Current Computer-Aided Drug Design

Author(s): Amar Y. Al-Ansi and Zijing Lin*

DOI: 10.2174/1573409918666220827151546

MDO: A Computational Protocol for Prediction of Flexible Enzyme-ligand Binding Mode

Page: [448 - 458] Pages: 11

  • * (Excluding Mailing and Handling)

Abstract

Aim: The aim of the study was to develop a method for use in computer-aided drug design.

Background: Predicting the structure of enzyme-ligand binding mode is essential for understanding the properties, functions, and mechanisms of the bio-complex, but is rather difficult due to the enormous sampling space involved.

Objective: The objective was to conduct accurate prediction of enzyme-ligand binding mode conformation.

Methods: A new computational protocol, MDO, is proposed for finding the structure of the ligand binding pose. MDO consists of sampling enzyme sidechain conformations via molecular dynamics simulation of the enzyme-ligand system and clustering of the enzyme configurations, sampling ligand binding poses via molecular docking and clustering of the ligand conformations, and the optimal ligand binding pose prediction via geometry optimization and ranking by the ONIOM method. MDO is tested on 15 enzyme-ligand complexes with known accurate structures.

Results: The success rate of MDO predictions, with RMSD < 2 Å, is 67%, substantially higher than the 40% success rate of conventional methods. The MDO success rate can be increased to 83% if the ONIOM calculations are applied only for the starting poses with ligands inside the binding cavities.

Conclusion: The MDO protocol provides high-quality enzyme-ligand binding mode prediction with reasonable computational cost. The MDO protocol is recommended for use in the structurebased drug design.

Keywords: Molecular dynamics, molecular docking, clustering analysis, binding pose prediction, structure-based drug design

