Revealing the Role of the Arg and Lys in Shifting Paradigm from BTK Selective Inhibition to the BTK/HCK Dual Inhibition - Delving into the Inhibitory Activity of KIN-8194 against BTK, and HCK in the Treatment of Mutated BTKCys481 Waldenström Macroglobulinemia: A Computational Approach


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Background:Despite the early success of Bruton's tyrosine kinase (BTK) inhibitors in the treatment of Waldenström macroglobulinemia (WM), these single-target drug therapies have limitations in their clinical applications, such as drug resistance. Several alternative strategies have been developed, including the use of dual inhibitors, to maximize the therapeutic potential of these drugs.

Objective:Recently, the pharmacological activity of KIN-8194 was repurposed to serve as a ‘dual-target’ inhibitor of BTK and Hematopoietic Cell Kinase (HCK). However, the structural dual inhibitory mechanism remains unexplored, hence the aim of this study.

Methods:Conducting predictive pharmacokinetic profiling of KIN-8194, as well as demonstrating a comparative structural mechanism of inhibition against the above-mentioned enzymes.

Results:Our results revealed favourable binding affinities of -20.17 kcal/mol, and -35.82 kcal/mol for KIN-8194 towards HCK and BTK, respectively. Catalytic residues Arg137/174 and Lys42/170 in BTK and Arg303 and Lys75/173/244/247 in HCK were identified as crucial mediators of the dual binding mechanism of KIN-8194, corroborated by high per-residue energy contributions and consistent high-affinity interactions of these residues. Prediction of the pharmacokinetics and physicochemical properties of KIN-8194 further established its inhibitory potential, evidenced by the favourable absorption, metabolism, excretion, and minimal toxicity properties. Structurally, KIN-8194 impacted the stability, flexibility, solvent-accessible surface area, and rigidity of BTK and HCK, characterized by various alterations observed in the bound and unbound structures, which proved enough to disrupt their biological function.

Conclusion:These structural insights provided a baseline for the understanding of the dual inhibitory activity of KIN- 8194. Establishing the cruciality of the interactions between the KIN-8194 and Arg and Lys residues could guide the structure-based design of novel dual BTK/HCK inhibitors with improved therapeutic activities.

