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res., 2014, 42(W1), W252-W258. doi: 10.1093/nar/gku340 PMID: 24782522
  94. Abdolmaleki, A.; Ghasemi, F.; Ghasemi, J.B. Computer-aided drug design to explore cyclodextrin therapeutics and biomedical applications. Chem. Biol. Drug Des., 2017, 89(2), 257-268. doi: 10.1111/cbdd.12825 PMID: 28205401
  95. Gutmanas, A.; Alhroub, Y.; Battle, G.M.; Berrisford, J.M.; Bochet, E.; Conroy, M.J.; Dana, J.M.; Fernandez Montecelo, M.A.; van Ginkel, G.; Gore, S.P.; Haslam, P.; Hatherley, R.; Hendrickx, P.M.S.; Hirshberg, M.; Lagerstedt, I.; Mir, S.; Mukhopadhyay, A.; Oldfield, T.J.; Patwardhan, A.; Rinaldi, L.; Sahni, G.; Sanz-García, E.; Sen, S.; Slowley, R.A.; Velankar, S.; Wainwright, M.E.; Kleywegt, G.J. PDBe: Protein data bank in Europe. Nucleic Acids Res., 2014, 42(D1), D285-D291. doi: 10.1093/nar/gkt1180 PMID: 24288376
  96. Barradas-Bautista, D.; Rosell, M.; Pallara, C.; Fernández-Recio, J. Structural prediction of protein--protein interactions by docking: Application to biomedical problems. Adv. Protein Chem. Struct. Biol., 2018, 110, 203-249. doi: 10.1016/bs.apcsb.2017.06.003 PMID: 29412997
  97. Choi, S.; Choi, K.Y. Screening-based approaches to identify small molecules that inhibit protein–protein interactions. Expert Opin. Drug Discov., 2017, 12(3), 293-303. doi: 10.1080/17460441.2017.1280456 PMID: 28067063
  98. Kinjo, A.R.; Suzuki, H.; Yamashita, R.; Ikegawa, Y.; Kudou, T.; Igarashi, R.; Kengaku, Y.; Cho, H.; Standley, D.M.; Nakagawa, A.; Nakamura, H. Protein Data Bank Japan (PDBj): Maintaining a structural data archive and resource description framework format. Nucleic Acids Res., 2012, 40(D1), D453-D460. doi: 10.1093/nar/gkr811 PMID: 21976737
  99. Chu, C.M.; Cheng, V.C.C.; Hung, I.F.N.; Wong, M.M.L.; Chan, K.H.; Chan, K.S.; Kao, R.Y.; Poon, L.L.; Wong, C.L.; Guan, Y.; Peiris, J.S.; Yuen, K.Y. Role of lopinavir/ritonavir in the treatment of SARS: Initial virological and clinical findings. Thorax, 2004, 59(3), 252-256. doi: 10.1136/thorax.2003.012658 PMID: 14985565
  100. Pruitt, K.D.; Tatusova, T.; Maglott, D.R. NCBI reference sequences (RefSeq): A curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res., 2007, 35(Database), D61-D65. doi: 10.1093/nar/gkl842 PMID: 17130148
  101. Swamidass, S.J. Mining small-molecule screens to repurpose drugs. Brief. Bioinform., 2011, 12(4), 327-335. doi: 10.1093/bib/bbr028 PMID: 21715466
  102. Ooms, F. Molecular modeling and computer aided drug design. Examples of their applications in medicinal chemistry. Curr. Med. Chem., 2000, 7(2), 141-158. doi: 10.2174/0929867003375317 PMID: 10637360
  103. Kimura, M.; Matsuda, Y.; Yoshioka, T.; Okano, Y. Cell cycle-dependent expression and centrosome localization of a third human aurora/Ipl1-related protein kinase, AIK3. J. Biol. Chem., 1999, 274(11), 7334-7340. doi: 10.1074/jbc.274.11.7334 PMID: 10066797
  104. Pirhadi, S.; Shiri, F.; Ghasemi, J.B. Methods and applications of structure based pharmacophores in drug discovery. Curr. Top. Med. Chem., 2013, 13(9), 1036-1047. doi: 10.2174/1568026611313090006 PMID: 23651482
  105. Gopalakrishnan, K.; Sowmiya, G.; Sheik, S.S.; Sekar, K. Ramachandran plot on the web (2.0). Protein Pept. Lett., 2007, 14(7), 669-671. doi: 10.2174/092986607781483912 PMID: 17897092
  106. Hess, B.; Kutzner, C.; van der Spoel, D.; Lindahl, E. GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation. J. Chem. Theory Comput., 2008, 4(3), 435-447. doi: 10.1021/ct700301q PMID: 26620784
  107. Zhu, M.; Gao, L.; Li, X.; Liu, Z.; Xu, C.; Yan, Y.; Walker, E.; Jiang, W.; Su, B.; Chen, X.; Lin, H. The analysis of the drug–targets based on the topological properties in the human protein–protein interaction network. J. Drug Target., 2009, 17(7), 524-532. doi: 10.1080/10611860903046610 PMID: 19530902
  108. Shim, J.S.; Liu, J.O. Recent advances in drug repositioning for the discovery of new anticancer drugs. Int. J. Biol. Sci., 2014, 10(7), 654-663. doi: 10.7150/ijbs.9224 PMID: 25013375
  109. Irwin, J.J.; Shoichet, B.K. ZINC--a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model., 2005, 45(1), 177-182. doi: 10.1021/ci049714+ PMID: 15667143
  110. Ashburn, T.T.; Thor, K.B. Drug repositioning: Identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov., 2004, 3(8), 673-683. doi: 10.1038/nrd1468 PMID: 15286734
  111. Di Tommaso, P.; Moretti, S.; Xenarios, I.; Orobitg, M.; Montanyola, A.; Chang, J.M.; Taly, J.F.; Notredame, C. T-Coffee: A web server for the multiple sequence alignment of protein and RNA sequences using structural information and homology extension. Nucleic Acids Res., 2011, 39(Suppl. 1), W13-W17. doi: 10.1093/nar/gkr245 PMID: 21558174
  112. Foroutan, M.; Fatemi, S.M.; Esmaeilian, F. A review of the structure and dynamics of nanoconfined water and ionic liquids via molecular dynamics simulation. Eur. Phys. J. E, 2017, 40(2), 19. doi: 10.1140/epje/i2017-11507-7 PMID: 28229319
  113. Ma, H.; Zhao, H. Drug target inference through pathway analysis of genomics data. Adv. Drug Deliv. Rev., 2013, 65(7), 966-972. doi: 10.1016/j.addr.2012.12.004 PMID: 23369829
  114. Kim, H.S. Drug repositioning: Exploring new indications for existing drug-disease relationships. Endocrinol. Metab., 2022, 37(1), 62-64. doi: 10.3803/EnM.2022.1403 PMID: 35255602
  115. Pettersen, E.F.; Goddard, T.D.; Huang, C.C.; Couch, G.S.; Greenblatt, D.M.; Meng, E.C.; Ferrin, T.E. UCSF Chimera?A visualization system for exploratory research and analysis. J. Comput. Chem., 2004, 25(13), 1605-1612. doi: 10.1002/jcc.20084 PMID: 15264254
  116. Younes, N.; Al-Sadeq, D.W. AL-Jighefee, H.; Younes, S.; Al-Jamal, O.; Daas, H.I.; Yassine, H.M.; Nasrallah, G.K. Challenges in laboratory diagnosis of the novel coronavirus SARS-CoV-2. Viruses, 2020, 12(6), 582. doi: 10.3390/v12060582 PMID: 32466458

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2024 Bentham Science Publishers