Graphical Abstract

[1]
Morgan, J.W.R.; Glotzer, S.C. The alchemical energy landscape for a pentameric cluster. J. Chem. Phys., 2020, 152(1), 014106.
[http://dx.doi.org/10.1063/1.5130030] [PMID: 31914762]
[2]
Ryde, U.; Söderhjelm, P. Ligand-binding affinity estimates supported by quantum-mechanical methods. Chem. Rev., 2016, 116(9), 5520-5566.
[http://dx.doi.org/10.1021/acs.chemrev.5b00630] [PMID: 27077817]
[3]
Jensen, F. Introduction to Computational Chemistry, 3rd ed; Wiley, 2017.
[4]
Xue, Q.; Liu, X.; Russell, P.; Li, J.; Pan, W.; Fu, J.; Zhang, A. Evaluation of the binding performance of flavonoids to estrogen receptor alpha by Autodock, Autodock Vina and Surflex-Dock. Ecotoxicol. Environ. Saf., 2022, 233, 113323.
[http://dx.doi.org/10.1016/j.ecoenv.2022.113323] [PMID: 35183811]
[5]
Pagadala, N.S.; Syed, K.; Tuszynski, J. Software for molecular docking: A review. Biophys. Rev., 2017, 9(2), 91-102.
[http://dx.doi.org/10.1007/s12551-016-0247-1] [PMID: 28510083]
[6]
Guedes, I.A.; Pereira, F.S.S.; Dardenne, L.E. Empirical scoring functions for structure-based virtual screening: Applications, critical aspects, and challenges. Front. Pharmacol., 2018, 9, 1089.
[http://dx.doi.org/10.3389/fphar.2018.01089] [PMID: 30319422]
[7]
Kurkcuoglu, Z.; Koukos, P.I.; Citro, N.; Trellet, M.E.; Rodrigues, J.P.G.L.M.; Moreira, I.S.; Roel-Touris, J.; Melquiond, A.S.J.; Geng, C.; Schaarschmidt, J.; Xue, L.C.; Vangone, A.; Bonvin, A.M.J.J. Performance of HADDOCK and a simple contact-based protein–ligand binding affinity predictor in the D3R Grand Challenge 2. J. Comput. Aided Mol. Des., 2018, 32(1), 175-185.
[http://dx.doi.org/10.1007/s10822-017-0049-y] [PMID: 28831657]
[8]
Barnard, D.; Diaz, B.; Hettich, L.; Chuang, E.; Zhang, X.F.; Avruch, J.; Marshall, M. Identification of the sites of interaction between c-Raf-1 and Ras-GTP. Oncogene, 1995, 10(7), 1283-1290.
[PMID: 7731678]
[9]
Kamenik, A.S.; Singh, I.; Lak, P.; Balius, T.E.; Liedl, K.R.; Shoichet, B.K. Energy penalties enhance flexible receptor docking in a model cavity. Proc. Natl. Acad. Sci. USA, 2021, 118(36), e2106195118.
[http://dx.doi.org/10.1073/pnas.2106195118] [PMID: 34475217]
[10]
Lam, P.C.H.; Abagyan, R.; Totrov, M. Ligand-biased ensemble receptor docking (LigBEnD): A hybrid ligand/receptor structure-based approach. J. Comput. Aided Mol. Des., 2018, 32(1), 187-198.
[http://dx.doi.org/10.1007/s10822-017-0058-x] [PMID: 28887659]
[11]
Allegra, M.; Tutone, M.; Tesoriere, L.; Attanzio, A.; Culletta, G.; Almerico, A.M. Evaluation of the IKKβ binding of indicaxanthin by induced-fit docking, binding pose metadynamics, and molecular dynamics. Front. Pharmacol., 2021, 12, 701568.
[http://dx.doi.org/10.3389/fphar.2021.701568] [PMID: 34566634]
[12]
Huang, S.Y.; Zou, X. Advances and challenges in protein-ligand docking. Int. J. Mol. Sci., 2010, 11(8), 3016-3034.
[http://dx.doi.org/10.3390/ijms11083016] [PMID: 21152288]
[13]
Mizutani, M.Y.; Takamatsu, Y.; Ichinose, T.; Nakamura, K.; Itai, A. Effective handling of induced-fit motion in flexible docking. Proteins, 2006, 63(4), 878-891.
[http://dx.doi.org/10.1002/prot.20931] [PMID: 16532451]
[14]
Kurcinski, M.; Kmiecik, S.; Zalewski, M.; Kolinski, A. Protein–protein docking with large-scale backbone flexibility using coarse-grained monte-carlo simulations. Int. J. Mol. Sci., 2021, 22(14), 7341.
[http://dx.doi.org/10.3390/ijms22147341] [PMID: 34298961]
[15]
