RNA molecules are essential for cellular information transfer and gene regulation, and RNAs have been implicated in many human diseases. Messenger and non-coding RNAs contain highly structured elements, and evidence suggests that many of these structures are important for function. Targeting these RNAs with small molecules offers opportunities to therapeutically modulate numerous cellular processes, including those linked to 'undruggable' protein targets. Despite this promise, there is currently only a single class of human-designed small molecules that target RNA used clinically - the linezolid antibiotics. However, a growing number of small-molecule RNA ligands are being identified, leading to burgeoning interest in the field. Here, we discuss principles for discovering small-molecule drugs that target RNA and argue that the overarching challenge is to identify appropriate target structures - namely, in disease-causing RNAs that have high information content and, consequently, appropriate ligand-binding pockets. If focus is placed on such druggable binding sites in RNA, extensive knowledge of the typical physicochemical properties of drug-like small molecules could then enable small-molecule drug discovery for RNA targets to become (only) roughly as difficult as for protein targets.

A pseudoknot forms in an RNA when nucleotides in a loop pair with a region outside the helices that close the loop. Pseudoknots occur relatively rarely in RNA but are highly overrepresented in functionally critical motifs in large catalytic RNAs, in riboswitches, and in regulatory elements of viruses. Pseudoknots are usually excluded from RNA structure prediction algorithms. When included, these pairings are difficult to model accurately, especially in large RNAs, because allowing this structure dramatically increases the number of possible incorrect folds and because it is difficult to search the fold space for an optimal structure. We have developed a concise secondary structure modeling approach that combines SHAPE (selective 2′-hydroxyl acylation analyzed by primer extension) experimental chemical probing information and a simple, but robust, energy model for the entropic cost of single pseudoknot formation. Structures are predicted with iterative refinement, using a dynamic programming algorithm. This melded experimental and thermodynamic energy function predicted the secondary structures and the pseudoknots for a set of 21 challenging RNAs of known structure ranging in size from 34 to 530 nt. On average, 93% of known base pairs were predicted, and all pseudoknots in wellfolded RNAs were identified. Information is encoded in an RNA molecule at two levels: in its primary sequence and in its ability to form higher-order secondary and tertiary structures. Nearly all RNAs can fold to form some secondary structure and, in many RNAs, highly structured regions encode important regulatory motifs. Such structured regulatory elements can be composed of canonical base pairs but may also feature specialized and distinctive RNA structures. Among the best characterized of these specialized structures are RNA pseudoknots. Pseudoknots are relatively rare but occur overwhelmingly in functionally important regions of RNA (2-4). For example, all of the large catalytic RNAs contain pseudoknots (5, 6); roughly two-thirds of the known classes of riboswitches contain pseudoknots that appear to be essential for ligand binding and gene regulatory functions (7); and pseudoknots occur prominently in the regulatory elements that viruses use to usurp cellular metabolism (3). Pseudoknots are thus harbingers of biological function. An important and challenging goal is to identify these structures reliably.Pseudoknots are excluded from the most widely used algorithms that model RNA secondary structure (8). This exclusion is based on the challenge of incorporating the pseudoknot structure into the efficient dynamic programming algorithm used in the most popular secondary structure prediction approaches and because of the additional computational effort required. The prediction of lowest free energy structures with pseudoknots is NP-complete (9), which means that lowest free energy structure cannot be solved as a function of sequence length in polynomial time. In addition, allowing pseudoknots greatly increases the number of (incorrect) hel...

SUMMARY Messenger RNAs (mRNAs) can fold into complex structures that regulate gene expression. Resolving such structures de novo has remained challenging and has limited understanding of the prevalence and functions of mRNA structure. We use SHAPE-MaP experiments in living E. coli cells to derive quantitative, nucleotide-resolution structure models for 194 endogenous transcripts encompassing approximately 400 genes. Individual mRNAs have exceptionally diverse architectures, and most contain well-defined structures. Active translation destabilizes mRNA structure in cells. Nevertheless, mRNA structure remains similar between in-cell and cell-free environments, indicating broad potential for structure-mediated gene regulation. We find that translation efficiency of endogenous genes is regulated by unfolding kinetics of structures overlapping the ribosome binding site. We discover conserved structured elements in 35% of untranslated regions, several of which we validate as novel protein binding motifs. RNA structure regulates every gene studied here in a meaningful way, implying that most functional structures remain to be discovered.

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.

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 impact FDH activity and selectivity. The enzymes studied could halogenate a far greater range of substrates than 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.

In an effort to understand the reaction mechanism of a B2 metallo-β-lactamase, steady-state and presteady state kinetic and rapid-freeze quench 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. Sitedirected 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 Co(II)-substituted ImiS demonstrated the appearance of a rhombic signal after 10 milliseconds that is assigned to a reaction intermediate that has a 5-coordinate metal center. A distinct product (EP) complex was also observed and began to appear in 18-19 milliseconds. Taken together, these results allow for a reaction mechanism to be offered for the B2 metallo-β-lactamases and demonstrates that the mononuclear Zn(II)-and dinuclear Zn(II)-containing enzymes share a common rate-limiting step, which is C-N bond cleavage.Bacterial resistance to antibiotics is a growing clinical concern (1,2). Zn(II)-containing β-lactamases (metallo-β-lactamases, MβL's) contain 1-2 moles of Zn(II) per mole of enzyme, hydrolyze all known cephalosporins, carbapenems and penicillins, are not inhibited by clavulanic acid and other classical β-lactamase inhibitors, and have no known clinically-useful inhibitor towards them (3,4). Previous studies have shown that there is significant structural and mechanistic diversity among the MβL's, leading to the grouping of the enzymes into three distinct subclasses: B1, B2, and B3 (5,6). Sequence identity ranges from 25-40% between members in one subclass and from 10-20% between members in different subclasses. Subclass B1 enzymes have been found in strains of Bacillus, Bacteroides, Pseudomonas, Serratia, and Chryseobacterium, and subclass B3 enzymes have been found in strains of Stenotrophomonas, Legionella, Fluoribacter, Janthinobacterium and Caulobacter (3,4). Enzymes from the B1 and B3 subclasses have broad substrate profiles and require two Zn(II) † This work was supported by the National Institutes of Health (GM40052 to MWC; AI056231 to BB, and EB001980 to the Medical College of Wisconsin).*To whom correspondence should be addressed: M. W. Crowder, e-mail: crowdemw@muohio.edu, phone: (513) 529-7274, fax: (513) 529-5715. NIH Public Access Author ManuscriptBiochemistry. A...

In an effort to probe the structure, mechanism, and biochemical properties of metallo-β-lactamase Bla2 from Bacillus anthracis, the enzyme was over-expressed, purified, and characterized. Metal analyses demonstrated that recombinant Bla2 tightly binds 1 eq 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, diCo(II) Bla2 (CoCo-Bla2) is substantially more active than the mono-Co(II) analog. Rapid kinetics and UV-Vis, 1 H NMR, EPR, and EXAFS spectroscopic studies show that Co(II) binding to Bla2 is distrubuted, 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.

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.