Об авторах

Ghazi Elamin

Department of Pharmaceutical Sciences, University of KwaZulu-Natal

Email: info@benthamscience.net

Aimen Aljoundi

Department of Pharmaceutical Sciences, University of KwaZulu-Natal

Email: info@benthamscience.net

Mohamed Alahmdi

Department of Chemistry, Faculty of Science, University of Tabuk

Email: info@benthamscience.net

Nader Abo-Dya

Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tabuk University

Email: info@benthamscience.net

Mahmoud Soliman

Department of Pharmaceutical Sciences, University of KwaZulu-Natal

Автор, ответственный за переписку.
Email: info@benthamscience.net

Список литературы

  1. Ababneh, O.; Abushukair, H.; Qarqash, A.; Syaj, S.; al Hadidi, S. The use of bruton tyrosine kinase inhibitors in waldenström’s macroglobulinemia. Clin. Hematol. Int., 2022, 4(1-2), 21-29. doi: 10.1007/s44228-022-00007-5 PMID: 35950210
  2. Castillo, J.J.; Olszewski, A.J.; Kanan, S.; Meid, K.; Hunter, Z.R.; Treon, S.P. Overall survival and competing risks of death in patients with Waldenström macroglobulinaemia: An analysis of the surveillance, epidemiology and end results database. Br. J. Haematol., 2015, 169(1), 81-89. doi: 10.1111/bjh.13264 PMID: 25521528
  3. Jeong, S.; Kong, S.G.; Kim, D.J.; Lee, S.; Lee, H.S. Incidence, prevalence, mortality, and causes of death in Waldenström macroglobulinemia: a nationwide, population-based cohort study. BMC Cancer, 2020, 20(1), 623. doi: 10.1186/s12885-020-07120-9 PMID: 32620091
  4. Yin, X.; Chen, L.; Fan, F.; Yan, H.; Zhang, Y.; Huang, Z.; Sun, C.; Hu, Y. Trends in incidence and mortality of waldenström macroglobulinemia: A population-based study. Front. Oncol., 2020, 10, 1712. doi: 10.3389/fonc.2020.01712 PMID: 33014849
  5. Pophali, P.A.; Bartley, A.; Kapoor, P.; Gonsalves, W.I.; Ashrani, A.A.; Marshall, A.L.; Siddiqui, M.A.; Kyle, R.A.; Go, R.S. Prevalence and survival of smouldering Waldenström macroglobulinaemia in the United States. Br. J. Haematol., 2019, 184(6), 1014-1017. doi: 10.1111/bjh.15201 PMID: 29532912
  6. Kyle, R.A; Benson, J.T; Larson, D.R Progression in smoldering Waldenström macroglobulinemia: Long-term results. Blood, 2012, 119(19), 4462-4466. doi: 10.1182/blood-2011-10-384768
  7. Herrinton, L.J.; Weiss, N.S. Incidence of Waldenstrom’s macroglobulinemia. Blood, 1993, 82(10), 3148-3150. doi: 10.1182/blood.V82.10.3148.3148 PMID: 8219203
  8. Groves, F.D.; Travis, L.B.; Devesa, S.S.; Ries, L.A.; Fraumeni, J.F. Waldenström’s macroglobulinemia: incidence patterns in the United States, 1988-1994. Cancer, 1998, 82(6), 1078-1081. doi: 10.1002/(SICI)1097-0142(19980315)82:63.0.CO;2-3 PMID: 9506352
  9. Sekhar, J.; Sanfilippo, K.; Zhang, Q.; Trinkaus, K.; Vij, R.; Morgensztern, D. Waldenström macroglobulinemia: A surveillance, epidemiology, and end results database review from 1988 to 2005. Leuk. Lymphoma, 2012, 53(8), 1625-1626. doi: 10.3109/10428194.2012.656103 PMID: 22239669
  10. Kastritis, E.; Morel, P.; Duhamel, A.; Gavriatopoulou, M.; Kyrtsonis, M.C.; Durot, E.; Symeonidis, A.; Laribi, K.; Hatjiharissi, E.; Ysebaert, L.; Vassou, A.; Giannakoulas, N.; Merlini, G.; Repousis, P.; Varettoni, M.; Michalis, E.; Hivert, B.; Michail, M.; Katodritou, E.; Terpos, E.; Leblond, V.; Dimopoulos, M.A. A revised international prognostic score system for Waldenström’s macroglobulinemia. Leukemia, 2019, 33(11), 2654-2661. doi: 10.1038/s41375-019-0431-y PMID: 31118465
