Moleküler Modelleme ve Bilgisayar Destekli İlaç Tasarımı: İlaç Araştırmalarında Yer Alan Her Bilim İnsanının İhtiyaç Duyduğu ve Kolayca Edinebileceği Beceri Seti

Abstract

Moleküler modellemede son birkaç on yıldır tanıklık ettiğimiz baş döndürücü gelişmeler ilerleyen bilgisayar ve enformasyon teknolojileri ile birlikte gerçekleşmektedir. Bilgisayar destekli ilaç tasarımı (BDİT), bir kimyasalın ilaca dönüştürülmesi için gerekli zaman ve masraflar göz önüne alındığında, muhtemelen moleküler modellemenin en önemli alanıdır. Mevcut derlemede BDİT’nın farklı aşamalarındaki moleküler modelleme yöntemleri, araçları ve uygulamalarına, bazı başarı hikayelerine de atıfta bulunularak odaklanılmıştır. Ayrıca, farklı amaçlar için kullanılan faydalı veri tabanları ve ticari olmayan yazılımlar tanıtılmıştır. Derleme, bu yöntemlerle ilgili, ilaç araştırmalarının herhangi bir alanında görev alan bilim insanlarına fikir vermeyi ve herkesin bu yöntemlerden, mevcut çok sayıda ücretsiz yazılım ve dokümantasyon ile en iyi şekilde faydalanabileceğini göstermeyi amaçlamaktadır.

Keywords

• moleküler modelleme,ilgisayar destekli ilaç tasarımı,sanal aktivite tarama,moleküler kenetleme,farmakofor modelleme,ADMET tahmini

Molecular Modelling and Computer Aided Drug Design: The Skill Set Every Scientist in Drug Research Needs and Can Easily Get

Abstract

The overwhelming advances we have been witnessing in molecular modelling for a couple of decades go hand in hand with the booming computer and information technologies. Computer-aided drug design (CADD) is probably the most important field of molecular modelling given the time scale and cost of turning a chemical entity into an approved drug. In this review we provide a brief definition of molecular modelling and CADD with historical corner stones. In this review methods, tools, and applications of molecular modelling in different stages of CADD were focused on by referring to a number of success stories. Useful data bases and non-commercial software for different purposes are also introduced. The review aims to provide a glimpse of these methods for scientists taking part in any field of drug research and to show that everyone can and should make the best of these methods with a vast amount of available free tools and documentation.

Keywords

• molecular modelling,computer-aided drug design,virtual screening,molecular docking,pharmacophore modelling,shape similarity,molecular dynamics simulations,ADMET prediction

References

1. 1. Dalkas GA, Vlachakis D, Tsagkrasoulis D, Kastania A, Kossida S: State-of-the-art technology in modern computer-aided drug design. Briefings in Bioinformatics 2013, 14(6):745-752.
2. 2. Ooms F: Molecular modeling and computer aided drug design. Examples of their applications in medicinal chemistry. Current Medicinal Chemistry 2000, 7(2):141-58.
3. 3. Kapetanovic IM: Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. Chemico-Biological Interactions 2008, 171(2):165-176.
4. 4. Leach AR: Molecular Modelling : Principles and Applications. Pearson Education Ltd.; Essex, UK, 2001. 5. Schlecht MF: Molecular Modeling on the PC. Wiley-VCH; New York, USA, 1997.
5. 6. Ramirez D: Computational Methods Applied to Rational Drug Design. The Open Medicinal Chemistry Journal 2016, 10:7-20.
6. 7. Blake RA: Target validation in drug discovery. Methods in Molecular Biology 2007, 356:367-77.
7. 8. Agamah FE, Mazandu GK, Hassan R, Bope CD, Thomford NE, Ghansah A, et al.: Computational/in silico methods in drug target and lead prediction. Briefings in Bioinformatics 2019, bbz103.
8. 9. Sahu TK, Pradhan D, Rao AR, Jena L: In silico site-directed mutagenesis of neutralizing mAb 4C4 and analysis of its interaction with G-H loop of VP1 to explore its therapeutic applications against FMD. Journal of Biomolecular Structure and Dynamics 2019, 37(10):2641-2651.