Ravindranath, P.A.; Forli, S.; Goodsell, D.S.; Olson, A.J.; Sanner, M.F. AutoDockFR: Advances in protein-ligand docking with explicitly specified binding site flexibility. PLOS Comput. Biol., 2015, 11(12), e1004586.
[http://dx.doi.org/10.1371/journal.pcbi.1004586] [PMID: 26629955]
[16]
Zhao, Y.; Sanner, M.F. FLIPDock: Docking flexible ligands into flexible receptors. Proteins, 2007, 68(3), 726-737.
[http://dx.doi.org/10.1002/prot.21423] [PMID: 17523154]
[17]
AutoDock What's new? Available from: http://autodock.scripps.edu/ (Accessed on: October 26, 2020).
[18]
Huang, S.Y. Comprehensive assessment of flexible-ligand docking algorithms: Current effectiveness and challenges. Brief. Bioinform., 2018, 19(5), 982-994.
[http://dx.doi.org/10.1093/bib/bbx030] [PMID: 28334282]
[19]
Singh, S.; Srivastava, H.K.; Kishor, G.; Singh, H.; Agrawal, P.; Raghava, G.P.S. Evaluation of protein-ligand docking methods on peptide-ligand complexes for docking small ligands to peptides. bioRxiv, 2017.
[http://dx.doi.org/10.1101/212514]
[20]
Cavasotto, C.N.; Aucar, M.G. High-throughput docking using quantum mechanical scoring. Front Chem., 2020, 8, 246.
[http://dx.doi.org/10.3389/fchem.2020.00246] [PMID: 32373579]
[21]
Nakliang, P.; Lazim, R.; Chang, H.; Choi, S. Multiscale molecular modeling in G protein-Coupled receptor (GPCR)-ligand studies. Biomolecules, 2020, 10(4), 631.
[http://dx.doi.org/10.3390/biom10040631] [PMID: 32325877]
[22]
Chung, L.W.; Sameera, W.M.C.; Ramozzi, R.; Page, A.J.; Hatanaka, M.; Petrova, G.P.; Harris, T.V.; Li, X.; Ke, Z.; Liu, F.; Li, H.B.; Ding, L.; Morokuma, K. The ONIOM method and its applications. Chem. Rev., 2015, 115(12), 5678-5796.
[http://dx.doi.org/10.1021/cr5004419] [PMID: 25853797]
[23]
Svensson, M.; Humbel, S.; Froese, R.D.J.; Matsubara, T.; Sieber, S.; Morokuma, K. ONIOM: A multilayered integrated MO+MM method for geometry optimizations and single point energy predictions. A test for Diels-Alder reactions and Pt(P(t-Bu)(3))(2)+H-2 oxidative addition. J. Phys. Chem., 1996, 100(50), 19357-19363.
[http://dx.doi.org/10.1021/jp962071j]
[24]
Morokuma, K. New challenges in quantum chemistry: Quests for accurate calculations for large molecular systems. Philos. Trans.- Royal Soc., Math. Phys. Eng. Sci., 2002, 360(1795), 1149-1164.
[http://dx.doi.org/10.1098/rsta.2002.0993] [PMID: 12804271]
[25]
Guo, W.; Wu, A.; Zhang, I.Y.; Xu, X. XO: An extended ONIOM method for accurate and efficient modeling of large systems. J. Comput. Chem., 2012, 33(27), 2142-2160.
[http://dx.doi.org/10.1002/jcc.23051] [PMID: 22764057]
[26]
Guo, W.; Wu, A.; Xu, X. XO: An extended ONIOM method for accurate and efficient geometry optimization of large molecules. Chem. Phys. Lett., 2010, 498(1-3), 203-208.
[http://dx.doi.org/10.1016/j.cplett.2010.08.033]
[27]
Rao, L.; Chi, B.; Ren, Y.; Li, Y.; Xu, X.; Wan, J. DOX: A new computational protocol for accurate prediction of the protein-ligand binding structures. J. Comput. Chem., 2016, 37(3), 336-344.
[http://dx.doi.org/10.1002/jcc.24217] [PMID: 26459237]
[28]
Wei, L.; Chi, B.; Ren, Y.; Rao, L.; Wu, J.; Shang, H.; Liu, J.; Xiao, Y.; Ma, M.; Xu, X.; Wan, J. Conformation Search Across Multiple-Level Potential-Energy Surfaces (CSAMP): A strategy for accurate prediction of protein–ligand binding structures. J. Chem. Theory Comput., 2019, 15(7), 4264-4279.
[http://dx.doi.org/10.1021/acs.jctc.8b01150] [PMID: 31142115]
[29]
Wei, L.; Chen, Y.; Liu, J.; Rao, L.; Ren, Y.; Xu, X.; Wan, J. Cov_DOX: A method for structure prediction of covalent protein-ligand bindings. J. Med. Chem., 2022, 65(7), 5528-5538.