  11. Dimopoulos, M.A.; Tedeschi, A.; Trotman, J.; García-Sanz, R.; Macdonald, D.; Leblond, V.; Mahe, B.; Herbaux, C.; Tam, C.; Orsucci, L.; Palomba, M.L.; Matous, J.V.; Shustik, C.; Kastritis, E.; Treon, S.P.; Li, J.; Salman, Z.; Graef, T.; Buske, C. Phase 3 Trial of Ibrutinib plus Rituximab in Waldenström’s macroglobulinemia. N. Engl. J. Med., 2018, 378(25), 2399-2410. doi: 10.1056/NEJMoa1802917 PMID: 29856685
  12. Moreno, D.F.; de Larrea, C.F.; Castillo, J.J. New treatment strategies for Waldenström macroglobulinemia. Clin. Adv. Hematol. Oncol., 2022, 20(8), 506-515. http://www.ncbi.nlm.nih.gov/pubmed/36125957 PMID: 36125957
  13. De, S.K. Fundamentals of cancer detection, treatment, and prevention; WILEY-VCH: New Jersey, 2022, pp. 67-131. doi: 10.1002/9783527838561
  14. Trotman, J.; Opat, S.; Gottlieb, D.; Simpson, D.; Marlton, P.; Cull, G.; Munoz, J.; Tedeschi, A.; Roberts, A.W.; Seymour, J.F.; Atwal, S.K.; Yu, Y.; Novotny, W.; Holmgren, E.; Tan, Z.; Hilger, J.D.; Huang, J.; Tam, C.S. Zanubrutinib for the treatment of patients with Waldenström macroglobulinemia: 3 years of follow-up. Blood, 2020, 136(18), 2027-2037. doi: 10.1182/blood.2020006449 PMID: 32698195
  15. Yang, G.; Buhrlage, S.J.; Tan, L.; Liu, X.; Chen, J.; Xu, L.; Tsakmaklis, N.; Chen, J.G.; Patterson, C.J.; Brown, J.R.; Castillo, J.J.; Zhang, W.; Zhang, X.; Liu, S.; Cohen, P.; Hunter, Z.R.; Gray, N.; Treon, S.P. HCK is a survival determinant transactivated by mutated MYD88, and a direct target of ibrutinib. Blood, 2016, 127(25), 3237-3252. doi: 10.1182/blood-2016-01-695098 PMID: 27143257
  16. Treon, S.P.; Xu, L.; Guerrera, M.L.; Jimenez, C.; Hunter, Z.R.; Liu, X.; Demos, M.; Gustine, J.; Chan, G.; Munshi, M.; Tsakmaklis, N.; Chen, J.G.; Kofides, A.; Sklavenitis-Pistofidis, R.; Bustoros, M.; Keezer, A.; Meid, K.; Patterson, C.J.; Sacco, A.; Roccaro, A.; Branagan, A.R.; Yang, G.; Ghobrial, I.M.; Castillo, J.J. Genomic landscape of Waldenström macroglobulinemia and its impact on treatment strategies. J. Clin. Oncol., 2020, 38(11), 1198-1208. doi: 10.1200/JCO.19.02314 PMID: 32083995
  17. Taguchi, T.; Kiyokawa, N.; Sato, N.; Saito, M.; Fujimoto, J. Characteristic expression of Hck in human B-cell precursors. Exp. Hematol., 2000, 28(1), 55-64. doi: 10.1016/S0301-472X(99)00127-7 PMID: 10658677
  18. Treon, S.P.; Tripsas, C.K.; Meid, K.; Warren, D.; Varma, G.; Green, R.; Argyropoulos, K.V.; Yang, G.; Cao, Y.; Xu, L.; Patterson, C.J.; Rodig, S.; Zehnder, J.L.; Aster, J.C.; Harris, N.L.; Kanan, S.; Ghobrial, I.; Castillo, J.J.; Laubach, J.P.; Hunter, Z.R.; Salman, Z.; Li, J.; Cheng, M.; Clow, F.; Graef, T.; Palomba, M.L.; Advani, R.H. Ibrutinib in previously treated Waldenström’s macroglobulinemia. N. Engl. J. Med., 2015, 372(15), 1430-1440. doi: 10.1056/NEJMoa1501548 PMID: 25853747
  19. Herman, S.E.M.; Gordon, A.L.; Hertlein, E.; Ramanunni, A.; Zhang, X.; Jaglowski, S.; Flynn, J.; Jones, J.; Blum, K.A.; Buggy, J.J.; Hamdy, A.; Johnson, A.J.; Byrd, J.C. Bruton tyrosine kinase represents a promising therapeutic target for treatment of chronic lymphocytic leukemia and is effectively targeted by PCI-32765. Blood, 2011, 117(23), 6287-6296. doi: 10.1182/blood-2011-01-328484 PMID: 21422473
