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Elucidating the Efficacy of Clinical Drugs Using FMO

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Recent Advances of the Fragment Molecular Orbital Method

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Abstract

It is known that first-principles based fragment molecular orbital (FMO) theory can be used to study protein–ligand interactions quantitatively. This includes obtaining amino acid residue-wise interactions with the ligand and detailed interaction components such as electrostatic, dispersion, charge-transfer, and exchange-repulsion. This chapter discusses the ability of FMO calculations on elucidating the efficacy of the clinical drugs that are available in the market. For this purpose, two kinds of clinical drugs, dipeptidyl peptidase IV (DPP-4) inhibitors (sitagliptin, linagliptin, alogliptin, teneligliptin, omarigliptin, and trelagliptin) and peroxisome proliferator-activated receptor-α (PPARα) modulators (fenofibrate and pemafibrate), were considered. The FMO calculations on relevant protein-drug complexes were made at the correlated Resolution-of-Identify second-order Moller Plesset (RI-MP2) level of theory utilizing correlation consistent double-zeta (cc-pVDZ) basis set. The results discussed here clearly reveal that interfragment interaction energies obtained using FMO calculations correlate significantly with the activity of the drugs and hence the activity of the drugs can be positively identified through FMO calculations. The results presented here further encourages that this novel calculation approach can be used for other types of drugs too to study the efficacy of the clinical as well as potential drugs.

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References

  1. Gordon MS, Fedorov DG, Pruitt SR, Slipchenko LV (2012) Fragmentation methods: a route to accurate calculations on large systems. Chem Rev 112(1):632–672

    Article  CAS  PubMed  Google Scholar 

  2. Kitaura K, Ikeo E, Asada T, Nakano T, Uebayasi M (1999) Fragment molecular orbital method: an approximate computational method for large molecules. Chem Phys Lett 313(3):701–706

    Article  CAS  Google Scholar 

  3. Fedorov DG, Kitaura K (2007) Extending the power of quantum chemistry to large systems with the fragment molecular orbital method. The J Phys Chem A 111(30):6904–6914

    Article  CAS  PubMed  Google Scholar 

  4. Fedorov DG, Nagata T, Kitaura K (2012) Exploring chemistry with the fragment molecular orbital method. Phys Chem Chem Phys 14(21):7562–7577

    Article  CAS  PubMed  Google Scholar 

  5. Otsuka T, Okimoto N, Taiji M (2015) Assessment and acceleration of binding energy calculations for protein-ligand complexes by the fragment molecular orbital method. J Comput Chem 36(30):2209–2218

    Article  CAS  PubMed  Google Scholar 

  6. Heifetz A, Trani G, Aldeghi M, MacKinnon CH, McEwan PA, Brookfield FA, Chudyk EI, Bodkin M, Pei Z, Burch JD, Ortwine DF (2016) Fragment molecular orbital method applied to lead optimization of novel interleukin-2 inducible T-cell kinase (ITK) inhibitors. J Med Chem 59(9):4352–4363

    Article  CAS  PubMed  Google Scholar 

  7. Minami A, Ishibashi S, Ikeda K, Ishitsubo E, Hori T, Tokiwa H, Taguchi R, Ieno D, Otsubo T, Matsuda Y, Sai S, Inada M, Suzuki T (2013) Catalytic preference of salmonella typhimurium LT2 sialidase for N-acetylneuraminic acid residues over N-glycolylneuraminic acid residues. FEBS Open Bio 3:231–236

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Fedorov DG, Kitaura K (2016) Subsystem analysis for the fragment molecular orbital method and its application to protein-ligand binding in solution. The J Phys Chem A 120(14):2218–2231

    Article  CAS  PubMed  Google Scholar 

  9. Sriwilaijaroen N, Magesh S, Imamura A, Ando H, Ishida H, Sakai M, Ishitsubo E, Hori T, Moriya S, Ishikawa T, Kuwata K, Odagiri T, Tashiro M, Hiramatsu H, Tsukamoto K, Miyagi T, Tokiwa H, Kiso M, Suzuki Y (2016) A novel potent and highly specific inhibitor against influenza viral N1–N9 neuraminidases: insight into neuraminidase-inhibitor interactions. J Med Chem 59(10):4563–4577