[http://dx.doi.org/10.1021/acs.jmedchem.1c02007] [PMID: 35353519]
[30]
Mishra, S.K.; Koča, J. Assessing the performance of MM/PBSA, MM/GBSA, and QM–MM/GBSA approaches on protein/carbohydrate complexes: Effect of implicit solvent models, QM methods, and entropic contributions. J. Phys. Chem. B, 2018, 122(34), 8113-8121.
[http://dx.doi.org/10.1021/acs.jpcb.8b03655] [PMID: 30084252]
[31]
Van Der Spoel, D.; Lindahl, E.; Hess, B.; Groenhof, G.; Mark, A.E.; Berendsen, H.J.C. GROMACS: Fast, flexible, and free. J. Comput. Chem., 2005, 26(16), 1701-1718.
[http://dx.doi.org/10.1002/jcc.20291] [PMID: 16211538]
[32]
Abraham, M.J.; Murtola, T.; Schulz, R.; Páll, S.; Smith, J.C.; Hess, B.; Lindahl, E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 2015, 1-2, 19-25.
[http://dx.doi.org/10.1016/j.softx.2015.06.001]
[33]
Best, R.B.; Zhu, X.; Shim, J.; Lopes, P.E.M.; Mittal, J.; Feig, M.; MacKerell, A.D., Jr Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ ψ and side-chain χ(1) and χ(2) dihedral angles. J. Chem. Theory Comput., 2012, 8(9), 3257-3273.
[http://dx.doi.org/10.1021/ct300400x] [PMID: 23341755]
[34]
Zoete, V.; Cuendet, M.A.; Grosdidier, A.; Michielin, O. SwissParam: A fast force field generation tool for small organic molecules. J. Comput. Chem., 2011, 32(11), 2359-2368.
[http://dx.doi.org/10.1002/jcc.21816] [PMID: 21541964]
[35]
Jorgensen, W.L.; Chandrasekhar, J.; Madura, J.D.; Impey, R.W.; Klein, M.L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys., 1983, 79(2), 926-935.
[http://dx.doi.org/10.1063/1.445869]
[36]
Hess, B.; Bekker, H.; Berendsen, H.J.C.; Fraaije, J.G.E.M. LINCS: A linear constraint solver for molecular simulations. J. Comput. Chem., 1997, 18(12), 1463-1472.
[http://dx.doi.org/10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H]
[37]
Van Gunsteren, W.F.; Berendsen, H.J.C. A leap-frog algorithm for stochastic dynamics. Mol. Simul., 1988, 1(3), 173-185.
[http://dx.doi.org/10.1080/08927028808080941]
[38]
Verlet, L. Computer “experiments” on classical fluids. I. thermodynamical properties of lennard-jones molecules. Phys. Rev., 1967, 159(1), 98-103.
[http://dx.doi.org/10.1103/PhysRev.159.98]
[39]
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.
[http://dx.doi.org/10.1063/1.448118]
[40]
Parrinello, M.; Rahman, A. Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys., 1981, 52(12), 7182-7190.
[http://dx.doi.org/10.1063/1.328693]
[41]
Darden, T.; York, D.; Pedersen, L. Particle mesh Ewald: An N ⋅log(N) method for Ewald sums in large systems. J. Chem. Phys., 1993, 98(12), 10089-10092.
[http://dx.doi.org/10.1063/1.464397]
[42]
Essmann, U.; Perera, L.; Berkowitz, M.L.; Darden, T.; Lee, H.; Pedersen, L.G. A smooth particle mesh Ewald method. J. Chem. Phys., 1995, 103(19), 8577-8593.
[http://dx.doi.org/10.1063/1.470117]
[43]
Abraham, M.J.; D. v. d. S.; Lindahl, E.; Hess, B. The GROMACS development team, GROMACS User Manual version 2018.In: SoftwareX; , 2018.
[44]
Daura, X.; Gademann, K.; Jaun, B.; Seebach, D.; van Gunsteren, W.F.; Mark, A.E. Peptide folding: When simulation meets experiment. Angew. Chem. Int. Ed., 1999, 38(1-2), 236-240.
[http://dx.doi.org/10.1002/(SICI)1521-3773(19990115)38:1/2<236::AID-ANIE236>3.0.CO;2-M]
[45]
Fraccalvieri, D.; Pandini, A.; Stella, F.; Bonati, L. Conformational and functional analysis of molecular dynamics trajectories by Self-Organising Maps. BMC Bioinformatics, 2011, 12(1), 158.