  20. Smith, C.I.E. From identification of the BTK kinase to effective management of leukemia. Oncogene, 2017, 36(15), 2045-2053. doi: 10.1038/onc.2016.343 PMID: 27669440
  21. Lucas, F.; Woyach, J.A. Inhibiting bruton’s tyrosine kinase in CLL and other B-cell malignancies. Target. Oncol., 2019, 14(2), 125-138. doi: 10.1007/s11523-019-00635-7 PMID: 30927175
  22. Woyach, J.A.; Furman, R.R.; Liu, T.M.; Ozer, H.G.; Zapatka, M.; Ruppert, A.S.; Xue, L.; Li, D.H.H.; Steggerda, S.M.; Versele, M.; Dave, S.S.; Zhang, J.; Yilmaz, A.S.; Jaglowski, S.M.; Blum, K.A.; Lozanski, A.; Lozanski, G.; James, D.F.; Barrientos, J.C.; Lichter, P.; Stilgenbauer, S.; Buggy, J.J.; Chang, B.Y.; Johnson, A.J.; Byrd, J.C. Resistance mechanisms for the Bruton’s tyrosine kinase inhibitor ibrutinib. N. Engl. J. Med., 2014, 370(24), 2286-2294. doi: 10.1056/NEJMoa1400029 PMID: 24869598
  23. Bond, D.A.; Woyach, J.A. Targeting BTK in CLL: Beyond Ibrutinib. Curr. Hematol. Malig. Rep., 2019, 14(3), 197-205. doi: 10.1007/s11899-019-00512-0 PMID: 31028669
  24. Kapoor, I.; Li, Y.; Sharma, A.; Zhu, H.; Bodo, J.; Xu, W.; Hsi, E.D.; Hill, B.T.; Almasan, A. Resistance to BTK inhibition by ibrutinib can be overcome by preventing FOXO3a nuclear export and PI3K/AKT activation in B-cell lymphoid malignancies. Cell Death Dis., 2019, 10(12), 924. doi: 10.1038/s41419-019-2158-0 PMID: 31801949
  25. Boran, A.D.W.; Iyengar, R. Systems approaches to polypharmacology and drug discovery. Curr. Opin. Drug Discov. Devel., 2010, 13(3), 297-309.http://www.ncbi.nlm.nih.gov/pubmed/20443163 PMID: 20443163
  26. Raghavendra, N.M.; Pingili, D.; Kadasi, S.; Mettu, A.; Prasad, S.V.U.M. Dual or multi-targeting inhibitors: The next generation anticancer agents. Eur. J. Med. Chem., 2018, 143, 1277-1300. doi: 10.1016/j.ejmech.2017.10.021 PMID: 29126724
  27. Méndez-Lucio, O.; Naveja, J.J.; Vite-Caritino, H.; Prieto-Martínez, F.D.; Medina-Franco, J.L. One drug for multiple targets: A computational perspective. J. Mex. Chem. Soc., 2016, 60(3), 168-181.https://www.jmcs.org.mx/index.php/jmcs/article/view/100
  28. Zhang, X.; Zegar, T.; Weiser, T.; Hamdan, F.H.; Berger, B.T.; Lucas, R.; Balourdas, D.I.I.; Ladigan, S.; Cheung, P.F.; Liffers, S.T.; Trajkovic-Arsic, M.; Scheffler, B.; Joerger, A.C.; Hahn, S.A.; Johnsen, S.A.; Knapp, S.; Siveke, J.T. Characterization of a dual BET/HDAC inhibitor for treatment of pancreatic ductal adenocarcinoma. Int. J. Cancer, 2020, 147(10), 2847-2861. doi: 10.1002/ijc.33137 PMID: 32599645
  29. Mokhtari, R.B.; Homayouni, T.S.; Baluch, N.; Morgatskaya, E.; Kumar, S.; Das, B.; Yeger, H. Combination therapy in combating cancer. Oncotarget, 2017, 8(23), 38022-38043. doi: 10.18632/oncotarget.16723 PMID: 28410237
  30. Mei, Y.; Yang, B. Rational application of drug promiscuity in medicinal chemistry. Future Med. Chem., 2018, 10(15), 1835-1851. doi: 10.4155/fmc-2018-0018 PMID: 30019924
