Quantifying Electronic Effects in QM and QM/MM Biomolecular Modeling with the Fukui Function

引用本文: QI Helena W., KARELINA Maria, KULIK Heather J. Quantifying Electronic Effects in QM and QM/MM Biomolecular Modeling with the Fukui Function[J]. 物理化学学报, 2018, 34(1): 81-91. doi: 10.3866/PKU.WHXB201706303 shu

Citation: QI Helena W., KARELINA Maria, KULIK Heather J. Quantifying Electronic Effects in QM and QM/MM Biomolecular Modeling with the Fukui Function[J]. Acta Physico-Chimica Sinica, 2018, 34(1): 81-91. doi: 10.3866/PKU.WHXB201706303 shu

Quantifying Electronic Effects in QM and QM/MM Biomolecular Modeling with the Fukui Function

1.
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;
2.
Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
基金项目:
The project was supported by the MIT Research Support Committee NEC Corporation Seed Grant and Burroughs Wellcome Fund Career Award at the Scientific Interface,USA.H.J.K.holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund.H.W.Q.was supported by a Department of Energy Computational Science Graduate Fellowship (DOE-CSGF).

摘要: Multi-scale quantum-mechanical/molecular-mechanical (QM/MM) and large-scale QM simulation provide valuable insight into enzyme mechanism and structure-property relationships. Analysis of the electron density afforded through these methods can enhance our understanding of how the enzyme environment modulates reactivity at the enzyme active site. From this perspective, tools from conceptual density functional theory to interrogate electron densities can provide added insight into enzyme function. We recently introduced the highly parallelizable Fukui shift analysis (FSA) method, which identifies how frontier states of an active site are altered by the presence of an additional QM residue to identify when QM treatment of a residue is essential as a result of quantum-mechanically affecting the behavior of the active site. We now demonstrate and analyze distance and residue dependence of Fukui function shifts in pairs of residues representing different non-covalent interactions. We also show how the interpretation of the Fukui function as a measure of relative nucleophilicity provides insight into enzymes that carry out S_N2 methyl transfer. The FSA method represents a promising approach for the systematic, unbiased determination of quantum mechanical effects in enzymes and for other complex systems that necessitate multi-scale modeling.

关键词:

English

Quantifying Electronic Effects in QM and QM/MM Biomolecular Modeling with the Fukui Function

1.
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;
2.
Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Received Date: 07 June 2017
Revised Date: 22 June 2017
Fund Project:
The project was supported by the MIT Research Support Committee NEC Corporation Seed Grant and Burroughs Wellcome Fund Career Award at the Scientific Interface,USA.H.J.K.holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund.H.W.Q.was supported by a Department of Energy Computational Science Graduate Fellowship (DOE-CSGF).

Abstract: Multi-scale quantum-mechanical/molecular-mechanical (QM/MM) and large-scale QM simulation provide valuable insight into enzyme mechanism and structure-property relationships. Analysis of the electron density afforded through these methods can enhance our understanding of how the enzyme environment modulates reactivity at the enzyme active site. From this perspective, tools from conceptual density functional theory to interrogate electron densities can provide added insight into enzyme function. We recently introduced the highly parallelizable Fukui shift analysis (FSA) method, which identifies how frontier states of an active site are altered by the presence of an additional QM residue to identify when QM treatment of a residue is essential as a result of quantum-mechanically affecting the behavior of the active site. We now demonstrate and analyze distance and residue dependence of Fukui function shifts in pairs of residues representing different non-covalent interactions. We also show how the interpretation of the Fukui function as a measure of relative nucleophilicity provides insight into enzymes that carry out S_N2 methyl transfer. The FSA method represents a promising approach for the systematic, unbiased determination of quantum mechanical effects in enzymes and for other complex systems that necessitate multi-scale modeling.

Key words:

1. [1]
  (1) Field, M. J.; Bash, P. A.; Karplus, M. J. Comput. Chem. 1990, 11, 700. doi: 10.1002/jcc.540110605
2. [2]
  (2) Bakowies, D.; Thiel, W. J. Phys. Chem. 1996, 100, 10580. doi: 10.1021/jp9536514
3. [3]
  (3) Mordasini, T. Z.; Thiel, W. Chimia 1998, 52, 288.
4. [4]
  (4) Monard, G.; Merz, K. M. Acc. Chem. Res. 1999, 32, 904. doi: 10.1021/ar970218z
5. [5]
  (5) Gao, J. L.; Truhlar, D. G. Annu. Rev. Phys. Chem. 2002, 53, 467. doi: 10.1146/annurev.physchem.53.091301.150114
6. [6]
  (6) Rosta, E.; Klahn, M.; Warshel, A. J. Phys. Chem. B 2006, 110, 2934. doi: 10.1021/jp057109j
7. [7]
  (7) Lin, H.; Truhlar, D. Theor. Chem. Acc. 2007, 117, 185. doi: 10.1007/s00214-006-0143-z
8. [8]
  (8) Warshel, A.; Levitt, M. J. Mol. Biol. 1976, 103, 227. doi: 10.1016/0022-2836(76)90311-9
9. [9]
  (9) Senn, H. M.; Thiel, W. Angew. Chem. Int. Ed. 2009, 48, 1198. doi: 10.1002/anie.200802019
10. [10]
  (10) Thellamurege, N. M.; Hirao, H. J. Phys. Chem. B 2014, 118, 2084. doi: 10.1021/jp412538n
11. [11]
  (11) Ponder, J. W.; Wu, C.; Ren, P.; Pande, V. S.; Chodera, J. D.; Schnieders, M. J.; Haque, I.; Mobley, D. L.; Lambrecht, D. S.; DiStasio, R. A., Jr. J. Phys. Chem. B 2010, 114, 2549. doi: 10.1021/jp910674d
12. [12]
  (12) Halgren, T. A.; Damm, W. Curr. Opin. Struct. Biol. 2001, 11, 236. doi: 10.1016/S0959-440X(00)00196-2
13. [13]
  (13) Vidossich, P.; Florin, G.; Alfonso-Prieto, M.; Derat, E.; Shaik, S.; Rovira, C. J. Phys. Chem. B 2010, 114, 5161. doi: 10.1021/jp911170b
14. [14]
  (14) Carloni, P.; Rothlisberger, U.; Parrinello, M. Acc. Chem. Res. 2002, 35, 455. doi: 10.1021/ar010018u
15. [15]
  (15) Kulik, H. J.; Luehr, N.; Ufimtsev, I. S.; Martinez, T. J. J. Phys. Chem. B 2012, 116, 12501. doi: 10.1021/jp307741u
16. [16]
  (16) Liu, F.; Luehr, N.; Kulik, H. J.; Martínez, T. J. J.Chem. Theory Comput. 2015, 11, 3131. doi: 10.1021/acs.jctc.5b00370
17. [17]
  (17) Flaig, D.; Beer, M.; Ochsenfeld, C. J. Chem. Theory Comput. 2012, 8, 2260. doi: 10.1021/ct300036s
18. [18]
  (18) Hartman, J. D.; Neubauer, T. J.; Caulkins, B. G.; Mueller, L. J.; Beran, G. J. J. Biomol. NMR 2015, 62, 327. doi: 10.1007/s10858-015-9947-2
19. [19]
  (19) Fox, S. J.; Pittock, C.; Fox, T.; Tautermann, C. S.; Malcolm, N.; Skylaris, C. K. J. Chem. Phys. 2011, 135, 224107. doi: 10.1063/1.3665893
20. [20]
  (20) Liao, R. Z.; Thiel, W. J. Comput. Chem. 2013, 34, 2389. doi: 10.1002/jcc.23403
21. [21]
  (21) Sadeghian, K.; Flaig, D.; Blank, I. D.; Schneider, S.; Strasser, R.; Stathis, D.; Winnacker, M.; Carell, T.; Ochsenfeld, C. Angew. Chem. Int. Ed. 2014, 53, 10044. doi: 10.1002/anie.201403334
22. [22]
  (22) Kulik, H. J.; Zhang, J.; Klinman, J. P.; Martinez, T. J. J. Phys. Chem. B 2016, 120, 11381. DOI: 10.1021/acs.jpcb.6b07814
23. [23]
  (23) Solt, I.; Kulhanek, P.; Simon, I.; Winfield, S.; Payne, M. C.; Csanyi, G.; Fuxreiter, M. J. Phys. Chem. B 2009, 113, 5728. doi: 10.1021/jp807277r
24. [24]
  (24) Isborn, C. M.; Goetz, A. W.; Clark, M. A.; Walker, R. C.; Martinez, T. J. J. Chem. Theory Comput. 2012, 8, 5092. doi: 10.1021/ct3006826
25. [25]
  (25) Vanpoucke, D. E.; Oláh, J.; De Proft, F.; Van Speybroeck, V.; Roos, G. J. Chem. Inf. Model. 2015, 55, 564. doi: 10.1021/ci5006417
26. [26]
  (26) Harris, T. V.; Szilagyi, R. K. J. Comput. Chem. 2016, 37, 1681. doi: 10.1002/jcc.24384
27. [27]
  (27) Karelina, M.; Kulik, H. J. J. Chem. Theory Comput. 2017, 13, 563. doi: 10.1021/acs.jctc.6b01049
28. [28]
  (28) Morgenstern, A.; Jaszai, M.; Eberhart, M. E.; Alexandrova, A. N. Chem. Sci. 2017. doi: 10.1039/C7SC01301A
29. [29]
  (29) Geerlings, P.; De Proft, F.; Langenaeker, W. Chem. Rev. 2003, 103, 1793. doi: 10.1021/cr990029p
30. [30]
  (30) Parr, R. G.; Yang, W. J. Am. Chem. Soc. 1984, 106, 4049. doi: 10.1021/ja00326a036
31. [31]
  (31) Yang, W.; Mortier, W. J. J. Am. Chem. Soc. 1986, 108, 5708. doi: 10.1021/ja00279a008
32. [32]
  (32) Faver, J.; Merz, K. M., Jr. J. Chem. Theory Comput. 2010, 6, 548. doi: 10.1021/ct9005085
33. [33]
  (33) Fukushima, K.; Wada, M.; Sakurai, M. Proteins: Struct., Funct., Bioinf. 2008, 71, 1940. doi: 10.1002/prot.21865
34. [34]
  (34) Guerra, C. F.; Handgraaf, J. W.; Baerends, E. J.; Bickelhaupt, F. M. J. Comput. Chem. 2004, 25, 189. doi: 10.1002/jcc.10351
35. [35]
  (35) Ufimtsev, I. S.; Martínez, T. J. J. Chem. Theory Comput. 2009, 5, 2619. doi: 10.1021/ct9003004