    Article  CAS  PubMed  Google Scholar 

  10. Tanaka S, Mochizuki Y, Komeiji Y, Okiyama Y, Fukuzawa K (2014) Electron-correlated fragment-molecular-orbital calculations for biomolecular and nano systems. Phys Chem Chem Phys 16(22):10310–10344

    Article  CAS  PubMed  Google Scholar 

  11. Fukuzawa K, Watanabe C, Kurisaki I, Taguchi N, Mochizuki Y, Nakano T, Tanaka S, Komeiji Y (2014) Accuracy of the fragment molecular orbital (FMO) calculations for DNA: Total energy, molecular orbital, and inter-fragment interaction energy. Comput Theor Chem 1034:7–16

    Article  CAS  Google Scholar 

  12. Heifetz A, Chudyk EI, Gleave L, Aldeghi M, Cherezov V, Fedorov DG, Biggin PC, Bodkin MJ (2016) The fragment molecular orbital method reveals new insight into the chemical nature of GPCR–ligand interactions. J Chem Inf Model 56(1):159–172

    Article  CAS  PubMed  Google Scholar 

  13. Mazanetz MP, Ichihara O, Law RJ, Whittaker M (2011) Prediction of cyclin-dependent kinase 2 inhibitor potency using the fragment molecular orbital method. J Cheminformatics 3(1):2

    Article  CAS  Google Scholar 

  14. Tagami U, Takahashi K, Igarashi S, Ejima C, Yoshida T, Takeshita S, Miyanaga W, Sugiki M, Tokumasu M, Hatanaka T, Kashiwagi T, Ishikawa K, Miyano H, Mizukoshi T (2016) Interaction analysis of FABP4 inhibitors by X-ray crystallography and fragment molecular orbital analysis. ACS Med Chem Lett 7(4):435–439

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Barker JJ, Barker O, Courtney SM, Gardiner M, Hesterkamp T, Ichihara O, Mather O, Montalbetti CA, Muller A, Varasi M, Whittaker M, Yarnold CJ (2010) Discovery of a novel Hsp90 inhibitor by fragment linking. ChemMedChem 5(10):1697–1700

    Article  CAS  PubMed  Google Scholar 

  16. Hitaoka S, Matoba H, Harada M, Yoshida T, Tsuji D, Hirokawa T, Itoh K, Chuman H (2011) Correlation analyses on binding affinity of sialic acid analogues and anti-influenza drugs with human neuraminidase using ab initio MO calculations on their complex structures–LERE-QSAR analysis (IV). J Chem Inf Model 51(10):2706–2716

    Article  CAS  PubMed  Google Scholar 

  17. Choi J, Kim H-J, Jin X, Lim H, Kim S, Roh I-S, Kang H-E, No KT, Sohn H-J (2018) Application of the fragment molecular orbital method to discover novel natural products for prion disease. Sci Rep 8(1):13063

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Arulmozhiraja S, Matsuo N, Ishitsubo E, Okazaki S, Shimano H, Tokiwa H (2016) Comparative binding analysis of dipeptidyl peptidase IV (DPP-4) with antidiabetic drugs—an ab initio fragment molecular orbital study. PLoS ONE 11(11):e0166275

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Yamamoto Y, Takei K, Arulmozhiraja S, Sladek V, Matsuo N, Han S-I, Matsuzaka T, Sekiya M, Tokiwa T, Shoji M, Shigeta Y, Nakagawa Y, Tokiwa H, Shimano H (2018) Molecular association model of PPARα and its new specific and efficient ligand, pemafibrate: structural basis for SPPARMα. Biochem Biophys Res Commun 499(2):239–245

    Article  CAS  PubMed  Google Scholar 

  20. Ishikawa T, Kuwata K (2009) Fragment molecular orbital calculation using the RI-MP2 method. Chem Phys Lett 474(1):195–198

    Article  CAS  Google Scholar 

  21. Feyereisen M, Fitzgerald G, Komornicki A (1993) Use of approximate integrals in ab initio theory. An application in MP2 energy calculations. Chem Phys Lett 208(5):359–363

    Google Scholar 

  22. Vahtras O, Almlöf J, Feyereisen MW (1993) Integral approximations for LCAO-SCF calculations. Chem Phys Lett 213(5):514–518