  31. Yang, G.; Wang, J.; Liu, X.; Munshi, M.; Chen, J.G.; Kofides, A.; Xu, L.; Tsakmaklis, N.; Demos, M.; Guerrera, M.L.; Chan, G.G.; Jimenez, C.; Hunter, Z.R.; Patterson, C.; Castillo, J.J.; Buhrlage, S.J.; Gray, N.; Treon, S.P. A novel hck and btk dual inhibitor kin-8194 shows superior activity over ibrutinib and overcomes btkc481s mediated ibrutinib resistance in vitro and in vivo in myd88 mutated b-cell lymphomas. Blood, 2019, 134(S1), 394. doi: 10.1182/blood-2019-130636
  32. Yang, G.; Wang, J.; Tan, L.; Munshi, M.; Liu, X.; Kofides, A.; Chen, J.G.; Tsakmaklis, N.; Demos, M.G.; Guerrera, M.L.; Xu, L.; Hunter, Z.R.; Che, J.; Patterson, C.J.; Meid, K.; Castillo, J.J.; Munshi, N.C.; Anderson, K.C.; Cameron, M.; Buhrlage, S.J.; Gray, N.S.; Treon, S.P. The HCK/BTK inhibitor KIN-8194 is active in MYD88-driven lymphomas and overcomes mutated BTKCys481 ibrutinib resistance. Blood, 2021, 138(20), 1966-1979. doi: 10.1182/blood.2021011405 PMID: 34132782
  33. Burley, S.K.; Berman, H.M.; Christie, C. RCSB protein data bank: Sustaining a living digital data resource that enables breakthroughs in scientific research and biomedical education. Protein Sci., 2018, 27(1), 316. doi: 10.1002/pro.3331 PMID: 29067736
  34. Bender, A.T.; Gardberg, A.; Pereira, A.; Johnson, T.; Wu, Y.; Grenningloh, R.; Head, J.; Morandi, F.; Haselmayer, P.; Liu-Bujalski, L. Ability of bruton’s tyrosine kinase inhibitors to sequester y551 and prevent phosphorylation determines potency for inhibition of fc receptor but not b-cell receptor signaling. Mol. Pharmacol., 2017, 91(3), 208-219. doi: 10.1124/mol.116.107037 PMID: 28062735
  35. Katsura, K.; Tomabechi, Y.; Matsuda, T.; Yonemochi, M.; Mikuni, J.; Ohsawa, N.; Terada, T.; Yokoyama, S.; Kukimoto-Niino, M.; Takemoto, C.; Shirouzu, M. Phosphorylated and non-phosphorylated HCK kinase domains produced by cell-free protein expression. Protein Expr. Purif., 2018, 150, 92-99. doi: 10.1016/j.pep.2018.05.005 PMID: 29793032
  36. Berman, H.M.; Battistuz, T.; Bhat, T.N. TheProtein Data Bank, 2002. Available from: https://pubmed.ncbi.nlm.nih.gov/12037327/
  37. Susi, K.; Emil, K.; Soleh, K.; Wahono, S. The molecular docking of 1,4-naphthoquinone derivatives as inhibitors of polo-like kinase 1 using molegro virtual docker J. Appl. Pharm. Sci., , 2014, 4(11), 047-053.http://japsonline.com/counter.php?aid=1369 doi: 10.7324/JAPS.2014.4119
  38. 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
  39. Eswar, N.; Webb, B.; Marti-Renom, M.A. Comparative protein structure modeling using modeller. Curr. Protoc. Bioinformatics, 2006, 5(1) doi: 10.1002/0471250953.bi0506s15
  40. Dunbrack, R.L. Jr Rotamer libraries in the 21st century. Curr. Opin. Struct. Biol., 2002, 12(4), 431-440. doi: 10.1016/S0959-440X(02)00344-5 PMID: 12163064
  41. Cherinka, B.; Andrews, B.H.; Sánchez-Gallego, J.; Brownstein, J.; Argudo-Fernández, M.; Blanton, M.; Bundy, K.; Jones, A.; Masters, K.; Law, D.R.; Rowlands, K.; Weijmans, A-M.; Westfall, K.; Yan, R. Marvin: A tool kit for streamlined access and visualization of the sdss-iv manga data set. Astron. J., 2019, 158(2), 74. doi: 10.3847/1538-3881/ab2634
  42. Hanwell, M.D.; Curtis, D.E.; Lonie, D.C.; Vandermeersch, T.; Zurek, E.; Hutchison, G.R. Avogadro: An advanced semantic chemical editor, visualization, and analysis platform. J. Cheminform., 2012, 4(1), 17. doi: 10.1186/1758-2946-4-17 PMID: 22889332
