Positions of His‐64 and a bound water in human carbonic anhydrase II upon binding three structurally related inhibitors

Smith, Graham; Alexander, Richard; Christianson, D.W.; McKeever, Brian; Ponticello, Gerald S.; Springer, James P.; Randall, William C.; Baldwin, John J.; Habecker, Charles N.

doi:10.1002/pro.5560030115

Cited by 64 publications

(54 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To do this we employed tradi- tional medicinal chemistry principles and supplemented this approach with structure-based drug design. Structure-based drug design has been extremely helpful in the development of a number of FDA-approved drugs such as the carbonic anhydrase inhibitor, dorzolamide (47), and many HIV protease inhibitors, including indinavir (48,49) and nelfinavir (50,51). X-ray crystallography also proved invaluable in explaining the mechanism of action for imatinib, the first FDA-approved kinase inhibitor by illuminating the critical observation that imatinib bound to the inactive conformation of the enzyme (52).…”

Section: Discussionmentioning

confidence: 99%

Structure-Activity Relationships and X-ray Structures Describing the Selectivity of Aminopyrazole Inhibitors for c-Jun N-terminal Kinase 3 (JNK3) over p38

Kamenecka

Habel

Duckett

et al. 2009

Journal of Biological Chemistry

View full text Add to dashboard Cite

c-Jun N-terminal kinase 3␣1 (JNK3␣1) is a mitogen-activated protein kinase family member expressed primarily in the brain that phosphorylates protein transcription factors, including c-Jun and activating transcription factor-2 (ATF-2) upon activation by a variety of stress-based stimuli. In this study, we set out to design JNK3-selective inhibitors that had >1000-fold selectivity over p38, another closely related mitogen-activated protein kinase family member. To do this we employed traditional medicinal chemistry principles coupled with structurebased drug design. Inhibitors from the aminopyrazole class, such as SR-3576, were found to be very potent JNK3 inhibitors (IC 50 ‫؍‬ 7 nM) with >2800-fold selectivity over p38 (p38 IC 50 > 20 M) and had cell-based potency of ϳ1 M. In contrast, indazole-based inhibitors exemplified by SR-3737 were potent inhibitors of both JNK3 (IC 50 ‫؍‬ 12 nM) and p38 (IC 50 ‫؍‬ 3 nM). These selectivity differences between the indazole class and the aminopyrazole class came despite nearly identical binding (root mean square deviation ‫؍‬ 0.33 Å ) of these two compound classes to JNK3. The structural features within the compounds giving rise to the selectivity in the aminopyrazole class include the highly planar nature of the pyrazole, N-linked phenyl structures, which better occupied the smaller active site of JNK3 compared with the larger active site of p38.Because the initial reports on the discovery of p38 (1) and c-Jun N-terminal kinase (JNK) 2 (2-6) these mitogen-activated protein kinase family members have generated great interest as drug targets. p38 especially has garnered considerable interest, particularly for the treatment of rheumatoid arthritis and Crohn disease, and numerous compounds have entered clinical trials for these indications (7-11). Because most of the p38 inhibitors are competitive versus ATP (12-17), and there are 518 kinases in the genome, it was crucial to develop compounds that are selective against a broad panel of kinases so that compounds could be advanced to clinical development. The molecular basis that gives rise to selective p38 inhibitors from numerous structural classes has been reported (18 -20) and is centered on amino acid differences at the so-called "gatekeeper" Thr-106 residue in p38 (Met in all of the JNK isoforms and Gln in extracellular regulated kinase, the other mitogenactivated protein kinase family member). Many compounds have been synthesized that take advantage of this deeper hydrophobic pocket in p38, compared with JNK3, and the structures of the compounds have included trifluoromethyl and other large moieties, which all contribute to p38 selectivity (21). In contrast to p38, there have been fewer reports for selective JNK inhibitors, and the clinical development of JNK inhibitors also lags that of p38. Despite the paucity of highly selective JNK inhibitors that have advanced to clinical development, numerous recent reports have begun to emerge that show compounds from various structural classes (benzothiazole pyrimidines, aminopyr...

show abstract

Section: Discussionmentioning

confidence: 99%

Structure-Activity Relationships and X-ray Structures Describing the Selectivity of Aminopyrazole Inhibitors for c-Jun N-terminal Kinase 3 (JNK3) over p38

Kamenecka

Habel

Duckett

et al. 2009

Journal of Biological Chemistry

View full text Add to dashboard Cite

show abstract

“…The experimental structures of carbonic anhydrase II complexed with three structurally related inhibitors (1cnx, 1cnw, and 1cny) 42 reveal that the conformation of the native protein remains rather unchanged upon binding. However, another set of three experimentally determined carbonic anhydrase II complexes 40 show that in the binding of a specific inhibitor (1cil), the side chain attached to the 4-amino group is in a conformation that forces His-64 to occupy a nonnative position and causes the bound water to be absent. This change in conformation was used to explain 40 why 1cil binds more tightly than the other two structurally related inhibitors also studied in that paper (PDB codes 1cin and 1cim).…”

Section: Changes In the Protein Conformationmentioning

confidence: 99%

“…However, another set of three experimentally determined carbonic anhydrase II complexes 40 show that in the binding of a specific inhibitor (1cil), the side chain attached to the 4-amino group is in a conformation that forces His-64 to occupy a nonnative position and causes the bound water to be absent. This change in conformation was used to explain 40 why 1cil binds more tightly than the other two structurally related inhibitors also studied in that paper (PDB codes 1cin and 1cim). The structure of the 1okl complex 43 shows that a conformational change of Leu-198 is required to accommodate that ligand, compared to other complexes of cah.…”