    Article  CAS  Google Scholar 

  23. Bernholdt DE, Harrison RJ (1996) Large-scale correlated electronic structure calculations: the RI-MP2 method on parallel computers. Chem Phys Lett 250(5):477–484

    Article  CAS  Google Scholar 

  24. Dunning TH (1989) Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen. The J Chem Phys 90(2):1007–1023

    Google Scholar 

  25. International Diabetes Federation. IDF Diabetes Atlas, 8th edn. https://www.diabetesatlas.org/

  26. World Health Organization (2016) Global report on diabetes. World Health Organization, France

    Google Scholar 

  27. Kieffer TJ, McIntosh CH, Pederson RA (1995) Degradation of glucose-dependent insulinotropic polypeptide and truncated glucagon-like peptide 1 in vitro and in vivo by dipeptidyl peptidase IV. Endocrinology 136(8):3585–3596

    Article  CAS  PubMed  Google Scholar 

  28. Deacon CF, Johnsen AH, Holst JJ (1995) Degradation of glucagon-like peptide-1 by human plasma in vitro yields an N-terminally truncated peptide that is a major endogenous metabolite in vivo. The J Clin Endocrinol Metab 80(3):952–957

    CAS  PubMed  Google Scholar 

  29. Rosenstock J, Zinman B (2007) Dipeptidyl peptidase-4 inhibitors and the management of type 2 diabetes mellitus. Curr Opin Endocrinol Diabetes Obes 14(2):98–107

    Article  CAS  PubMed  Google Scholar 

  30. Cahn A, Cernea S, Raz I (2016) An update on DPP-4 inhibitors in the management of type 2 diabetes. Expert Opin Emerg Drugs 21(4):409–419

    Article  CAS  PubMed  Google Scholar 

  31. Kim D, Wang L, Beconi M, Eiermann GJ, Fisher MH, He H, Hickey GJ, Kowalchick JE, Leiting B, Lyons K, Marsilio F, McCann ME, Patel RA, Petrov A, Scapin G, Patel SB, Roy RS, Wu JK, Wyvratt MJ, Zhang BB, Zhu L, Thornberry NA, Weber AE (2005) (2R)-4-Oxo-4-[3-(trifluoromethyl)-5,6-dihydro[1,2,4]triazolo[4,3-a]pyrazin- 7(8H)-yl]-1-(2,4,5-trifluorophenyl)butan-2-amine: a potent, orally active dipeptidyl peptidase IV inhibitor for the treatment of type 2 diabetes. J Med Chem 48(1):141–151

    Article  CAS  PubMed  Google Scholar 

  32. Eckhardt M, Langkopf E, Mark M, Tadayyon M, Thomas L, Nar H, Pfrengle W, Guth B, Lotz R, Sieger P, Fuchs H, Himmelsbach F (2007) 8-(3-(R)-Aminopiperidin-1-yl)-7-but-2-ynyl-3-methyl-1-(4-methyl-quinazolin-2-ylmethyl)-3,7-dihydropurine-2,6-dione (BI 1356), a highly potent, selective, long-acting, and orally bioavailable DPP-4 inhibitor for the treatment of type 2 diabetes. J Med Chem 50(26):6450–6453

    Article  CAS  PubMed  Google Scholar 

  33. Zhang Z, Wallace MB, Feng J, Stafford JA, Skene RJ, Shi L, Lee B, Aertgeerts K, Jennings A, Xu R, Kassel DB, Kaldor SW, Navre M, Webb DR, Gwaltney SL (2011) Design and synthesis of pyrimidinone and pyrimidinedione inhibitors of dipeptidyl peptidase IV. J Med Chem 54(2):510–524

    Article  CAS  PubMed  Google Scholar 

  34. Yoshida T, Akahoshi F, Sakashita H, Kitajima H, Nakamura M, Sonda S, Takeuchi M, Tanaka Y, Ueda N, Sekiguchi S, Ishige T, Shima K, Nabeno M, Abe Y, Anabuki J, Soejima A, Yoshida K, Takashina Y, Ishii S, Kiuchi S, Fukuda S, Tsutsumiuchi R, Kosaka K, Murozono T, Nakamaru Y, Utsumi H, Masutomi N, Kishida H, Miyaguchi I, Hayashi Y (2012) Discovery and preclinical profile of teneligliptin (3-[(2S,4S)-4-[4-(3-methyl-1-phenyl-1H-pyrazol-5-yl)piperazin-1-yl]pyrrolidin-2-y lcarbonyl]thiazolidine): a highly potent, selective, long-lasting and orally active dipeptidyl peptidase IV inhibitor for the treatment of type 2 diabetes. Bioorg Med Chem 20(19):5705–5719

