Pervasive Regulatory Functions of mRNA Structure Revealed by High-Resolution SHAPE Probing.

mRNAs can fold into complex structures that regulate gene expression. Resolving such structures de novo has remained challenging and has limited our 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 the 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 UTRs, 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.

Accurate SHAPE-directed RNA secondary structure modeling, including pseudoknots.

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 well-folded RNAs were identified.

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) Biochemistry 50, 8049; Nat. Chem. 3, 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.

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 A of the accepted structure to reach the P

Structure and mechanism of copper- and nickel-substituted analogues of metallo-beta-lactamase L1.

In an effort to further probe metal binding to metallo-beta-lactamase L1 (mbetal 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 k(cat) (96 s(-1)) and K(m) (224 microM) 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 (1)H 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. (1)H NMR spectra demonstrate that Ni(II) binds in the Zn(2) 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 Zn(2) site modulates substrate preference in mbetal L1. These studies demonstrate that the Zn(2) 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 beta-lactam-containing antibiotics.

Differential binding of Co(II) and Zn(II) to metallo-beta-lactamase Bla2 from Bacillus anthracis.

In an effort to probe the structure, mechanism, and biochemical properties of metallo-beta-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, (1)H 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 targeting RNA with drug-like small molecules.

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.

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 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.