Discovery and Evaluation of Potent P<sub>1</sub> Aryl Heterocycle-Based Thrombin Inhibitors

Young, Matthew; Barrow, James C.; Glass, Kristen L.; Lundell, G. F.; Newton, Christina L.; Pellicore, Janetta M.; Rittle, Kenneth E.; Selnick, Harold G.; Stauffer, Kenneth J.; Vacca, Joseph P.; Williams, Peter; Bohn, Dennis L.; Clayton, Franklin C.; Cook, Jacquelynn J.; Krueger, Julie A.; Kuo, Lawrence C.; Lewis, Sidney D.; Lucas, Bobby J.; McMasters, Daniel R.; Miller‐Stein, Cynthia; Pietrak, Beth; Wallace, Audrey A.; White, Rebecca B.; Wong, Bradley K.; Yan, Yong-Bin; Nantermet, Philippe G.

doi:10.1021/jm030303e

Cited by 94 publications

(69 citation statements)

References 20 publications

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“…The distances between the corresponding oxygen and nitrogen atoms are 2.87, 2.88, and 2.85 , and thus the overall contribution of electrostatic energy is another important driving force for 177 binding to Ser214 and Gly216 ( Figure 6A and Table 3). Additionally, the P1 aromatic ring contacts the sulfur atoms of Cys191-Cys220 disulfide linkage, forming a donor-p interaction similar to that observed in other groups [53] ( Figure 7A), thus the van der Waals energy favors binding for these two residues. Although there is a strong ion-pair interaction between Glu217 and 177 (À17.89 kcal mol À1 in Table 3), the overall electrostatic energy disfavors the binding because of the penalty of the desolvation free energy (18.03 kcal mol À1 in Table 3).…”

Section: H T U N G T R E N N U N G (Complex)-contribution(inhibitor)-supporting

confidence: 64%

Selectivity of Neutral/Weakly Basic P1 Group Inhibitors of Thrombin and Trypsin by a Molecular Dynamics Study

Wu¹,

Han²,

Zhang³

2008

Chemistry A European J

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Molecular dynamics (MD) simulations followed by molecular mechanics generalized Born surface area (MM-GBSA) analyses have been carried out to study the selectivity of two neutral and weakly basic P1 group inhibitors (177 and CDA) to thrombin and trypsin. Detailed binding free energies between these inhibitors and individual protein residues are calculated by using a per-residue basis decomposition method. The analysis of the detailed interaction energies provides insight on the protein-inhibitor-binding mechanism and helps to elucidate the basis for achieving selectivity through interpretation of the structural and energetic results from the simulations. The study shows that the dominant factor of selectivity for both inhibitors is van der Waals energy, which suggests better shape complementarity and packing with thrombin. Nonpolar solvation free energy and total entropy contribution are also in favor of selectivity, but the contributions are much smaller. Binding mode and structural analysis show that 177 binds to thrombin and trypsin in a similar binding mode. In contrast, the CDA binds to thrombin and trypsin in very different modes.

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Section: H T U N G T R E N N U N G (Complex)-contribution(inhibitor)-supporting

confidence: 64%

Selectivity of Neutral/Weakly Basic P1 Group Inhibitors of Thrombin and Trypsin by a Molecular Dynamics Study

Wu¹,

Han²,

Zhang³

2008

Chemistry A European J

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“…Molecular modeling and docking of the pyridine derivative interactions were performed using the crystal structure of thrombin available through the Protein Data Bank (PDB code: 1SL3, [15]). Water molecules and other heteroatoms were deleted from the thrombin structure before docking.…”

