164
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Molecular mechanisms of inhibitor bindings to A-FABP deciphered by using molecular dynamics simulations and calculations of MM-GBSA

ORCID Icon, , , &
Pages 293-315 | Received 10 Dec 2020, Accepted 15 Feb 2021, Published online: 03 Mar 2021

References

  • A. Chmurzyńska, The multigene family of fatty acid-binding proteins (FABPs): Function, structure and polymorphism, J. Appl. Genet. 47 (2006), pp. 39–48. doi:10.1007/BF03194597.
  • J.F.C. Glatz and G.J. van der Vusse, Cellular fatty acid-binding proteins: Their function and physiological significance, Prog. Lipid Res. 35 (1996), pp. 243–282. doi:10.1016/s0163-7827(96)00006-9.
  • A. Vogel Hertzel and D.A. Bernlohr, The mammalian fatty acid-binding protein multigene family: Molecular and genetic insights into function, Trends Endocrin. Met. 11 (2000), pp. 175–180. doi:10.1016/S1043-2760(00)00257-5.
  • L. Banaszak, N. Winter, Z. Xu, D.A. Bernlohr, S. Cowan, and A.T. Jones, Lipid-binding proteins: A family of fatty acid and retinoid transport proteins, Adv. Protein Chem. 45 (1994), pp. 90–152. doi:10.1016/S0065-3233(08)60639-7.
  • G.S. Hotamisligil, Inflammation and metabolic disorders, Nature 444 (2006), pp. 860–867. doi:10.1038/nature05485.
  • M. Furuhashi and G.S. Hotamisligil, Fatty acid-binding proteins: Role in metabolic diseases and potential as drug targets, Nat. Rev. Drug Discov. 7 (2008), pp. 489–503. doi:10.1038/nrd2589.
  • A. Reese-Wagoner, J. Thompson, and L. Banaszak, Structural properties of the adipocyte lipid binding protein, BBA-Mol. Cell Biol. L 1441 (1999), pp. 106–116. doi:10.1016/s1388-1981(99)00154-7.
  • J. Ory, C.D. Kane, M.A. Simpson, L.J. Banaszak, and D.A. Bernlohr, Biochemical and crystallographic analyses of a portal mutant of the adipocyte lipid-binding protein, J. Biol. Chem. 272 (1997), pp. 9793–9801. doi:10.1074/jbc.272.15.9793.
  • J.J. Ory, A. Mazhary, H. Kuang, R.R. Davies, M.D. Distefano, and L.J. Banaszak, Structural characterization of two synthetic catalysts based on adipocyte lipid-binding protein, Protein Eng. Des. Sel. 11 (1998), pp. 253–261. doi:10.1093/protein/11.4.253.
  • J.M. LaLonde, D.A. Bernlohr, and L.J. Banaszak, X-ray crystallographic structures of adipocyte lipid-binding protein complexed with palmitate and hexadecanesulfonic acid. Properties of cavity binding sites, Biochemistry 33 (1994), pp. 4885–4895. doi:10.1021/bi00182a017.
  • J.M. LaLonde, M.A. Levenson, J.J. Roe, D.A. Bernlohr, and L.J. Banaszak, Adipocyte lipid-binding protein complexed with arachidonic acid. Titration calorimetry and X-ray crystallographic studies, J. Biol. Chem. 269 (1994), pp. 25339–25347. doi:10.1016/S0021-9258(18)47253-5.
  • Z.H. Xu, M.K. Buelt, L.J. Banaszak, and D.A. Bernlohr, Expression, purification, and crystallization of the adipocyte lipid binding protein, J. Biol. Chem. 266 (1991), pp. 14367–14370. doi:10.1016/S0021-9258(18)98693-X.
  • Z. Xu, D.A. Bernlohr, and L.J. Banaszak, Crystal structure of recombinant murine adipocyte lipid-binding protein, Biochemistry 31 (1992), pp. 3484–3492. doi:10.1021/bi00128a024.
