RNA sequence to structure analysis from comprehensive pairwise mutagenesis of multiple self-cleaving ribozymes.

Self-cleaving ribozymes are RNA molecules that catalyze the cleavage of their own phosphodiester backbones. These ribozymes are found in all domains of life and are also a tool for biotechnical and synthetic biology applications. Self-cleaving ribozymes are also an important model of sequence-to-function relationships for RNA because their small size simplifies synthesis of genetic variants and self-cleaving activity is an accessible readout of the functional consequence of the mutation. Here, we used a high-throughput experimental approach to determine the relative activity for every possible single and double mutant of five self-cleaving ribozymes. From this data, we comprehensively identified non-additive effects between pairs of mutations (epistasis) for all five ribozymes. We analyzed how changes in activity and trends in epistasis map to the ribozyme structures. The variety of structures studied provided opportunities to observe several examples of common structural elements, and the data was collected under identical experimental conditions to enable direct comparison. Heatmap-based visualization of the data revealed patterns indicating structural features of the ribozymes including paired regions, unpaired loops, non-canonical structures, and tertiary structural contacts. The data also revealed signatures of functionally critical nucleotides involved in catalysis. The results demonstrate that the data sets provide structural information similar to chemical or enzymatic probing experiments, but with additional quantitative functional information. The large-scale data sets can be used for models predicting structure and function and for efforts to engineer self-cleaving ribozymes.

Predicting higher-order mutational effects in an RNA enzyme by machine learning of high-throughput experimental data.

Ribozymes are RNA molecules that catalyze biochemical reactions. Self-cleaving ribozymes are a common naturally occurring class of ribozymes that catalyze site-specific cleavage of their own phosphodiester backbone. In addition to their natural functions, self-cleaving ribozymes have been used to engineer control of gene expression because they can be designed to alter RNA processing and stability. However, the rational design of ribozyme activity remains challenging, and many ribozyme-based systems are engineered or improved by random mutagenesis and selection (in vitro evolution). Improving a ribozyme-based system often requires several mutations to achieve the desired function, but extensive pairwise and higher-order epistasis prevent a simple prediction of the effect of multiple mutations that is needed for rational design. Recently, high-throughput sequencing-based approaches have produced data sets on the effects of numerous mutations in different ribozymes (RNA fitness landscapes). Here we used such high-throughput experimental data from variants of the CPEB3 self-cleaving ribozyme to train a predictive model through machine learning approaches. We trained models using either a random forest or long short-term memory (LSTM) recurrent neural network approach. We found that models trained on a comprehensive set of pairwise mutant data could predict active sequences at higher mutational distances, but the correlation between predicted and experimentally observed self-cleavage activity decreased with increasing mutational distance. Adding sequences with increasingly higher numbers of mutations to the training data improved the correlation at increasing mutational distances. Systematically reducing the size of the training data set suggests that a wide distribution of ribozyme activity may be the key to accurate predictions. Because the model predictions are based only on sequence and activity data, the results demonstrate that this machine learning approach allows readily obtainable experimental data to be used for RNA design efforts even for RNA molecules with unknown structures. The accurate prediction of RNA functions will enable a more comprehensive understanding of RNA fitness landscapes for studying evolution and for guiding RNA-based engineering efforts.

Triple, Mutually Orthogonal Bioorthogonal Pairs through the Design of Electronically Activated Sulfamate-Containing Cycloalkynes.

Interest in mutually exclusive pairs of bioorthogonal labeling reagents continues to drive the design of new compounds that are capable of fast and predictable reactions. The ability to easily modify S-, N-, and O-containing cyclooctynes (SNO-OCTs) enables electronic tuning of various SNO-OCTs to influence their cycloaddition rates with Type I-III dipoles. As opposed to optimizations based on just one specific dipole class, the electrophilicity of the alkynes in SNO-OCTs can be manipulated to achieve divergent reactivities and furnish mutually orthogonal dual ligation systems. Significant reaction rate enhancements of a difluorinated SNO-OCT derivative, as compared to the parent scaffold, were noted, with the second-order rate constant in cycloadditions with diazoacetamides exceeding 5.13 M-1 s-1. Computational and experimental studies were employed to inform the design of triple ligation systems that encompass three orthogonal reactivities. Finally, polar SNO-OCTs are rapidly internalized by mammalian cells and remain functional in the cytosol for live-cell labeling, highlighting their potential for diverse in vitro and in vivo applications.

Silver-Catalyzed Enantioselective Propargylic C-H Bond Amination through Rational Ligand Design.

Asymmetric C-H amination via nitrene transfer is a powerful tool to prepare enantioenriched amine precursors from abundant C-H bonds. Herein, we report a regio- and enantioselective synthesis of γ-alkynyl γ-aminoalcohols via a silver-catalyzed propargylic C-H amination. The protocol was enabled by a new bis(oxazoline) (BOX) ligand designed via a rapid structure-activity relationship (SAR) analysis. The method utilizes accessible carbamate esters bearing γ-propargylic C-H bonds and furnishes versatile products in good yields and excellent enantioselectivity (90-99% ee). The putative Ag-nitrene is proposed to undergo enantiodetermining hydrogen-atom transfer (HAT) during the C-H amination event. Density functional theory calculations shed insight into the origin of enantioselectivity in the HAT step.

Solvent-free synthesis of soluble, near-IR absorbing titanyl phthalocyanine derivatives.

Solvent-free synthesis of a series of alkylthio-substituted titanyl phthalocyanine (TiOPc) derivatives starting from the corresponding phthalonitriles (Pn) is reported. This methodology eliminates the formation of the unmetalated phthalocyanine (H2Pc), a side product that makes purification difficult. The alkylthio groups on the reported derivatives enhance solubility in common organic solvents and shift the absorption to the near-IR region.