Probing the Reaction Mechanism of the D-ala-D-ala Dipeptidase, VanX, by Using Stopped-Flow Kinetic and Rapid-Freeze Quench EPR Studies on the Co(II)-Substituted Enzyme

In an effort to probe the reaction mechanism of VanX, the D-ala-D-ala dipeptidase required for high-level vancomycin resistance in bacteria, stopped-flow kinetic and rapid-freeze quench EPR studies were conducted on the Co(II)-substituted enzyme when reacted with d-ala-d-ala. The intensity of the Co(II) ligand field band at 550 nm decreased (ε550 = 140 to 18 M-1 cm-1) when VanX was reacted with substrate, suggesting that the coordination number of the metal increases from 5 to 6 upon substrate binding. The stopped-flow trace was fitted to a kinetic mechanism that suggests the presence of an intermediate whose breakdown is rate-limiting. Rapid-freeze quench EPR studies verified the presence of a reaction intermediate that exhibits an unusually low hyperfine constant (33 G), which suggests a bidentate coordination of the intermediate to the metal center. The EPR studies also identified a distinct enzyme product complex. The results were used to offer a detailed reaction mechanism for VanX that can be used to guide future inhibitor design efforts

Structure and Mechanism of Copper- and Nickel-Substituted Analogues of Metallo-β-lactamase L1

In an effort to further probe metal binding to metallo-β-lactamase L1 (mβl L1), Cu- (Cu-L1) and Ni-substituted (Ni-L1) L1 were prepared and characterized by kinetic and spectroscopic studies. Cu-L1 bound 1.7 equiv of Cu and small amounts of Zn(II) and Fe. The EPR spectrum of Cu-L1 exhibited two overlapping, axial signals, indicative of type 2 sites with distinct affinities for Cu(II). Both signals indicated multiple nitrogen ligands. Despite the expected proximity of the Cu(II) ions, however, only indirect evidence was found for spin−spin coupling. Cu-L1 exhibited higher kcat (96 s−1) and Km (224 μM) values, as compared to the values of dinuclear Zn(II)-containing L1, when nitrocefin was used as substrate. The Ni-L1 bound 1 equiv of Ni and 0.3 equiv of Zn(II). Ni-L1 was EPR-silent, suggesting that the oxidation state of nickel was +2; this suggestion was confirmed by 1H NMR spectra, which showed relatively sharp proton resonances. Stopped-flow kinetic studies showed that ZnNi-L1 stabilized significant amounts of the nitrocefin-derived intermediate and that the decay of intermediate is rate-limiting. 1H NMR spectra demonstrate that Ni(II) binds in the Zn2 site and that the ring-opened product coordinates Ni(II). Both Cu-L1 and ZnNi-L1 hydrolyze cephalosporins and carbapenems, but not penicillins, suggesting that the Zn2 site modulates substrate preference in mβl L1. These studies demonstrate that the Zn2 site in L1 is very flexible and can accommodate a number of different transition metal ions; this flexibility could possibly offer an organism that produces L1 an evolutionary advantage when challenged with β-lactam-containing antibiotics

Mechanistic Studies on the Mononuclear Zn\u3csup\u3eII\u3c/sup\u3e-Containing Metallo-β-lactamase ImiS from \u3cem\u3eAeromonas sobria\u3c/em\u3e

In an effort to understand the reaction mechanism of a B2 metallo-β-lactamase, steady-state and pre-steady-state kinetic and rapid freeze quench electron paramagnetic resonance (EPR) studies were conducted on ImiS and its reaction with imipenem and meropenem. pH dependence studies revealed no inflection points in the pH range of 5.0−8.5, while proton inventories demonstrated at least 1 rate-limiting proton transfer. Site-directed mutagenesis studies revealed that Lys224 plays a catalytic role in ImiS, while the side chain of Asn233 does not play a role in binding or catalysis. Stopped-flow fluorescence studies on ImiS, which monitor changes in tryptophan fluorescence on the enzyme, and its reaction with imipenem and meropenem revealed biphasic fluorescence time courses with a rate of fluorescence loss of 160 s-1 and a slower rate of fluorescence regain of 98 s-1. Stopped-flow UV−vis studies, which monitor the concentration of substrate, revealed a rapid loss in absorbance during catalysis with a rate of 97 s-1. These results suggest that the rate-limiting step in the reaction catalyzed by ImiS is C−N bond cleavage. Rapid freeze quench EPR studies on CoII-substituted ImiS demonstrated the appearance of a rhombic signal after 10 ms that is assigned to a reaction intermediate that has a five-coordinate metal center. A distinct product (EP) complex was also observed and began to appear in 18−19 ms. When these results are taken together, they allow for a reaction mechanism to be offered for the B2 metallo-β-lactamases and demonstrate that the mono- and dinuclear ZnII-containing enzymes share a common rate-limiting step, which is C−N bond cleavage

