Hidden specificity in an apparently nonspecific RNA-binding protein

Massively parallel dissection of RNA in RNA-protein interactions in vivo

Many of the biological functions performed by RNA are mediated by RNA-binding proteins (RBPs), and understanding the molecular basis of these interactions is fundamental to biology. Here, we present massively parallel RNA assay combined with immunoprecipitation (MPRNA-IP) for in vivo high-throughput dissection of RNA-protein interactions and describe statistical models for identifying RNA domains and parsing the structural contributions of RNA. By using custom pools of tens of thousands of RNA sequences containing systematically designed truncations and mutations, MPRNA-IP is able to identify RNA domains, sequences, and secondary structures necessary and sufficient for protein binding in a single experiment. We show that this approach is successful for multiple RNAs of interest, including the long noncoding RNA NORAD, bacteriophage MS2 RNA, and human telomerase RNA, and we use it to interrogate the hitherto unknown sequence or structural RNA-binding preferences of the DNA-looping factor CTCF. By integrating systematic mutation analysis with crosslinking immunoprecipitation, MPRNA-IP provides a novel high-throughput way to elucidate RNA-based mechanisms behind RNA-protein interactions in vivo.

The discovery of a catalytic RNA within RNase P and its legacy

Sidney Altman's discovery of the processing of one RNA by another RNA that acts like an enzyme was revolutionary in biology and the basis for his sharing the 1989 Nobel Prize in Chemistry with Thomas Cech. These breakthrough findings support the key role of RNA in molecular evolution, where replicating RNAs (and similar chemical derivatives) either with or without peptides functioned in protocells during the early stages of life on Earth, an era referred to as the RNA world. Here, we cover the historical background highlighting the work of Altman and his colleagues and the subsequent efforts of other researchers to understand the biological function of RNase P and its catalytic RNA subunit and to employ it as a tool to downregulate gene expression. We primarily discuss bacterial RNase P-related studies but acknowledge that many groups have significantly contributed to our understanding of archaeal and eukaryotic RNase P, as reviewed in this special issue and elsewhere.

Role of mRNA-binding proteins in retinal neovascularization

Retinal neovascularization (RNV) is a pathological process that primarily occurs in diabetic retinopathy, retinopathy of prematurity, and retinal vein occlusion. It is a common yet debilitating clinical condition that culminates in blindness. Urgent efforts are required to explore more efficient and less limiting therapeutic strategies. Key RNA-binding proteins (RBPs), crucial for post-transcriptional regulation of gene expression by binding to RNAs, are closely correlated with RNV development. RBP-RNA interactions are altered during RNV. Here, we briefly review the characteristics and functions of RBPs, and the mechanism of RNV. Then, we present insights into the role of the regulatory network of RBPs in RNV. HuR, eIF4E, LIN28B, SRSF1, METTL3, YTHDF1, Gal-1, HIWI1, and ZFR accelerate RNV progression, whereas YTHDF2 and hnRNPA2B1 hinder it. The mechanisms elucidated in this review provide a reference to guide the design of therapeutic strategies to reverse abnormal processes.

The role of centromeric repeats and transcripts in kinetochore assembly and function

Centromeres are the chromosomal domains, where the kinetochore protein complex is formed, mediating proper segregation of chromosomes during cell division. Although the function of centromeres has remained conserved during evolution, centromeric DNA is highly variable, even in closely related species. In addition, the composition of the kinetochore complexes varies among organisms. Therefore, it is assumed that the centromeric position is determined epigenetically, and the centromeric histone H3 (CENH3) serves as an epigenetic marker. The loading of CENH3 onto centromeres depends on centromere-licensing factors, chaperones, and transcription of centromeric repeats. Several proteins that regulate CENH3 loading and kinetochore assembly interact with the centromeric transcripts and DNA in a sequence-independent manner. However, the functional aspects of these interactions are not fully understood. This review discusses the variability of centromeric sequences in different organisms and the regulation of their transcription through the RNA Pol II and RNAi machinery. The data suggest that the interaction of proteins involved in CENH3 loading and kinetochore assembly with centromeric DNA and transcripts plays a role in centromere, and possibly neocentromere, formation in a sequence-independent manner.