[http://dx.doi.org/10.1186/1471-2105-12-158] [PMID: 21569575]
[46]
Peng, L.X. The role of computer simulations in the preclinical development of semiflexible polymeric anticancer therapeutics. PhD Thesis, University of California, San Diego, 2010.
[47]
Menchaca, T.M.; Portilla, C.J.; Zepeda, R.C. Past, Present, and Future of Molecular Docking; IntechOpen, 2020, p. 90921.
[http://dx.doi.org/10.5772/intechopen.90921]
[48]
Molecular Operating Environment (MOE), 2015.10; Chemical Computing Group ULC, 1010 Sherbrooke St. West, Suite #910, Montreal; QC, Canada, H3A 2R7, 2015.
[49]
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.
[http://dx.doi.org/10.1002/jcc.20084] [PMID: 15264254]
[50]
Kelley, L.A.; Gardner, S.P.; Sutcliffe, M.J. An automated approach for clustering an ensemble of NMR-derived protein structures into conformationally related subfamilies. Protein Eng. Des. Sel., 1996, 9(11), 1063-1065.
[http://dx.doi.org/10.1093/protein/9.11.1063] [PMID: 8961360]
[51]
Khandelwal, A.; Lukacova, V.; Comez, D.; Kroll, D.M.; Raha, S.; Balaz, S. A combination of docking, QM/MM methods, and MD simulation for binding affinity estimation of metalloprotein ligands. J. Med. Chem., 2005, 48(17), 5437-5447.
[http://dx.doi.org/10.1021/jm049050v] [PMID: 16107143]
[52]
Chai, J.D.; Head-Gordon, M. Long-range corrected hybrid density functionals with damped atom–atom dispersion corrections. Phys. Chem. Chem. Phys., 2008, 10(44), 6615-6620.
[http://dx.doi.org/10.1039/b810189b] [PMID: 18989472]
[53]
Stewart, J.J.P. Optimization of parameters for semiempirical methods V: Modification of NDDO approximations and application to 70 elements. J. Mol. Model., 2007, 13(12), 1173-1213.
[http://dx.doi.org/10.1007/s00894-007-0233-4] [PMID: 17828561]
[54]
Foresman, J.B.; Frisch, A.E. Exploring Chemistry with Electronic Structure Methods, 2nd ed; Gaussian, 1996.
[55]
Frisch, M. J.; Trucks, G. W.; Schlegel, H. B. Gaussian Team, Gaussian 09, Revision D.01, 2013.
[56]
Available from: https://www.rcsb.org/ (Accessed on: October 26, 2020).
[57]
Zanotti, G.; Malpeli, G.; Berni, R. The interaction of N-ethyl retinamide with plasma retinol-binding protein (RBP) and the crystal structure of the retinoid-RBP complex at 1.9-A resolution. J. Biol. Chem., 1993, 268(33), 24873-24879.
[http://dx.doi.org/10.1016/S0021-9258(19)74546-3] [PMID: 8227049]
[58]
Holt, D.A.; Luengo, J.I.; Yamashita, D.S.; Oh, H.J.; Konialian, A.L.; Yen, H.K.; Rozamus, L.W.; Brandt, M.; Bossard, M.J.; Levy, M.A.; Eggleston, D.S.; Liang, J.; Schultz, L.W.; Stout, T.J.; Clardy, J. Design, synthesis, and kinetic evaluation of high-affinity FKBP ligands and the X-ray crystal structures of their complexes with FKBP12. J. Am. Chem. Soc., 1993, 115(22), 9925-9938.
[http://dx.doi.org/10.1021/ja00075a008]
[59]
Istvan, E.S.; Deisenhofer, J. Structural mechanism for statin inhibition of HMG-CoA reductase. Science, 2001, 292(5519), 1160-1164.
[http://dx.doi.org/10.1126/science.1059344] [PMID: 11349148]
[60]
Sacchettini, J.C.; Gordon, J.I.; Banaszak, L.J. Crystal structure of rat intestinal fatty-acid-binding protein. J. Mol. Biol., 1989, 208(2), 327-339.
[http://dx.doi.org/10.1016/0022-2836(89)90392-6] [PMID: 2671390]
[61]
Nalam, M.N.L.; Ali, A.; Altman, M.D.; Reddy, G.S.K.K.; Chellappan, S.; Kairys, V.; Özen, A.; Cao, H.; Gilson, M.K.; Tidor, B.; Rana, T.M.; Schiffer, C.A. Evaluating the substrate-envelope hypothesis: Structural analysis of novel HIV-1 protease inhibitors designed to be robust against drug resistance. J. Virol., 2010, 84(10), 5368-5378.