Section: Changes In the Protein Conformationmentioning

confidence: 99%

“…For carboxypeptidase and thermolysin without the ligand, the Zn 2+ is coordinated to two histidine groups, one glutamate group, and one water molecule. Substrate binding displaces the coordinated water from the active site, 36,40 and it has been suggested that further stabilization of the complexes is achieved through watermediated hydrogen bonds. 38 The native conformation of thermolysin remains essentially unchanged upon binding numerous inhibitors.…”

Section: Database Screening Efficacymentioning

confidence: 99%

See 1 more Smart Citation

HierVLS Hierarchical Docking Protocol for Virtual Ligand Screening of Large-Molecule Databases

et al. 2003

View full text Add to dashboard Cite

To provide practical means for rapidly scanning the extensive experimental combinatorial chemistry libraries now available for high-throughput screening (HTS), it is essential to establish computational virtual ligand screening (VLS) techniques to rapidly identify out of a large library all active compounds against a particular protein target. Toward this goal we developed HierVLS, a fast hierarchical docking approach that starts with a coarse grain conformational search over a large number of configurations filtered with a fast but crude energy function, followed by a succession of finer grain levels, using successively more accurate but more expensive descriptions of the ligand-protein-solvent interactions to filter successively fewer cases. The final step of this procedure optimizes one configuration of the ligand in the protein site using our most accurate energy expression and description of the solvent, which would be impractical for all conformations and sites sampled in the coarse level. HierVLS is based on the HierDock approach, but rather than allowing an hour or more to determine the best binding site and energy for each ligands (as in HierDock), we have adapted our procedure so that it can lead to reliable results while using only 4 min (866 MHz Pentium III processor) per ligand. To validate the accuracy for HierVLS to predict the experimentally observed binding conformation, we considered 37 cocrystal structures comprising 11 target proteins. We find that HierVLS identifies the correct binding mode for all 37 cocrystals. In addition, the calculated binding energies correlate well with available experimental binding constants. To validate how well HierVLS can identify the correct ligand in an extensive library of decoys, we considered a library of over 10 000 molecules. HierVLS identifies 26 out of the 37 cases in the top 2% ranked by binding affinity among the 10 037 molecules. The failures result from either metalcontaining sites on the protein or water-mediated ligand-protein interactions, which we anticipate can be solved within the constraints of practical VLS. We then applied HierVLS to screen a 55000-compound virtual library against the target protein-tyrosine phosphatase 1B (ptp1b). The top 250 compounds by binding affinity included all six ptp1b cocrystal ligands added to the library plus three other experimentally confirmed binders. The best (top 1) binder is an experimentally confirmed positive. We conclude that HierVLS is useful for selecting leads for a particular target out of large combinatorial databases.

show abstract

“…For example, in HCAII, the ligand forms a metal-ligand bond that is one of the primary binding interactions [14] with Zinc and the bond length is 1.96 Å (Fig. 2b).…”

Section: Metal-ligand Interactionsmentioning

confidence: 99%

GemAffinity: A Scoring Function for Predicting Binding Affinity and Virtual Screening

Hsu¹,

Chen²,

Yang

2009

2009 IEEE International Conference on Bioinformatics and Biomedicine

View full text Add to dashboard Cite

Abstract-Prediction of protein-ligand binding affinities is an important issue in molecular recognition and virtual screening. We have developed a scoring function, namely GemAffinity, to predict binding affinities by analyzing 88 descriptors derived from 891 protein-ligand structures selected from the Protein Data Bank (PDB). Based on these 88 descriptors, we derived GemAffinity using a stepwise regression method to identify five descriptors, including van der Waals contact; metal-ligand interactions; water effects; ligand deformation penalties; and highly conserved residues interacting to a bound ligand with hydrogen bonds. GemAffinity was evaluated on an independent set, and the correlation between predicted and experimental values is 0.572. GemAffinity is the best among 13 methods on this set. Our GemAffinity was then applied to virtual screening for thymidine kinase (TK), human carbonic anhydrase II (HCAII), estrogen receptor of antagonists (ER) and agonists (ERA). Experimental results indicate thatGemAffinity is able to reduce the disadvantages (i.e. preferring highly polar or high molecular weight compounds) of energy-based scoring functions. In addition, GemAffinity easily combined with other scoring functions to enrich screening accuracies. We believe that GemAffinity is useful to predict binding affinity and virtual screening.

show abstract

Positions of His‐64 and a bound water in human carbonic anhydrase II upon binding three structurally related inhibitors

Cited by 64 publications

References 24 publications

Structure-Activity Relationships and X-ray Structures Describing the Selectivity of Aminopyrazole Inhibitors for c-Jun N-terminal Kinase 3 (JNK3) over p38

Structure-Activity Relationships and X-ray Structures Describing the Selectivity of Aminopyrazole Inhibitors for c-Jun N-terminal Kinase 3 (JNK3) over p38

HierVLS Hierarchical Docking Protocol for Virtual Ligand Screening of Large-Molecule Databases

GemAffinity: A Scoring Function for Predicting Binding Affinity and Virtual Screening

Contact Info

Product

Resources

About