  • Z. Xu, D.A. Bernlohr, and L.J. Banaszak, The adipocyte lipid-binding protein at 1.6-A resolution. Crystal structures of the apoprotein and with bound saturated and unsaturated fatty acids, J. Biol. Chem. 268 (1993), pp. 7874–7884. doi:10.1016/S0021-9258(18)53039-8.
  • F. Lehmann, S. Haile, E. Axen, C. Medina, J. Uppenberg, S. Svensson, T. Lundbäck, L. Rondahl, and T. Barf, Discovery of inhibitors of human adipocyte fatty acid-binding protein, a potential type 2 diabetes target, Bioorg. Med. Chem. Lett. 14 (2004), pp. 4445–4448. doi:10.1016/j.bmcl.2004.06.057.
  • G.S. Hotamisligil, R.S. Johnson, R.J. Distel, R. Ellis, V.E. Papaioannou, and B.M. Spiegelman, Uncoupling of obesity from insulin resistance through a targeted mutation in ap2, the adipocyte fatty acid binding protein, Science 274 (1996), pp. 1377–1379. doi:10.1126/science.274.5291.1377.
  • N.R. Coe, M.A. Simpson, and D.A. Bernlohr, Targeted disruption of the adipocyte lipid-binding protein (aP2 protein) gene impairs fat cell lipolysis and increases cellular fatty acid levels, J. Lipid Res. 40 (1999), pp. 967–972. doi:10.1016/S0022-2275(20)32133-7.
  • K.T. Uysal, L. Scheja, S.M. Wiesbrock, S. Bonner-Weir, and G.K.S. Hotamisligil, improved glucose and lipid metabolism in genetically obese mice lacking aP2, Endocrinology 141 (2000), pp. 3388–3396. doi:10.1210/endo.141.9.7637.
  • M. Furuhashi, G. Tuncman, C.Z. Görgün, L. Makowski, G. Atsumi, E. Vaillancourt, K. Kono, V.R. Babaev, S. Fazio, M.F. Linton, R. Sulsky, J.A. Robl, R.A. Parker, and G.S. Hotamisligil, Treatment of diabetes and atherosclerosis by inhibiting fatty-acid-binding protein aP2, Nature 447 (2007), pp. 959–965. doi:10.1038/nature05844.
  • L. Makowski, J.B. Boord, K. Maeda, V.R. Babaev, K.T. Uysal, M.A. Morgan, R.A. Parker, J. Suttles, S. Fazio, G.S. Hotamisligil, and M.F. Linton, Lack of macrophage fatty-acid–binding protein aP2 protects mice deficient in apolipoprotein E against atherosclerosis, Nat. Med. 7 (2001), pp. 699–705. doi:10.1038/89076.
  • L. Scheja, L. Makowski, K.T. Uysal, S.M. Wiesbrock, D.R. Shimshek, D.S. Meyers, M. Morgan, R.A. Parker, and G.S. Hotamisligil, Altered insulin secretion associated with reduced lipolytic efficiency in aP2-/- mice, Diabetes 48 (1999), pp. 1987–1994. doi:10.2337/diabetes.48.10.1987.
  • B. Vural, F. Atalar, C. Ciftci, A. Demirkan, B. Susleyici-Duman, D. Gunay, B. Akpinar, E. Sagbas, U. Ozbek, and A.S. Buyukdevrim, Presence of fatty-acid-binding protein 4 expression in human epicardial adipose tissue in metabolic syndrome, Cardiovasc. Pathol. 17 (2008), pp. 392–398. doi:10.1016/j.carpath.2008.02.006.
  • A. Cabré, I. Lázaro, J. Girona, J.M. Manzanares, F. Marimón, N. Plana, M. Heras, and L. Masana, Fatty acid binding protein 4 is increased in metabolic syndrome and with thiazolidinedione treatment in diabetic patients, Atherosclerosis 195 (2007), pp. e150–e158. doi:10.1016/j.atherosclerosis.2007.04.045.
  • M. Furuhashi, Fatty acid-binding protein 4 in cardiovascular and metabolic diseases, J. Atheroscler. Thromb. 26 (2019), pp. 216–232. doi:10.5551/jat.48710.