The specificity landscape of bacterial ribonuclease P

Developing quantitative models of substrate specificity for RNA processing enzymes is a key step toward understanding their biology and guiding applications in biotechnology and biomedicine. Optimally, models to predict relative rate constants for alternative substrates should integrate an understanding of structures of the enzyme bound to "fast" and "slow" substrates, large datasets of rate constants for alternative substrates, and transcriptomic data identifying in vivo processing sites. Such data are either available or emerging for bacterial ribonucleoprotein RNase P a widespread and essential tRNA 5' processing endonuclease, thus making it a valuable model system for investigating principles of biological specificity. Indeed, the well-established structure and kinetics of bacterial RNase P enabled the development of high throughput measurements of rate constants for tRNA variants and provided the necessary framework for quantitative specificity modeling. Several studies document the importance of conformational changes in the precursor tRNA substrate as well as the RNA and protein subunits of bacterial RNase P during binding, although the functional roles and dynamics are still being resolved. Recently, results from cryo-EM studies of E. coli RNase P with alternative precursor tRNAs are revealing prospective mechanistic relationships between conformational changes and substrate specificity. Yet, extensive uncharted territory remains, including leveraging these advances for drug discovery, achieving a complete accounting of RNase P substrates, and understanding how the cellular context contributes to RNA processing specificity in vivo.

The structural basis of tRNA recognition by arginyl-tRNA-protein transferase

Arginyl-tRNA-protein transferase 1 (ATE1) is a master regulator of protein homeostasis, stress response, cytoskeleton maintenance, and cell migration. The diverse functions of ATE1 arise from its unique enzymatic activity to covalently attach an arginine onto its protein substrates in a tRNA-dependent manner. However, how ATE1 (and other aminoacyl-tRNA transferases) hijacks tRNA from the highly efficient ribosomal protein synthesis pathways and catalyzes the arginylation reaction remains a mystery. Here, we describe the three-dimensional structures of Saccharomyces cerevisiae ATE1 with and without its tRNA cofactor. Importantly, the putative substrate binding domain of ATE1 adopts a previously uncharacterized fold that contains an atypical zinc-binding site critical for ATE1 stability and function. The unique recognition of tRNA^Arg by ATE1 is coordinated through interactions with the major groove of the acceptor arm of tRNA. Binding of tRNA induces conformational changes in ATE1 that helps explain the mechanism of substrate arginylation.

RNA-binding proteins in cancer drug discovery

RNA-binding proteins (RBPs) are crucial players in tumorigenesis and, hence, promising targets in cancer drug discovery. However, they are largely regarded as 'undruggable', because of the often noncatalytic and complex interactions between protein and RNA, which limit the discovery of specific inhibitors. Nonetheless, over the past 10 years, drug discovery efforts have uncovered RBP inhibitors with clinical relevance, highlighting the disruption of RNA-protein networks as a promising avenue for cancer therapeutics. In this review, we discuss the role of structurally distinct RBPs in cancer, and the mechanisms of RBP-directed small-molecule inhibitors (SMOIs) focusing on drug-protein interactions, binding surfaces, potency, and translational potential. Additionally, we underline the limitations of RBP-targeting drug discovery assays and comment on future trends in the field.

Two distinct binding modes provide the RNA-binding protein RbFox with extraordinary sequence specificity

Specificity of RNA-binding proteins for target sequences varies considerably. Yet, it is not understood how certain few proteins achieve markedly higher sequence specificity than most others. Here we show that the RNA Recognition Motif of RbFox accomplishes extraordinary sequence specificity by employing functionally and structurally distinct binding modes. Affinity measurements of RbFox for all binding site variants reveal the existence of two distinct binding modes. The first exclusively accommodates cognate and closely related RNAs with high affinity. The second mode accommodates all other RNAs with reduced affinity by imposing large thermodynamic penalties on non-cognate sequences. NMR studies indicate marked structural differences between the two binding modes, including large conformational rearrangements distant from the RNA-binding site. Distinct binding modes by a single RNA-binding module explain extraordinary sequence selectivity and reveal an unknown layer of functional diversity, cross talk and regulation in RNA-protein interactions.