[http://dx.doi.org/10.1128/JVI.02531-09] [PMID: 20237088]
[62]
Ali, A.; Reddy, G.S.K.K.; Cao, H.; Anjum, S.G.; Nalam, M.N.L.; Schiffer, C.A.; Rana, T.M. Discovery of HIV-1 protease inhibitors with picomolar affinities incorporating N-aryl-oxazolidinone-5-carboxamides as novel P2 ligands. J. Med. Chem., 2006, 49(25), 7342-7356.
[http://dx.doi.org/10.1021/jm060666p] [PMID: 17149864]
[63]
Ali, A.; Reddy, G.S.K.K.; Nalam, M.N.L.; Anjum, S.G.; Cao, H.; Schiffer, C.A.; Rana, T.M. Structure-based design, synthesis, and structure-activity relationship studies of HIV-1 protease inhibitors incorporating phenyloxazolidinones. J. Med. Chem., 2010, 53(21), 7699-7708.
[http://dx.doi.org/10.1021/jm1008743] [PMID: 20958050]
[64]
Altman, M.D.; Ali, A.; Kumar Reddy, G.S.K.; Nalam, M.N.L.; Anjum, S.G.; Cao, H.; Chellappan, S.; Kairys, V.; Fernandes, M.X.; Gilson, M.K.; Schiffer, C.A.; Rana, T.M.; Tidor, B. HIV-1 protease inhibitors from inverse design in the substrate envelope exhibit subnanomolar binding to drug-resistant variants. J. Am. Chem. Soc., 2008, 130(19), 6099-6113.
[http://dx.doi.org/10.1021/ja076558p] [PMID: 18412349]
[65]
Asojo, O.A.; Afonina, E.; Gulnik, S.V.; Yu, B.; Erickson, J.W.; Randad, R.; Medjahed, D.; Silva, A.M. Structures of Ser205 mutant plasmepsin II from Plasmodium falciparum at 1.8 Å in complex with the inhibitors rs367 and rs370. Acta Crystallogr. D Biol. Crystallogr., 2002, 58(12), 2001-2008.
[http://dx.doi.org/10.1107/S0907444902014695] [PMID: 12454457]
[66]
Asojo, O.A.; Gulnik, S.V.; Afonina, E.; Yu, B.; Ellman, J.A.; Haque, T.S.; Silva, A.M. Novel uncomplexed and complexed structures of plasmepsin II, an aspartic protease from Plasmodium falciparum. J. Mol. Biol., 2003, 327(1), 173-181.
[http://dx.doi.org/10.1016/S0022-2836(03)00036-6] [PMID: 12614616]
[67]
Safo, M.K.; Moure, C.M.; Burnett, J.C.; Joshi, G.S.; Abraham, D.J. High-resolution crystal structure of deoxy hemoglobin complexed with a potent allosteric effector. Protein Sci., 2001, 10(5), 951-957.
[http://dx.doi.org/10.1110/ps.50601] [PMID: 11316875]
[68]
Yuvaniyama, J.; Chitnumsub, P.; Kamchonwongpaisan, S.; Vanichtanankul, J.; Sirawaraporn, W.; Taylor, P.; Walkinshaw, M.D.; Yuthavong, Y. Insights into antifolate resistance from malarial DHFR-TS structures. Nat. Struct. Mol. Biol., 2003, 10(5), 357-365.
[http://dx.doi.org/10.1038/nsb921] [PMID: 12704428]
[69]
Dvir, H.; Jiang, H.L.; Wong, D.M.; Harel, M.; Chetrit, M.; He, X.C.; Jin, G.Y.; Yu, G.L.; Tang, X.C.; Silman, I.; Bai, D.L.; Sussman, J.L. X-ray structures of Torpedo californica acetylcholinesterase complexed with (+)-huperzine A and (-)-huperzine B: Structural evidence for an active site rearrangement. Biochemistry, 2002, 41(35), 10810-10818.
[http://dx.doi.org/10.1021/bi020151+] [PMID: 12196020]
[70]
McVey, C.E.; Walsh, M.A.; Dodson, G.G.; Wilson, K.S.; Brannigan, J.A. Crystal structures of penicillin acylase enzyme-substrate complexes: Structural insights into the catalytic mechanism. J. Mol. Biol., 2001, 313(1), 139-150.
[http://dx.doi.org/10.1006/jmbi.2001.5043] [PMID: 11601852]
[71]
Toney, J.H.; Hammond, G.G.; Fitzgerald, P.M.D.; Sharma, N.; Balkovec, J.M.; Rouen, G.P.; Olson, S.H.; Hammond, M.L.; Greenlee, M.L.; Gao, Y.D. Succinic acids as potent inhibitors of plasmid-borne IMP-1 metallo-beta-lactamase. J. Biol. Chem., 2001, 276(34), 31913-31918.
[http://dx.doi.org/10.1074/jbc.M104742200] [PMID: 11390410]