  • L. Shu, R.L. Hoo, X. Wu, Y. Pan, I.P. Lee, L.Y. Cheong, S.R. Bornstein, X. Rong, J. Guo, and A. Xu, A-FABP mediates adaptive thermogenesis by promoting intracellular activation of thyroid hormones in brown adipocytes, Nat. Commun. 8 (2017), pp. 14147. doi:10.1038/ncomms14147.
  • C. Mathis, I. Lascombe, F. Monnien, H. Bittard, F. Kleinclauss, I. Bedgedjian, S. Fauconnet, and S. Valmary-Degano, Down-regulation of A-FABP predicts non-muscle invasive bladder cancer progression: Investigation with a long term clinical follow-up, BMC Cancer 18 (2018), pp. 1239. doi:10.1186/s12885-018-5137-4.
  • G. Llaverias, V. Noé, S. Peñuelas, M. Vázquez-Carrera, R.M. Sánchez, J.C. Laguna, C.J. Ciudad, and M. Alegret, Atorvastatin reduces CD68, FABP4, and HBP expression in oxLDL-treated human macrophages, Biochem. Bioph. Res. Co. 318 (2004), pp. 265–274. doi:10.1016/j.bbrc.2004.04.021.
  • R. Sulsky, D.R. Magnin, Y. Huang, L. Simpkins, P. Taunk, M. Patel, Y. Zhu, T.R. Stouch, D. Bassolino-Klimas, R. Parker, T. Harrity, R. Stoffel, D.S. Taylor, T.B. Lavoie, K. Kish, B.L. Jacobson, S. Sheriff, L.P. Adam, W.R. Ewing, and J.A. Robl, Potent and selective biphenyl azole inhibitors of adipocyte fatty acid binding protein (aFABP), Bioorg. Med. Chem. Lett. 17 (2007), pp. 3511–3515. doi:10.1016/j.bmcl.2006.12.044.
  • J. Song, P. Ren, L. Zhang, X.L. Wang, L. Chen, and Y.H. Shen, Metformin reduces lipid accumulation in macrophages by inhibiting FOXO1-mediated transcription of fatty acid-binding protein 4, Biochem. Bioph. Res. Co. 393 (2010), pp. 89–94. doi:10.1016/j.bbrc.2010.01.086.
  • U. Tagami, K. Takahashi, S. Igarashi, C. Ejima, T. Yoshida, S. Takeshita, W. Miyanaga, M. Sugiki, M. Tokumasu, T. Hatanaka, T. Kashiwagi, K. Ishikawa, H. Miyano, and T. Mizukoshi, Interaction analysis of FABP4 inhibitors by x-ray crystallography and fragment molecular orbital analysis, ACS Med. Chem. Lett. 7 (2016), pp. 435–439. doi:10.1021/acsmedchemlett.6b00040.
  • J.M. Gonzalez and S.Z. Fisher, Structural analysis of ibuprofen binding to human adipocyte fatty-acid binding protein (FABP4), Acta Crystallogr. F 71 (2015), pp. 163–170. doi:10.1107/S2053230X14027897.
  • D.-D. Gao, H.-X. Dou, H.-X. Su, -M.-M. Zhang, T. Wang, Q.-F. Liu, H.-Y. Cai, H.-P. Ding, Z. Yang, W.-L. Zhu, Y.-C. Xu, H.-Y. Wang, and Y.-X. Li, From hit to lead: Structure-based discovery of naphthalene-1-sulfonamide derivatives as potent and selective inhibitors of fatty acid binding protein 4, Eur. J. Med. Chem. 154 (2018), pp. 44–59. doi:10.1016/j.ejmech.2018.05.007.
  • A.V. Hertzel, K. Hellberg, J.M. Reynolds, A.C. Kruse, B.E. Juhlmann, A.J. Smith, M.A. Sanders, D.H. Ohlendorf, J. Suttles, and D.A. Bernlohr, Identification and characterization of a small molecule inhibitor of fatty acid binding proteins, J. Med. Chem. 52 (2009), pp. 6024–6031. doi:10.1021/jm900720m.