36. [36]
  (36) Petachem. http://www.petachem.com. (accessed May 20, 2017).
37. [37]
  (37) Rutherford, K.; Le Trong, I.; Stenkamp, R. E.; Parson, W. W. J. Mol. Biol. 2008, 380, 120. doi: 10.1016/j.jmb.2008.04.040
38. [38]
  (38) Patra, N.; Ioannidis, E. I.; Kulik, H. J. PloS One 2016, 11, e0161868. doi: 10.1371/journal.pone.0161868
39. [39]
  (39) Zhang, J.; Kulik, H. J.; Martinez, T. J.; Klinman, J. P. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 7954. doi: 10.1073/pnas.1506792112
40. [40]
  (40) Griffith, S. C.; Sawaya, M. R.; Boutz, D. R.; Thapar, N.; Katz, J. E.; Clarke, S.; Yeates, T. O. J. Mol. Biol. 2001, 313, 1103. doi: 10.1006/jmbi.2001.5095
41. [41]
  (41) Labahn, J.; Granzin, J.; Schluckebier, G.; Robinson, D. P.; Jack, W. E.; Schildkraut, I.; Saenger, W. Proc. Natl. Acad. Sci.U. S. A. 1994, 91, 10957.
42. [42]
  (42) Anandakrishnan, R.; Aguilar, B.; Onufriev, A. V. Nucleic Acids Res. 2012, 40, W537. doi: 10.1093/nar/gks375
43. [43]
  (43) Gordon, J. C.; Myers, J. B.; Folta, T.; Shoja, V.; Heath, L. S.; Onufriev, A. Nucleic Acids Res. 2005, 33, W368. doi: 10.1093/nar/gki464
44. [44]
  (44) Myers, J.; Grothaus, G.; Narayanan, S.; Onufriev, A. Proteins: Struct., Funct., Bioinf. 2006, 63, 928. doi: 10.1002/prot.20922
45. [45]
  (45) Case, D.A.; Berryman, J. T.; Betz, R.M.; Cerutti, D.S., Cheatham, Ⅲ, D.S.; Darden, T.A.; Duke, R.E.; Giese, T.J., Gohlke, H.; Goetz, A.W.; et al., Amber 2015; University of California: San Francisco. 2015.
46. [46]
  (46) Maier, J. A.; Martinez, C.; Kasavajhala, K.; Wickstrom, L.; Hauser, K. E.; Simmerling, C. J. Chem. Theory Comput. 2015, 11, 3696. doi: 10.1021/acs.jctc.5b00255
47. [47]
  (47) Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C. Proteins: Struct., Funct., Bioinf. 2006, 65, 712. doi: 10.1002/prot.21123
48. [48]
  (48) Wang, J.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A. J. Comput. Chem. 2004, 25, 1157. doi: 10.1002/jcc.20035
49. [49]
  (49) Bayly, C. I.; Cieplak, P.; Cornell, W.; Kollman, P. A. J. Phys. Chem. 1993, 97, 10269. doi: 10.1021/j100142a004
50. [50]
  (50) Gordon, M. S.; Schmidt, M. W. Theory Appl. Comput. Chem.: First Forty Years 2005, 1167.
51. [51]
  (51) Harihara, P. C.; Pople, J. A. Theor Chim Acta 1973, 28, 213. doi: 10.1007/bf00533485
52. [52]
  (52) Wang, F.; Becker, J.-P.; Cieplak, P.; Dupradeau, F.-Y. R.E.D. Python: Object Oriented Programming for Amber Force Fields; Université De Picardie - Jules Verne: Sanford|Burnham Medical Research Institute, Nov. 2013. http://q4md-forcefieldtools.org/REDServer-Development/ (accessed 5/20/17).
53. [53]
  (53) Vanquelef, E.; Simon, S.; Marquant, G.; Garcia, E.; Klimerak, G.; Delepine, J. C.; Cieplak, P.; Dupradeau, F.-Y. Nucleic Acids Res. 2011, 39, W511. doi: 10.1093/nar/gkr288
54. [54]
  (54) Dupradeau, F.-Y.; Pigache, A.; Zaffran, T.; Savineau, C.; Lelong, R.; Grivel, N.; Lelong, D.; Rosanski, W.; Cieplak, P. Phys. Chem. Chem. Phys. 2010, 12, 7821. doi: 10.1039/C0CP00111B
55. [55]
  (55) Allnér, O.; Nilsson, L.; Villa, A. J. Chem. Theory Comput. 2012, 8, 1493. doi: 10.1021/ct3000734
56. [56]
  (56) Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. J. Chem. Phys. 1983, 79, 926. doi: 10.1063/1.445869
57. [57]
  (57) Ryckaert, J.-P.; Ciccotti, G.; Berendsen, H. J. C. J. Comput. Phys. 1977, 23, 327. doi: 10.1016/0021-9991(77)90098-5
58. [58]
  (58) Schrodinger, L. L. C. The PyMOL Molecular Graphics System, Version 1.7.4.3. 2010.
59. [59]
  (59) Rohrdanz, M. A.; Martins, K. M.; Herbert, J. M. J. Chem. Phys. 2009, 130, 054112. doi: 10.1063/1.3073302
60. [60]
  (60) Becke, A. D. J. Chem. Phys. 1993, 98, 5648. doi: 10.1063/1.464913
61. [61]
  (61) Stephens, P. J.; Devlin, F. J.; Chabalowski, C. F.; Frisch, M. J. J. Phys. Chem. 1994, 98, 11623. doi: 10.1021/j100096a001
62. [62]
  (62) Lee, C.; Yang, W.; Parr, R. G. Phys. Rev. B 1988, 37, 785. doi: 10.1103/PhysRevB.37.785
63. [63]
  (63) Lu, T.; Chen, F. J. Comput. Chem. 2012, 33, 580. doi: 10.1002/jcc.22885
64. [64]
  (64) Axelrod, J.; Tomchick, R. J. Biol.Chem. 1958, 233, 702.
65. [65]
  (65) Hegazi, M. F.; Borchardt, R. T.; Schowen, R. L. J. Am. Chem. Soc. 1979, 101, 4359. doi: 10.1021/ja00509a052
66. [66]
  (66) Woodard, R. W.; Tsai, M. D.; Floss, H. G.; Crooks, P. A.; Coward, J. K. J. Biol. Chem. 1980, 255, 9124.