  43. Allouche, A.R. Gabedit-A graphical user interface for computational chemistry softwares. J. Comput. Chem., 2011, 32(1), 174-182. doi: 10.1002/jcc.21600 PMID: 20607691
  44. 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
  45. Case, D.A.; Belfon, K.; Ben-Shalom, I.Y.; Brozell, S.R.; Cerutti, D.S. Amber 2020 reference manual; University of California: San Francisco, 2020, pp. 1-918. Available from: https://ambermd.org/doc12/Amber20.pdf
  46. Salomon-Ferrer, R.; Case, D.A.; Walker, R.C. An overview of the Amber biomolecular simulation package. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2013, 3(2), 198-210. doi: 10.1002/wcms.1121
  47. Ponder, J.W.; Case, D.A. Force fields for protein simulations. Adv. Protein Chem., 2003, 66, 27-85. doi: 10.1016/S0065-3233(03)66002-X PMID: 14631816
  48. Wang, J.; Wolf, R.M.; Caldwell, J.W.; Kollman, P.A.; Case, D.A. Development and testing of a general amber force field. J. Comput. Chem., 2004, 25(9), 1157-1174. doi: 10.1002/jcc.20035 PMID: 15116359
  49. Grest, G.S.; Kremer, K. Molecular dynamics simulation for polymers in the presence of a heat bath. Phys. Rev. A Gen. Phys., 1986, 33(5), 3628-3631. doi: 10.1103/PhysRevA.33.3628 PMID: 9897103
  50. Berendsen, H.J.C.; Postma, J.P.M.; van Gunsteren, W.F.; DiNola, A.; Haak, J.R. Molecular dynamics with coupling to an external bath. J. Chem. Phys., 1984, 81(8), 3684-3690. doi: 10.1063/1.448118
  51. Ryckaert, J.P.; Ciccotti, G.; Berendsen, H.J.C. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys., 1977, 23(3), 327-341. doi: 10.1016/0021-9991(77)90098-5
  52. Roe, D.R.; Cheatham, T.E. III PTRAJ and CPPTRAJ: Software for processing and analysis of molecular dynamics trajectory data. J. Chem. Theory Comput., 2013, 9(7), 3084-3095. doi: 10.1021/ct400341p PMID: 26583988
  53. Seifert, E. OriginPro 9.1: Scientific data analysis and graphing software-software review. J. Chem. Inf. Model., 2014, 54(5), 1552. doi: 10.1021/ci500161d PMID: 24702057
  54. BIOVIA In: DS. Discovery Studio 2016 Client; Dassault Systèmes: San Diego, 2016.
  55. Massova, I.; Kollman, P.A. Combined molecular mechanical and continuum solvent approach (MM- PBSA/GBSA) to predict ligand binding. Perspect. Drug Discov. Des., 2000, 18(1), 113-135. doi: 10.1023/A:1008763014207
  56. Genheden, S.; Kuhn, O.; Mikulskis, P.; Hoffmann, D.; Ryde, U. The normal-mode entropy in the MM/GBSA method: Effect of system truncation, buffer region, and dielectric constant. J. Chem. Inf. Model., 2012, 52(8), 2079-2088. doi: 10.1021/ci3001919 PMID: 22817270
  57. Onufriev, A.; Bashford, D.; Case, D.A. Modification of the generalized born model suitable for macromolecules. J. Phys. Chem. B, 2000, 104(15), 3712-3720. doi: 10.1021/jp994072s
  58. Ylilauri, M.; Pentikäinen, O.T. MMGBSA as a tool to understand the binding affinities of filamin-peptide interactions. J. Chem. Inf. Model., 2013, 53(10), 2626-2633. doi: 10.1021/ci4002475 PMID: 23988151
  59. Kollman, P.A.; Massova, I.; Reyes, C.; Kuhn, B.; Huo, S.; Chong, L.; Lee, M.; Lee, T.; Duan, Y.; Wang, W.; Donini, O.; Cieplak, P.; Srinivasan, J.; Case, D.A.; Cheatham, T.E., III Calculating structures and free energies of complex molecules: Combining molecular mechanics and continuum models. Acc. Chem. Res., 2000, 33(12), 889-897. doi: 10.1021/ar000033j PMID: 11123888