Section: Methodsmentioning

confidence: 99%

Molecular modeling optimization of anticoagulant pyridine derivatives

Prada

Madden

Ostrov

et al. 2008

Journal of Molecular Graphics and Modelling

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Intravascular clotting remains a major health problem in the United States, the most prominent being deep vein thrombosis, pulmonary embolism and thromboembolic stroke. Previous reports on the use of pyridine derivatives in cardiovascular drug development encourage us to pursue new types of compounds based on a pyridine scaffold. Eleven pyridine derivatives (oximes, semicarbazones, N-oxides) previously synthesized in our laboratories were tested as anticoagulants on pooled normal plasma using the prothrombin time (PT) protocol. The best anticoagulant within the oxime series was compound AF4, within the oxime N-oxide series was compound AF4-N-Oxide, and within the semicarbazone series, compound MD1-30Y. We also used a molecular modeling approach to guide our efforts, and found that there was good correlation between coagulation data and computational energy scores. Molecular docking was performed to target the active site of thrombin with the DOCK v5.2 package. The results of molecular modeling indicate that improvement in anticoagulant activities can be expected by functionalization at the 3-position of the pyridine ring and by N-oxide formation. Results reported here prove the suitability of DOCK in the lead optimization process.

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“…Noncovalent complex may achieve a very tight binding as well in this case. In fact, the tightest binding (K i = 1.4 pM) among all these thrombin complexes is achieved by a noncovalent complex formed between human alpha-thrombin and a heterocycle-aryl based inhibitor (PDB entry 1SL3) [31] . Why does not the formation of a covalent linkage lead to a dramatic increase in protein-ligand binding affinity?…”

Section: Comparison Of the Binding Constants Of Covalent And Noncovalmentioning

confidence: 99%

A Statistical Survey on the Binding Constants of Covalently Bound Protein–Ligand Complexes

Liu

et al. 2010

Molecular Informatics

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We have conducted a statistical survey to compare the binding constants of covalently and noncovalently bound protein-ligand complexes as two groups. In our study, a total of 1602 complexes formed between various types of proteins and small-molecule ligands were selected from the PDBbind database (version 2008), all of which had high-resolution three-dimensional structures and reliable experimentally measured binding constants. These complexes were further classified as 79 covalent complexes, 131 Zn-containing complexes, and 1392 noncovalent complexes. Covalent complexes formed through reversible mechanisms are found to be associated with higher binding constants than noncovalent complexes. Two-sample T-test indicates that the difference is statistically significant. The advantage, however, is only modest (<20 folds). The same trend is also observed on a set of covalent and noncovalent complexes formed by thrombin. Our results indicate that reversible covalent bonding formed between protein and ligand will not automatically lead to a much tighter binding in general. Thus, our survey does not provide any supporting evidence for Houk's hypothesis which states that covalent bonding formed between enzyme and transition state accounts for the extraordinary proficiency of enzymes.

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Discovery and Evaluation of Potent P₁ Aryl Heterocycle-Based Thrombin Inhibitors

Cited by 94 publications

References 20 publications

Selectivity of Neutral/Weakly Basic P1 Group Inhibitors of Thrombin and Trypsin by a Molecular Dynamics Study

Selectivity of Neutral/Weakly Basic P1 Group Inhibitors of Thrombin and Trypsin by a Molecular Dynamics Study

Molecular modeling optimization of anticoagulant pyridine derivatives

A Statistical Survey on the Binding Constants of Covalently Bound Protein–Ligand Complexes

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Discovery and Evaluation of Potent P1 Aryl Heterocycle-Based Thrombin Inhibitors

Cited by 94 publications

References 20 publications

Selectivity of Neutral/Weakly Basic P1 Group Inhibitors of Thrombin and Trypsin by a Molecular Dynamics Study

Selectivity of Neutral/Weakly Basic P1 Group Inhibitors of Thrombin and Trypsin by a Molecular Dynamics Study

Molecular modeling optimization of anticoagulant pyridine derivatives

A Statistical Survey on the Binding Constants of Covalently Bound Protein–Ligand Complexes

Contact Info

Product

Resources

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Discovery and Evaluation of Potent P₁ Aryl Heterocycle-Based Thrombin Inhibitors