扫一扫看文章

计量

PDF下载量: 5
文章访问数: 702
HTML全文浏览量: 51

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Quantifying Electronic Effects in QM and QM/MM Biomolecular Modeling with the Fukui Function

English

Quantifying Electronic Effects in QM and QM/MM Biomolecular Modeling with the Fukui Function

CCS Chemistry：嵌段共聚物自组装成 “三明治”二维有机材料，实现纳米级压力传感功能

CCS Chemistry：颜徐州、鲍哲南：你的皮肤可能是一片SPMs

CCS Chemistry：工作高效不疲劳，只须让催化剂动起来

CCS Chemistry：静电组装导向聚合- 聚电解质纳米凝胶量化制备新策略

CCS Chemistry：金黄色葡萄球菌醛基脱氢酶是如何识别特异性底物的？

计量

通讯作者: 陈斌, bchen63@163.com

中国化学会秘书处

Quantifying Electronic Effects in QM and QM/MM Biomolecular Modeling with the Fukui Function

English

Quantifying Electronic Effects in QM and QM/MM Biomolecular Modeling with the Fukui Function

CCS Chemistry：嵌段共聚物自组装成 “三明治”二维有机材料，实现纳米级压力传感功能

CCS Chemistry：颜徐州、鲍哲南： 你的皮肤可能是一片SPMs

CCS Chemistry：工作高效不疲劳，只须让催化剂动起来

CCS Chemistry：静电组装导向聚合- 聚电解质纳米凝胶量化制备新策略

CCS Chemistry：金黄色葡萄球菌醛基脱氢酶是如何识别特异性底物的？

计量

文章相关

通讯作者: 陈斌, bchen63@163.com

中国化学会秘书处

CCS Chemistry：颜徐州、鲍哲南：你的皮肤可能是一片SPMs