9. 10. Anand P, Nagarajan D, Mukherjee S, Chandra N: ABS-Scan: In silico alanine scanning mutagenesis for binding site residues in protein-ligand complex. F1000Research 2014, 3:214.
10. 11. Audouze K, Taboureau O: Chemical biology databases: from aggregation, curation to representation. Drug Discovery Today: Technologies 2015, 14:25-29.
11. 12. Hughes JP, Rees S, Kalindjian SB, Philpott KL: Principles of early drug discovery. British Journal of Pharmacology 2011, 162(6):1239-1249.
12. 13. Schneider N, Jackels C, Andres C, Hutter MC: Gradual in silico filtering for druglike substances. Journal of Chemical Information and Modeling 2008, 48(3):613-628.
13. 14. Baell J, Walters MA: Chemistry: Chemical con artists foil drug discovery. Nature 2014, 513(7519):481-483.
14. 15. Hay M, Thomas DW, Craighead JL, Economides C, Rosenthal J: Clinical development success rates for investigational drugs. Nature Biotechnology 2014, 32(1):40-51.
15. 16. Katsila T, Spyroulias GA, Patrinos GP, Matsoukas MT: Computational approaches in target identification and drug discovery. Computational and Structural Biotechnology Journal 2016, 14:177-184.
16. 17. Keseru GM, Makara GM: Hit discovery and hit-to-lead approaches. Drug Discovery Today 2006, 11(15-16):741-748.
17. 18. Zhao H. Scaffold selection and scaffold hopping in lead generation: a medicinal chemistry perspective. Drug Discovery Today 2007, 12(3-4):149-155.
18. 19. Subramaniyan B: QSAR and Lead Optimization. In: Puratchikody A, Lakshmana Prabu S, Umamaheswari A (eds.), Computer Applications in Drug Discovery and Development, IGI Global; Pennsylvania, USA. 2018: pp 21.
19. 20. Hosea NA, Jones HM: Predicting pharmacokinetic profiles using in silico derived parameters. Molecular Pharmaceutics 2013, 10(4):1207-1215.
20. 21. Daina A, Michielin O, Zoete V: SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports 2017, 7:42717.
21. 22. Chemi G, Gemma S, Campiani G, Brogi S, Butini S, Brindisi M: Computational Tool for Fast in silico Evaluation of hERG K(+) Channel Affinity. Frontiers in Chemistry 2017, 5:7.
22. 23. Bikadi Z, Hazai I, Malik D, Jemnitz K, Veres Z, Hari P, et al.: Predicting P-glycoprotein-mediated drug transport based on support vector machine and three-dimensional crystal structure of P-glycoprotein. PLoS One 2011, 6(10):e25815.
23. 24. Martin YC, Kofron JL, Traphagen LM: Do structurally similar molecules have similar biological activity? Journal of Medicinal Chemistry 2002, 45(19):4350-4358.
24. 25. Zoete V, Daina A, Bovigny C, Michielin O. SwissSimilarity: A Web Tool for Low to Ultra High Throughput Ligand-Based Virtual Screening. Journal of Chemical Information and Modeling 2016, 56(8):1399-1404.
25. 26. Bajorath J: Selected concepts and investigations in compound classification, molecular descriptor analysis, and virtual screening. Journal of Chemical Information and Modeling 2001, 41(2):233-245.
26. 27. Cereto-Massague A, Ojeda MJ, Valls C, Mulero M, Garcia-Vallve S, Pujadas G: Molecular fingerprint similarity search in virtual screening. Methods 2015, 71:58-63.
27. 28. Caporuscio F, Tafi A: Pharmacophore modelling: a forty year old approach and its modern synergies. Current Medicinal Chemistry 2011, 18(17):2543-2553.
28. 29. Kumar A, Zhang KYJ: Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery. Frontiers in Chemistry 2018, 6:315.
29. 30. Thijs G, Langenaeker W, De Winter H: Application of spectrophores™ to map vendor chemical space using self-organising maps. Journal of Cheminformatics 2011, 3(1):P7.
30. 31. Ballester PJ, Richards WG: Ultrafast shape recognition to search compound databases for similar molecular shapes. Journal of Computational Chemistry 2007, 28(10):1711-1723.
31. 32. Batool M, Ahmad B, Choi S: A Structure-Based Drug Discovery Paradigm. International Journal of Molecular Sciences 2019, 20(11): E2783.
32. 33. Muhammed MT, Aki-Yalcin E: Homology modeling in drug discovery: Overview, current applications, and future perspectives. Chemical Biology & Drug Design 2019, 93(1):12-20.