  60. Hou, T.; Wang, J.; Li, Y.; Wang, W. Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J. Chem. Inf. Model., 2011, 51(1), 69-82. doi: 10.1021/ci100275a PMID: 21117705
  61. Homeyer, N.; Gohlke, H. Free energy calculations by the molecular mechanics poisson-boltzmann surface area method. Mol. Inform., 2012, 31(2), 114-122. doi: 10.1002/minf.201100135 PMID: 27476956
  62. Sitkoff, D.; Sharp, K.A.; Honig, B. Accurate calculation of hydration free energies using macroscopic solvent models. J. Phys. Chem., 1994, 98(7), 1978-1988. doi: 10.1021/j100058a043
  63. Wan, H.; Hu, J.; Tian, X.; Chang, S. Molecular dynamics simulations of wild type and mutants of human complement receptor 2 complexed with C3d. Phys. Chem. Chem. Phys., 2013, 15(4), 1241-1251. doi: 10.1039/C2CP41388D PMID: 23229122
  64. Chang, S.; Hu, J.; Lin, P.; Jiao, X.; Tian, X. Substrate recognition and transport behavior analyses of amino acid antiporter with coarse-grained models. Mol. Biosyst., 2010, 6(12), 2430-2438. doi: 10.1039/c005266c PMID: 20838682
  65. Fakhar, Z.; Govender, T.; Maguire, G.E.M.; Lamichhane, G.; Walker, R.C.; Kruger, H.G.; Honarparvar, B. Differential flap dynamics in L, D-transpeptidase2 from Mycobacterium tuberculosis revealed by molecular dynamics. Mol. Biosyst., 2017, 13(6), 1223-1234. doi: 10.1039/C7MB00110J PMID: 28480928
  66. David CC; Jacobs, DJ Principal component analysis: A method for determining the essential dynamics of proteins. Methods Mol. Biol., 2014, 1084, 193-226. doi: 10.1007/978-1-62703-658-0_11 PMID: 24061923
  67. Levy, R.M.; Srinivasan, A.R.; Olson, W.K.; McCammon, J.A. Quasi-harmonic method for studying very low frequency modes in proteins. Biopolymers, 1984, 23(6), 1099-1112. doi: 10.1002/bip.360230610 PMID: 6733249
  68. Chen, J.; Wang, J.; Zhu, W. Binding modes of three inhibitors 8CA, F8A and I4A to A-FABP studied based on molecular dynamics simulation. PLoS One, 2014, 9(6), e99862. doi: 10.1371/journal.pone.0099862 PMID: 24918907
  69. Ichiye, T.; Karplus, M. Collective motions in proteins: A covariance analysis of atomic fluctuations in molecular dynamics and normal mode simulations. Proteins, 1991, 11(3), 205-217. doi: 10.1002/prot.340110305 PMID: 1749773
  70. Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual molecular dynamics. J. Mol. Graph., , 1996, 14(1) 33-38, 27-28.. doi: 10.1016/0263-7855(96)00018-5 PMID: 8744570
  71. Webborn, P.J.H. The role of pharmacokinetic studies in drug discovery: where are we now, how did we get here and where are we going? Future Med. Chem., 2014, 6(11), 1233-1235. doi: 10.4155/fmc.14.76 PMID: 25162995
  72. Daina, A.; Zoete, V. A boiled-egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem, 2016, 11(11), 1117-1121. doi: 10.1002/cmdc.201600182 PMID: 27218427
  73. Ahmed, S.S.S.J.; Ramakrishnan, V. Systems biological approach of molecular descriptors connectivity: optimal descriptors for oral bioavailability prediction. PLoS One, 2012, 7(7), e40654. doi: 10.1371/journal.pone.0040654 PMID: 22815781