  • T. Barf, F. Lehmann, K. Hammer, S. Haile, E. Axen, C. Medina, J. Uppenberg, S. Svensson, L. Rondahl, and T. Lundbäck, N-Benzyl-indolo carboxylic acids: Design and synthesis of potent and selective adipocyte fatty-acid binding protein (A-FABP) inhibitors, Bioorg. Med. Chem. Lett. 19 (2009), pp. 1745–1748. doi:10.1016/j.bmcl.2009.01.084.
  • E.L. Wu, Y. Mei, K. Han, and J.Z.H. Zhang, Quantum and molecular dynamics study for binding of macrocyclic inhibitors to human α-thrombin, Biophys. J. 92 (2007), pp. 4244–4253. doi:10.1529/biophysj.106.099150.
  • J. Chen, J. Wang, and W. Zhu, Binding modes of three inhibitors 8CA, F8A and I4A to A-FABP studied based on molecular dynamics simulation, Plos One 9 (2014), pp. e99862. doi:10.1371/journal.pone.0099862.
  • F. Yan, X. Liu, S. Zhang, J. Su, Q. Zhang, and J. Chen, Electrostatic interaction-mediated conformational changes of adipocyte fatty acid binding protein probed by molecular dynamics simulation, J. Biomol. Struct. Dyn. 37 (2019), pp. 3583–3595. doi:10.1080/07391102.2018.1520648.
  • M. De Vivo, M. Masetti, G. Bottegoni, and A. Cavalli, Role of molecular dynamics and related methods in drug discovery, J. Med. Chem. 59 (2016), pp. 4035–4061. doi:10.1021/acs.jmedchem.5b01684.
  • T.E. Cheatham, J. Srinivasan, D.A. Case, and P.A. Kollman, Molecular dynamics and continuum solvent studies of the stability of PolyG-PolyC and PolyA-PolyT DNA duplexes in solution, J. Biomol. Struct. Dyn. 16 (1998), pp. 265–280. doi:10.1080/07391102.1998.10508245.
  • D. Shi, Q. Bai, S. Zhou, X. Liu, H. Liu, and X. Yao, Molecular dynamics simulation, binding free energy calculation and unbinding pathway analysis on selectivity difference between FKBP51 and FKBP52: Insight into the molecular mechanism of isoform selectivity, Proteins 86 (2018), pp. 43–56. doi:10.1002/prot.25401.
  • J. Chen, X. Wang, L. Pang, J.Z.H. Zhang, and T. Zhu, Effect of mutations on binding of ligands to guanine riboswitch probed by free energy perturbation and molecular dynamics simulations, Nucleic Acids Res. 47 (2019), pp. 6618–6631. doi:10.1093/nar/gkz499.
  • J. Zhao, B. Yin, H. Sun, L. Pang, and J. Chen, Identifying hot spots of inhibitor-CDK2 bindings by computational alanine scanning, Chem. Phys. Lett. 747 (2020), pp. 137329. doi:10.1016/j.cplett.2020.137329.
  • J. Devillers, Computational Design of Chemicals for the Control of Mosquitoes and Their Diseases, CRC Press, Boca Raton, 2018.
  • Y. Gao, T. Zhu, and J. Chen, Exploring drug-resistant mechanisms of I84V mutation in HIV-1 protease toward different inhibitors by thermodynamics integration and solvated interaction energy method, Chem. Phys. Lett. 706 (2018), pp. 400–408. doi:10.1016/j.cplett.2018.06.040.
  • H. Gohlke and D.A. Case, Converging free energy estimates: MM-PB(GB)SA studies on the protein–protein complex Ras–Raf, J. Comput. Chem. 25 (2004), pp. 238–250. doi:10.1002/jcc.10379.
  • I. Massova and P.A. Kollman, Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding, Perspect. Drug Discov. 18 (2000), pp. 113–135. doi:10.1023/A:1008763014207.