33. 34. Schwede T: Homology Modeling of Protein Structures. In: Roberts GCK (eds), Encyclopedia of Biophysics. Springer; Berlin, Germany. 2013: pp 992-998.
34. 35. Fiser A, Sali A: ModLoop: automated modeling of loops in protein structures. Bioinformatics 2003, 19(18):2500-2501.
35. 36. Liang S, Grishin NV: Side-chain modeling with an optimized scoring function. Protein Science 2002, 11(2):322-331.
36. 37. Saranyah K, Kalva S, Mukund N, Singh SK, Saleena LM: Homology modeling and in silico site directed mutagenesis of pyruvate ferredoxin oxidoreductase from Clostridium thermocellum. Combinatorial Chemistry & High Throughput Screening 2015, 18(10):975-989.
37. 38. Meng XY, Zhang HX, Mezei M, Cui M: Molecular docking: a powerful approach for structure-based drug discovery. Current Computer-Aided Drug Design 2011, 7(2):146-157.
38. 39. de Ruyck J, Brysbaert G, Blossey R, Lensink MF: Molecular docking as a popular tool in drug design, an in silico travel. Advances and Applications in Bioinformatics and Chemistry 2016, 9:1-11.
39. 40. 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. Journal of Medicinal Chemistry 2004, 47(7):1739-1749.
40. 41. Hargrove TY, Friggeri L, Wawrzak Z, Qi A, Hoekstra WJ, Schotzinger RJ, et al.: Structural analyses of Candida albicans sterol 14alpha-demethylase complexed with azole drugs address the molecular basis of azole-mediated inhibition of fungal sterol biosynthesis. Journal of Biological Chemistry 2017, 292(16):6728-6743.
41. 42. Haile JM: Molecular Dynamics Simulation: Elementary Methods. Wiley; Chichester, UK, 1992.
42. 43. Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, Legrand S, Beberg AL, et al.: Accelerating molecular dynamic simulation on graphics processing units. ournal of Computational Chemistry 2009, 30(6):864-872.
43. 44. Salmaso V, Moro S: Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview. Frontiers in Pharmacology 2018, 9:923.
44. 45. Wieder M, Perricone U, Seidel T, Langer T: Pharmacophore Models Derived From Molecular Dynamics Simulations of Protein-Ligand Complexes: A Case Study. Natural Product Communications 2016, 11(10):1499-1504.
45. 46. Ban TA: The role of serendipity in drug discovery. Dialogues in Clinical Neuroscience 2006, 8(3):335-344.
46. 47. Talele TT, Khedkar SA, Rigby AC: Successful applications of computer aided drug discovery: moving drugs from concept to the clinic. Current Topics in Medicinal Chemistry 2010, 10(1):127-141.
47. 48. Eriksson AE, Jones TA, Liljas A: Refined structure of human carbonic anhydrase II at 2.0 A resolution. Proteins 1988, 4(4):274-282.
48. 49. Baldwin JJ, Ponticello GS, Anderson PS, Christy ME, Murcko MA, Randall WC, et al.: Thienothiopyran-2-sulfonamides: novel topically active carbonic anhydrase inhibitors for the treatment of glaucoma. Journal of Medicinal Chemistry 1989, 32(12):2510-2513.
49. 50. Greer J, Erickson JW, Baldwin JJ, Varney MD: Application of the three-dimensional structures of protein target molecules in structure-based drug design. Journal of Medicinal Chemistry 1994, 37(8):1035-1054.
50. 51. McKimm-Breschkin JL: Influenza neuraminidase inhibitors: antiviral action and mechanisms of resistance. Influenza and Other Respiratory Viruses 2013, 7 Suppl 1:25-36.
51. 52. von Itzstein M, Wu WY, Kok GB, Pegg MS, Dyason JC, Jin B, et al.: Rational design of potent sialidase-based inhibitors of influenza virus replication. Nature 1993, 363(6428):418-423.
52. 53. Wolfenden R: Analog approaches to the structure of the transition state in enzyme reactions. Accounts of Chemical Research 1972, 5(1):10-18.
53. 54. Göschke R, Cohen NC, Wood JM, Maibaum J: Design and synthesis of novel 2,7-dialkyl substituted 5(S)-amino-4(S)-hydroxy-8-phenyl-octanecarboxamides as in vitro potent peptidomimetic inhibitors of human renin. Bioorganic & Medicinal Chemistry Letters 1997, 7(21):2735-2740.
54. 55. Rahuel J, Rasetti V, Maibaum J, Rueger H, Goschke R, Cohen NC, et al.: Structure-based drug design: the discovery of novel nonpeptide orally active inhibitors of human renin. Chemical Biology 2000, 7(7):493-504.