Differential Binding of Co(II) and Zn(II) to Metallo-β-Lactamase Bla2 from \u3cem\u3eBacillus anthracis\u3c/em\u3e

In an effort to probe the structure, mechanism, and biochemical properties of metallo-β-lactamase Bla2 from Bacillus anthracis, the enzyme was overexpressed, purified, and characterized. Metal analyses demonstrated that recombinant Bla2 tightly binds 1 equiv of Zn(II). Steady-state kinetic studies showed that mono-Zn(II) Bla2 (1Zn-Bla2) is active, while di-Zn(II) Bla2 (ZnZn-Bla2) was unstable. Catalytically, 1Zn-Bla2 behaves like the related enzymes CcrA and L1. In contrast, di-Co(II) Bla2 (CoCo-Bla2) is substantially more active than the mono-Co(II) analogue. Rapid kinetics and UV−vis, 1H NMR, EPR, and EXAFS spectroscopic studies show that Co(II) binding to Bla2 is distributed, while EXAFS shows that Zn(II) binding is sequential. To our knowledge, this is the first documented example of a Zn enzyme that binds Co(II) and Zn(II) via distinct mechanisms, underscoring the need to demonstrate transferability when extrapolating results on Co(II)-substituted proteins to the native Zn(II)-containing forms

Principles for Understanding the Accuracy of SHAPE-Directed RNA Structure Modeling

Accurate RNA structure modeling is an important, incompletely solved, challenge. Single-nucleotide resolution SHAPE (selective 2'-hydroxyl acylation analyzed by primer extension) yields an experimental measurement of local nucleotide flexibility that can be incorporated as pseudo-free energy change constraints to direct secondary structure predictions. Prior work from our laboratory has emphasized both the overall accuracy of this approach and the need for nuanced interpretation of some apparent discrepancies between modeled and accepted structures. Recent studies by Das and colleagues [Kladwang et al., Biochemistry 50:8049 (2011) and Nat. Chem. 3:954 (2011)], focused on analyzing six small RNAs, yielded poorer RNA secondary structure predictions than expected based on prior benchmarking efforts. To understand the features that led to these divergent results, we re-examined four RNAs yielding the poorest results in this recent work – tRNAPhe, the adenine and cyclic-di-GMP riboswitches, and 5S rRNA. Most of the errors reported by Das and colleagues reflected non-standard experiment and data processing choices, and selective scoring rules. For two RNAs, tRNAPhe and the adenine riboswitch, secondary structure predictions are nearly perfect if no experimental information is included but were rendered inaccurate by the Das and colleagues SHAPE data. When best practices were used, single-sequence SHAPE-directed secondary structure modeling recovered ~93% of individual base pairs and greater than 90% of helices in the four RNAs, essentially indistinguishable from the mutate-and-map approach with the exception of a single helix in the 5S rRNA. The field of experimentally-directed RNA secondary structure prediction is entering a phase focused on the most difficult prediction challenges. We outline five constructive principles for guiding this field forward

On the significance of an RNA tertiary structure prediction

Tertiary structure prediction is important for understanding structure–function relationships for RNAs whose structures are unknown and for characterizing RNA states recalcitrant to direct analysis. However, it is unknown what root-mean-square deviation (RMSD) corresponds to a statistically significant RNA tertiary structure prediction. We use discrete molecular dynamics to generate RNA-like folds for structures up to 161 nucleotides (nt) that have complex tertiary interactions and then determine the RMSD distribution between these decoys. These distributions are Gaussian-like. The mean RMSD increases with RNA length and is smaller if secondary structure constraints are imposed while generating decoys. The compactness of RNA molecules with true tertiary folds is intermediate between closely packed spheres and a freely jointed chain. We use this scaling relationship to define an expression relating RMSD with the confidence that a structure prediction is better than that expected by chance. This is the prediction significance, and corresponds to a P-value. For a 100-nt RNA, the RMSD of predicted structures should be within 25 Å of the accepted structure to reach the P ≤ 0.01 level if the secondary structure is predicted de novo and within 14 Å if secondary structure information is used as a constraint. This significance approach should be useful for evaluating diverse RNA structure prediction and molecular modeling algorithms