  • H. Sun, Y. Li, M. Shen, S. Tian, L. Xu, P. Pan, Y. Guan, and T. Hou, Assessing the performance of MM/PBSA and MM/GBSA methods. 5. Improved docking performance using high solute dielectric constant MM/GBSA and MM/PBSA rescoring, Phys. Chem. Chem. Phys. 16 (2014), pp. 22035–22045. doi:10.1039/C4CP03179B.
  • H.K. Srivastava and G.N. Sastry, Molecular dynamics investigation on a series of HIV protease inhibitors: Assessing the performance of MM-PBSA and MM-GBSA approaches, J. Chem. Inf. Model. 52 (2012), pp. 3088–3098. doi:10.1021/ci300385h.
  • P. Lagarias, K. Barkan, E. Tzortzini, M. Stampelou, E. Vrontaki, G. Ladds, and A. Kolocouris, Insights to the binding of a selective adenosine A3 receptor antagonist using molecular dynamic simulations, MM-PBSA and MM-GBSA free energy calculations, and mutagenesis, J. Chem. Inf. Model. 59 (2019), pp. 5183–5197. doi:10.1021/acs.jcim.9b00751.
  • J. Chen, X. Wang, T. Zhu, Q. Zhang, and J.Z.H. Zhang, A comparative insight into amprenavir resistance of mutations V32I, G48V, I50V, I54V, and I84V in HIV-1 protease based on thermodynamic integration and MM-PBSA methods, J. Chem. Inf. Model. 55 (2015), pp. 1903–1913. doi:10.1021/acs.jcim.5b00173.
  • S. Tian, J. Zeng, X. Liu, J. Chen, J.Z.H. Zhang, and T. Zhu, Understanding the selectivity of inhibitors toward PI4KIIIα and PI4KIIIβ based molecular modeling, Phys. Chem. Chem. Phys. 21 (2019), pp. 22103–22112. doi:10.1039/C9CP03598B.
  • J. Chen, J. Wang, and W. Zhu, Mutation L1196M-induced conformational changes and the drug resistant mechanism of anaplastic lymphoma kinase studied by free energy perturbation and umbrella sampling, Phys. Chem. Chem. Phys. 19 (2017), pp. 30239–30248. doi:10.1039/C7CP05418A.
  • R.D. Keane and R.J. Adrian, Theory of cross-correlation analysis of PIV images, Appl. Sci. Res. 49 (1992), pp. 191–215. doi:10.1007/BF00384623.
  • B. Podobnik and H.E. Stanley, Detrended cross-correlation analysis: A new method for analyzing two nonstationary time series, Phys. Rev. Lett. 100 (2008), pp. 084102. doi:10.1103/PhysRevLett.100.084102.
  • J. Chen, W. Wang, H. Sun, L. Pang, and B. Yin, Mutation-mediated influences on binding of anaplastic lymphoma kinase to crizotinib decoded by multiple replica Gaussian accelerated molecular dynamics, J. Comput. Aid. Mol. Des. 34 (2020), pp. 1289–1305. doi:10.1007/s10822-020-00355-5.
  • J. Zhao, H. Sun, W. Wang, L. Zhang, and J. Chen, Theoretical insights into mutation-mediated conformational changes of the GNP-bound H-RAS, Chem. Phys. Lett. 759 (2020), pp. 138042. doi:10.1016/j.cplett.2020.138042.
  • S. Wold, K. Esbensen, and P. Geladi, Principal component analysis, Chemom. Intell. Lab. 2 (1987), pp. 37–52. doi:10.1016/0169-7439(87)80084-9.
  • H. Abdi and L.J. Williams, Principal component analysis, WIREs Comput. Stat. 2 (2010), pp. 433–459. doi:10.1002/wics.101.
  • M.E. Tipping and C.M. Bishop, Probabilistic principal component analysis, J. R. Stat. Soc. B 61 (1999), pp. 611–622. doi:10.1111/1467-9868.00196.
  • J. Chen, W. Wang, L. Pang, and W. Zhu, Unveiling conformational dynamics changes of H-Ras induced by mutations based on accelerated molecular dynamics, Phys. Chem. Chem. Phys. 22 (2020), pp. 21238–21250. doi:10.1039/D0CP03766D.