    Article  CAS  PubMed  Google Scholar 

  35. Biftu T, Sinha-Roy R, Chen P, Qian X, Feng D, Kuethe JT, Scapin G, Gao YD, Yan Y, Krueger D, Bak A, Eiermann G, He J, Cox J, Hicks J, Lyons K, He H, Salituro G, Tong S, Patel S, Doss G, Petrov A, Wu J, Xu SS, Sewall C, Zhang X, Zhang B, Thornberry NA, Weber AE (2014) Omarigliptin (MK-3102): a novel long-acting DPP-4 inhibitor for once-weekly treatment of type 2 diabetes. J Med Chem 57(8):3205–3212

    Article  CAS  PubMed  Google Scholar 

  36. Grimshaw CE, Jennings A, Kamran R, Ueno H, Nishigaki N, Kosaka T, Tani A, Sano H, Kinugawa Y, Koumura E, Shi L, Takeuchi K (2016) Trelagliptin (SYR-472, Zafatek), novel once-weekly treatment for type 2 diabetes, inhibits dipeptidyl peptidase-4 (DPP-4) via a non-covalent mechanism. PLoS ONE 11(6):e0157509

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Schechter I, Berger A (1967) On the size of the active site in proteases. I. Papain. Biochem Biophys Res Commun 27(2):157–162

    Article  CAS  PubMed  Google Scholar 

  38. Nabeno M, Akahoshi F, Kishida H, Miyaguchi I, Tanaka Y, Ishii S, Kadowaki T (2013) A comparative study of the binding modes of recently launched dipeptidyl peptidase IV inhibitors in the active site. Biochem Biophys Res Commun 434(2):191–196

    Article  CAS  PubMed  Google Scholar 

  39. Schnapp G, Klein T, Hoevels Y, Bakker RA, Nar H (2016) Comparative analysis of binding kinetics and thermodynamics of dipeptidyl peptidase-4 inhibitors and their relationship to structure. J Med Chem 59(16):7466–7477

    Article  CAS  PubMed  Google Scholar 

  40. Shimano H, Sato R (2017) SREBP-regulated lipid metabolism: convergent physiology—divergent pathophysiology. Nat Rev. Endocrinol 13(12):710–730

    Article  CAS  PubMed  Google Scholar 

  41. Brelivet Y, Rochel N, Moras D (2012) Structural analysis of nuclear receptors: from isolated domains to integral proteins. Mol Cell Endocrinol 348(2):466–473

    Article  CAS  PubMed  Google Scholar 

  42. Kota BP, Huang TH, Roufogalis BD (2005) An overview on biological mechanisms of PPARs. Pharmacol Res 51(2):85–94

    Article  CAS  PubMed  Google Scholar 

  43. Desvergne B, Wahli W (1999) Peroxisome proliferator-activated receptors: nuclear control of metabolism. Endocr Rev 20(5):649–688

    CAS  PubMed  Google Scholar 

  44. Diabetes Atherosclerosis Intervention Study Investigators (2001) Effect of fenofibrate on progression of coronary-artery disease in type 2 diabetes: the diabetes atherosclerosis intervention study, a randomised study. Lancet (London, England), 357(9260):905–910

    Google Scholar 

  45. Bloomfield Rubins H, Davenport J, Babikian V, Brass LM, Collins D, Wexler L, Wagner S, Papademetriou V, Rutan G, Robins SJ (2001) Reduction in stroke with gemfibrozil in men with coronary heart disease and low HDL cholesterol: the veterans affairs HDL intervention trial (VA-HIT). Circulation 103(23):2828–2833

    Article  CAS  PubMed  Google Scholar 

  46. Prevention, Bezafibrate Infarction (2000) Secondary prevention by raising HDL cholesterol and reducing triglycerides in patients with coronary artery disease. Circulation 102(1):21–27