  74. Kruijtzer, C.M.F.; Beijnen, J.H.; Schellens, J.H.M. Improvement of oral drug treatment by temporary inhibition of drug transporters and/or cytochrome P450 in the gastrointestinal tract and liver: an overview. Oncologist, 2002, 7(6), 516-530. doi: 10.1634/theoncologist.7-6-516 PMID: 12490739
  75. Mukherjee, J.; Gupta, M.N. Increasing importance of protein flexibility in designing biocatalytic processes. Biotechnol. Rep., 2015, 6, 119-123. doi: 10.1016/j.btre.2015.04.001 PMID: 28626705
  76. Xie, Y.; An, J.; Yang, G.; Wu, G.; Zhang, Y.; Cui, L.; Feng, Y. Enhanced enzyme kinetic stability by increasing rigidity within the active site. J. Biol. Chem., 2014, 289(11), 7994-8006. doi: 10.1074/jbc.M113.536045 PMID: 24448805
  77. Celej, M.S.; Montich, G.G.; Fidelio, G.D. Protein stability induced by ligand binding correlates with changes in protein flexibility. Protein Sci., 2003, 12(7), 1496-1506. doi: 10.1110/ps.0240003 PMID: 12824495
  78. Liu, K.; Kokubo, H. Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations: A cross-docking study. J. Chem. Inf. Model., 2017, 57(10), 2514-2522. doi: 10.1021/acs.jcim.7b00412 PMID: 28902511
  79. Agoni, C.; Salifu, E.Y.; Munsamy, G.; Olotu, F.A.; Soliman, M. CF3‐Pyridinyl substitution on antimalarial therapeutics: Probing differential ligand binding and dynamical inhibitory effects of a novel triazolopyrimidine‐based inhibitor on Plasmodium falciparum dihydroorotate dehydrogenase. Chem. Biodivers., 2019, 16(12), e1900365. doi: 10.1002/cbdv.201900365 PMID: 31589372
  80. Luque, I.; Freire, E. Structural stability of binding sites: Consequences for binding affinity and allosteric effects. Proteins, 2000, 4, 63-71. doi: 10.1002/1097-0134(2000)41:4+3.0.CO;2-6
  81. Salifu, E.Y.; Issahaku, A.R.; Agoni, C.; Ibrahim, M.A.A.; Manimbulu, N.; Soliman, M.E.S. Prioritizing the catalytic gatekeepers through pan- inhibitory mechanism of entrectinib against alk, ros1 and trka tyrosine kinases. Cell Biochem. Biophys., 2022, 80(1), 11-21. doi: 10.1007/s12013-021-01052-2 PMID: 35040089
  82. Brüschweiler, R. Efficient RMSD measures for the comparison of two molecular ensembles. Proteins, 2003, 50(1), 26-34. doi: 10.1002/prot.10250 PMID: 12471596
  83. Pitera, J.W. Expected distributions of root-mean-square positional deviations in proteins. J. Phys. Chem. B, 2014, 118(24), 6526-6530. doi: 10.1021/jp412776d PMID: 24655018
  84. Kumar, C.V.; Swetha, R.G.; Anbarasu, A.; Ramaiah, S. Computational analysis reveals the association of threonine 118 methionine mutation in pmp22 resulting in CMT-1A. Adv. Bioinformatics., 2014, 2014, 502618. doi: 10.1155/2014/502618 PMID: 25400662
  85. Teilum, K.; Olsen, J.G.; Kragelund, B.B. Functional aspects of protein flexibility. Cell. Mol. Life Sci., 2009, 66(14), 2231-2247. doi: 10.1007/s00018-009-0014-6 PMID: 19308324
  86. Gromiha, M.; Ahmad, S. Role of solvent accessibility in structure based drug design. Curr. Computeraided Drug Des., 2005, 1(3), 223-235. doi: 10.2174/1573409054367664
  87. Lobanov, M.Y.; Bogatyreva, N.S.; Galzitskaya, O.V. Radius of gyration as an indicator of protein structure compactness. Mol. Biol., 2008, 42(4), 623-628. doi: 10.1134/S0026893308040195

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