  • F. Yan, X. Liu, S. Zhang, J. Su, Q. Zhang, and J. Chen, Molecular dynamics exploration of selectivity of dual inhibitors 5M7, 65X, and 65Z toward fatty acid binding proteins 4 and 5, Int. J. Mol. Sci. 19 (2018), pp. 2496. doi:10.3390/ijms19092496.
  • J. Chen, X. Liu, S. Zhang, J. Chen, H. Sun, L. Zhang, and Q. Zhang, Molecular mechanism with regard to the binding selectivity of inhibitors toward FABP5 and FABP7 explored by multiple short molecular dynamics simulations and free energy analyses, Phys. Chem. Chem. Phys. 22 (2020), pp. 2262–2275. doi:10.1039/C9CP05704H.
  • G. Hu, A. Ma, and J. Wang, Ligand selectivity mechanism and conformational changes in guanine riboswitch by molecular dynamics simulations and free energy calculations, J. Chem. Inf. Model. 57 (2017), pp. 918–928. doi:10.1021/acs.jcim.7b00139.
  • J. Chen, J. Wang, B. Yin, L. Pang, W. Wang, and W. Zhu, Molecular mechanism of binding selectivity of inhibitors toward BACE1 and BACE2 revealed by multiple short molecular dynamics simulations and free-energy predictions, ACS Chem. Neurosci. 10 (2019), pp. 4303–4318. doi:10.1021/acschemneuro.9b00348.
  • S.L. Wu, L.F. Wang, H.B. Sun, W. Wang, and Y.X. Yu, Probing molecular mechanism of inhibitor bindings to bromodomain-containing protein 4 based on molecular dynamics simulations and principal component analysis, SAR QSAR Environ. Res. 31 (2020), pp. 547–570. doi:10.1080/1062936X.2020.1777584.
  • L.F. Wang, Y. Wang, Z.Y. Yang, J. Zhao, H.B. Sun, and S.L. Wu, Revealing binding selectivity of inhibitors toward bromodomain-containing proteins 2 and 4 using multiple short molecular dynamics simulations and free energy analyses, SAR QSAR Environ. Res. 31 (2020), pp. 373–398. doi:10.1080/1062936X.2020.1748107.
  • D.A. Case, T.E. Cheatham III, T. Darden, H. Gohlke, R. Luo, K.M. Merz Jr, A. Onufriev, C. Simmerling, B. Wang, and R.J. Woods, The Amber biomolecular simulation programs, J. Comput. Chem. 26 (2005), pp. 1668–1688. doi:10.1002/jcc.20290.
  • T.-S. Lee, D.S. Cerutti, D. Mermelstein, C. Lin, S. LeGrand, T.J. Giese, A. Roitberg, D.A. Case, R.C. Walker, and D.M. York, GPU-accelerated molecular dynamics and free energy methods in Amber18: Performance enhancements and new features, J. Chem. Inf. Model. 58 (2018), pp. 2043–2050. doi:10.1021/acs.jcim.8b00462.
  • A. Jakalian, B.L. Bush, D.B. Jack, and C.I. Bayly, Fast, efficient generation of high-quality atomic charges. AM1-BCC model: I. Method, J. Comput. Chem. 21 (2000), pp. 132–146. doi:10.1002/(SICI)1096-987X(20000130)21:2<132::AID-JCC5>3.0.CO;2-P.
  • A. Jakalian, D.B. Jack, and C.I. Bayly, Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation, J. Comput. Chem. 23 (2002), pp. 1623–1641. doi:10.1002/jcc.10128.
  • X. He, V.H. Man, W. Yang, T.-S. Lee, and J. Wang, A fast and high-quality charge model for the next generation general AMBER force field, J. Chem. Phys. 153 (2020), pp. 114502. doi:10.1063/5.0019056.
  • J.A. Maier, C. Martinez, K. Kasavajhala, L. Wickstrom, K.E. Hauser, and C. Simmerling, ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB, J. Chem. Theory Comput. 11 (2015), pp. 3696–3713. doi:10.1021/acs.jctc.5b00255.