    Google Scholar 

  47. Raza-Iqbal S, Tanaka T, Anai M, Inagaki T, Matsumura Y, Ikeda K, Taguchi A, Gonzalez FJ, Sakai J, Kodama T (2015) Transcriptome analysis of K-877 (a novel selective PPARalpha modulator (SPPARMalpha))-regulated genes in primary human hepatocytes and the mouse liver. J Atherosclerosis Thromb 22(8):754–772

    Article  CAS  Google Scholar 

  48. Ishibashi S, Yamashita S, Arai H, Araki E, Yokote K, Suganami H, Fruchart JC, Kodama T (2016) Effects of K-877, a novel selective PPARalpha modulator (SPPARMalpha), in dyslipidaemic patients: a randomized, double blind, active- and placebo-controlled, phase 2 trial. Atherosclerosis 249:36–43

    Article  CAS  PubMed  Google Scholar 

  49. Hennuyer N, Duplan I, Paquet C, Vanhoutte J, Woitrain E, Touche V, Colin S, Vallez E, Lestavel S, Lefebvre P, Staels B (2016) The novel selective PPARalpha modulator (SPPARMalpha) pemafibrate improves dyslipidemia, enhances reverse cholesterol transport and decreases inflammation and atherosclerosis. Atherosclerosis 249:200–208

    Article  CAS  PubMed  Google Scholar 

  50. Fruchart JC (2013) Selective peroxisome proliferator-activated receptor alpha modulators (SPPARMalpha): the next generation of peroxisome proliferator-activated receptor alpha-agonists. Cardiovasc Diabetology 12:82

    Article  CAS  Google Scholar 

  51. Takei K, Han SI, Murayama Y, Satoh A, Oikawa F, Ohno H, Osaki Y, Matsuzaka T, Sekiya M, Iwasaki H, Yatoh S, Yahagi N, Suzuki H, Yamada N, Nakagawa Y, Shimano H (2017) Selective peroxisome proliferator-activated receptor-alpha modulator K-877 efficiently activates the peroxisome proliferator-activated receptor-alpha pathway and improves lipid metabolism in mice. J Diab Invest 8(4):446–452

    Article  CAS  Google Scholar 

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Acknowledgments

This work has been supported by AMED-CREST #18gm0910003, and JSPS KAKENHI Grant Number 17H06395 and 15H02541 (H.S.). The computations in this work were performed using the Research Center for Computational Science, Okazaki, Japan; the Center for Computational Sciences (CCS) at University of Tsukuba, Japan; and the facilities of the Supercomputer Center, the Institute for Solid State Physics, The University of Tokyo, Japan.

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Authors and Affiliations

  1. Department of Chemistry, Rikkyo University, 3-34-1 Nishi-Ikebukuro, Toshima, Tokyo 171-8501, Japan

    Sundaram Arulmozhiraja & Hiroaki Tokiwa

  2. Department of Internal Medicine (Endocrinology and Metabolism), Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki 305-8575, Japan

    Hitoshi Shimano

  3. International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki 305-8575, Japan

    Hitoshi Shimano

  4. Life Science Center for Survival Dynamics, Tsukuba Advanced Research Alliance (TARA), University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan

    Hitoshi Shimano

  5. AMED-CREST, Japan Agency for Medical Research and Development (AMED), 1-7-1, Ohte-machi, Chiyoda-ku, Tokyo 100-0004, Japan

    Hitoshi Shimano

Authors
  1. Sundaram Arulmozhiraja
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  2. Hiroaki Tokiwa
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  3. Hitoshi Shimano
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Corresponding author

Correspondence to Sundaram Arulmozhiraja .

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Editors and Affiliations

  1. Department of Chemistry, Faculty of Science, Rikkyo University, Tokyo, Japan

    Yuji Mochizuki

  2. Graduate School of System Informatics, Kobe University, Kobe, Japan

    Shigenori Tanaka

  3. School of Pharmacy and Pharmaceutical Sciences, Hoshi University, Tokyo, Japan

    Kaori Fukuzawa

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Arulmozhiraja, S., Tokiwa, H., Shimano, H. (2021). Elucidating the Efficacy of Clinical Drugs Using FMO. In: Mochizuki, Y., Tanaka, S., Fukuzawa, K. (eds) Recent Advances of the Fragment Molecular Orbital Method. Springer, Singapore. https://doi.org/10.1007/978-981-15-9235-5_16

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