Principles for Understanding the Accuracy of SHAPE-Directed RNA Structure Modeling

Accurate RNA structure modeling is an important, incompletely solved, challenge. Single-nucleotide resolution SHAPE (selective 2'-hydroxyl acylation analyzed by primer extension) yields an experimental measurement of local nucleotide flexibility that can be incorporated as pseudo-free energy change constraints to direct secondary structure predictions. Prior work from our laboratory has emphasized both the overall accuracy of this approach and the need for nuanced interpretation of some apparent discrepancies between modeled and accepted structures. Recent studies by Das and colleagues [Kladwang et al., Biochemistry 50:8049 (2011) and Nat. Chem. 3:954 (2011)], focused on analyzing six small RNAs, yielded poorer RNA secondary structure predictions than expected based on prior benchmarking efforts. To understand the features that led to these divergent results, we re-examined four RNAs yielding the poorest results in this recent work – tRNA(Phe), the adenine and cyclic-di-GMP riboswitches, and 5S rRNA. Most of the errors reported by Das and colleagues reflected non-standard experiment and data processing choices, and selective scoring rules. For two RNAs, tRNA(Phe) and the adenine riboswitch, secondary structure predictions are nearly perfect if no experimental information is included but were rendered inaccurate by the Das and colleagues SHAPE data. When best practices were used, single-sequence SHAPE-directed secondary structure modeling recovered ~93% of individual base pairs and greater than 90% of helices in the four RNAs, essentially indistinguishable from the mutate-and-map approach with the exception of a single helix in the 5S rRNA. The field of experimentally-directed RNA secondary structure prediction is entering a phase focused on the most difficult prediction challenges. We outline five constructive principles for guiding this field forward

Understanding Flavin-Dependent Halogenase Reactivity via Substrate Activity Profiling

The activity of four native FDHs and four engineered FDH variants on 93 low-molecular-weight arenes was used to generate FDH substrate activity profiles. These profiles provided insights into how substrate class, functional group substitution, electronic activation, and binding affect FDH activity and selectivity. The enzymes studied could halogenate a far greater range of substrates than have been previously recognized, but significant differences in their substrate specificity and selectivity were observed. Trends between the electronic activation of each site on a substrate and halogenation conversion at that site were established, and these data, combined with docking simulations, suggest that substrate binding can override electronic activation even on compounds differing appreciably from native substrates. These findings provide a useful framework for understanding and exploiting FDH reactivity for organic synthesis

Principles for Understanding the Accuracy of SHAPE-Directed RNA Structure Modeling

Accurate RNA structure modeling is an important, incompletely solved, challenge. Single-nucleotide resolution SHAPE (selective 2′-hydroxyl acylation analyzed by primer extension) yields an experimental measurement of local nucleotide flexibility that can be incorporated as pseudo-free energy change constraints to direct secondary structure predictions. Prior work from our laboratory has emphasized both the overall accuracy of this approach and the need for nuanced interpretation of modeled structures. Recent studies by Das and colleagues [Kladwang, W., et al. (2011) <i>Biochemistry 50</i>, 8049; <i>Nat. Chem</i>. <i>3</i>, 954], focused on analyzing six small RNAs, yielded poorer RNA secondary structure predictions than expected on the basis of prior benchmarking efforts. To understand the features that led to these divergent results, we re-examined four RNAs yielding the poorest results in this recent work: tRNA^Phe, the adenine and cyclic-di-GMP riboswitches, and 5S rRNA. Most of the errors reported by Das and colleagues reflected nonstandard experiment and data processing choices, and selective scoring rules. For two RNAs, tRNA^Phe and the adenine riboswitch, secondary structure predictions are nearly perfect if no experimental information is included but were rendered inaccurate by the SHAPE data of Das and colleagues. When best practices were used, single-sequence SHAPE-directed secondary structure modeling recovered ∼93% of individual base pairs and >90% of helices in the four RNAs, essentially indistinguishable from the results of the mutate-and-map approach with the exception of a single helix in the 5S rRNA. The field of experimentally directed RNA secondary structure prediction is entering a phase focused on the most difficult prediction challenges. We outline five constructive principles for guiding this field forward