  • J. Wang, R.M. Wolf, J.W. Caldwell, P.A. Kollman, and D.A. Case, Development and testing of a general amber force field, J. Comput. Chem. 25 (2004), pp. 1157–1174. doi:10.1002/jcc.20035.
  • K.G. Sprenger, V.W. Jaeger, and J. Pfaendtner, The general AMBER force field (GAFF) can accurately predict thermodynamic and transport properties of many ionic liquids, J. Phys. Chem. B 119 (2015), pp. 5882–5895. doi:10.1021/acs.jpcb.5b00689.
  • J.P.M. Jämbeck and A.P. Lyubartsev, Update to the general amber force field for small solutes with an emphasis on free energies of hydration, J. Phys. Chem. B 118 (2014), pp. 3793–3804. doi:10.1021/jp4111234.
  • W.L. Jorgensen, J. Chandrasekhar, J.D. Madura, R.W. Impey, and M.L. Klein, Comparison of simple potential functions for simulating liquid water, J. Chem. Phys. 79 (1983), pp. 926–935. doi:10.1063/1.445869.
  • D.J. Price and C.L. Brooks III, A modified TIP3P water potential for simulation with Ewald summation, J. Chem. Phys. 121 (2004), pp. 10096–10103. doi:10.1063/1.1808117.
  • P. Mark and L. Nilsson, Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K, J. Phys. Chem. A 105 (2001), pp. 9954–9960. doi:10.1021/jp003020w.
  • S. Miyamoto and P.A. Kollman, Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models, J. Comput. Chem. 13 (1992), pp. 952–962. doi:10.1002/jcc.540130805.
  • U. Essmann, L. Perera, M.L. Berkowitz, T. Darden, H. Lee, and L.G. Pedersen, A smooth particle mesh Ewald method, J. Chem. Phys. 103 (1995), pp. 8577–8593. doi:10.1063/1.470117.
  • T. Darden, D. York, and L. Pedersen, Particle mesh Ewald: An Nlog(N) method for Ewald sums in large systems, J. Chem. Phys. 98 (1993), pp. 10089–10092. doi:10.1063/1.464397.
  • R. Salomon-Ferrer, A.W. Götz, D. Poole, S. Le Grand, and R.C. Walker, Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald, J. Chem. Theory Comput. 9 (2013), pp. 3878–3888. doi:10.1021/ct400314y.
  • D.R. Roe and T.E. Cheatham, PTRAJ and CPPTRAJ: Software for processing and analysis of molecular dynamics trajectory data, J. Chem. Theory Comput. 9 (2013), pp. 3084–3095. doi:10.1021/ct400341p.
  • H. Sun, Y. Li, S. Tian, L. Xu, and T. Hou, Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set, Phys. Chem. Chem. Phys. 16 (2014), pp. 16719–16729. doi:10.1039/C4CP01388C.
  • E. Wang, G. Weng, H. Sun, H. Du, F. Zhu, F. Chen, Z. Wang, and T. Hou, Assessing the performance of the MM/PBSA and MM/GBSA methods. 10. Impacts of enhanced sampling and variable dielectric model on protein–protein interactions, Phys. Chem. Chem. Phys. 21 (2019), pp. 18958–18969. doi:10.1039/C9CP04096J.
  • L.L. Duan, T. Zhu, Y.C. Li, Q.G. Zhang, and J.Z.H. Zhang, Effect of polarization on HIV-1protease and fluoro-substituted inhibitors binding energies by large scale molecular dynamics simulations, Sci. Rep. 7 (2017), pp. 42223. doi:10.1038/srep42223.
  • J. Chen, B. Yin, W. Wang, and H. Sun, effects of disulfide bonds on binding of inhibitors to β-amyloid cleaving enzyme 1 decoded by multiple replica accelerated molecular dynamics smulations, ACS Chem. Neurosci. 11 (2020), pp. 1811–1826. doi:10.1021/acschemneuro.0c00234.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.