Analysis of the Sequence Preference of Saporin by Deep Sequencing

Ribosome-inactivating proteins (RIPs) are RNA:adenosine glycosidases that inactivate eukaryotic ribosomes by depurinating the sarcin-ricin loop (SRL) in 28S rRNA. The GAGA sequence at the top of the SRL or at the top of a hairpin loop is assumed to be their target motif. Saporin is a RIP widely used to develop immunotoxins for research and medical applications, but its sequence specificity has not been investigated. Here, we combine the conventional aniline cleavage assay for depurinated nucleic acids with high-throughput sequencing to study sequence-specific depurination of oligonucleotides caused by saporin. Our data reveal the sequence preference of saporin for different substrates and show that the GAGA motif is not efficiently targeted by this protein, neither in RNA nor in DNA. Instead, a preference of saporin for certain hairpin DNAs was observed. The observed sequence-specific activity of saporin may be relevant to antiviral or apoptosis-inducing effects of RIPs. The developed method could also be useful for studying the sequence specificity of depurination by other RIPs or enzymes.

Structural and mechanistic basis for recognition of alternative tRNA precursor substrates by bacterial ribonuclease P

Binding of precursor tRNAs (ptRNAs) by bacterial ribonuclease P (RNase P) involves an encounter complex (ES) that isomerizes to a catalytic conformation (ES*). However, the structures of intermediates and the conformational changes that occur during binding are poorly understood. Here, we show that pairing between the 5′ leader and 3′RCCA extending the acceptor stem of ptRNA inhibits ES* formation. Cryo-electron microscopy single particle analysis reveals a dynamic enzyme that becomes ordered upon formation of ES* in which extended acceptor stem pairing is unwound. Comparisons of structures with alternative ptRNAs reveals that once unwinding is completed RNase P primarily uses stacking interactions and shape complementarity to accommodate alternative sequences at its cleavage site. Our study reveals active site interactions and conformational changes that drive molecular recognition by RNase P and lays the foundation for understanding how binding interactions are linked to helix unwinding and catalysis.

Ribonuclease P efficiently processes all tRNA precursors despite sequence variation at the site of cleavage. Here, authors use high-throughput enzymology and cryoEM to reveal conformational changes that drive recognition by bacterial RNase P.

Dynamic RNA fitness landscapes of a group I ribozyme during changes to the experimental environment

Fitness landscapes of protein and RNA molecules can be studied experimentally using high-throughput techniques to measure the functional effects of numerous combinations of mutations. The rugged topography of these molecular fitness landscapes is important for understanding and predicting natural and experimental evolution. Mutational effects are also dependent upon environmental conditions, but the effects of environmental changes on fitness landscapes remains poorly understood. Here, we investigate the changes to the fitness landscape of a catalytic RNA molecule while changing a single environmental variable that is critical for RNA structure and function. Using high-throughput sequencing of in vitro selections, we mapped a fitness landscape of the Azoarcus group I ribozyme under eight different concentrations of magnesium ions (1–48 mM MgCl₂). The data revealed the magnesium dependence of 16,384 mutational neighbors, and from this, we investigated the magnesium induced changes to the topography of the fitness landscape. The results showed that increasing magnesium concentration improved the relative fitness of sequences at higher mutational distances while also reducing the ruggedness of the mutational trajectories on the landscape. As a result, as magnesium concentration was increased, simulated populations evolved toward higher fitness faster. Curve-fitting of the magnesium dependence of individual ribozymes demonstrated that deep sequencing of in vitro reactions can be used to evaluate the structural stability of thousands of sequences in parallel. Overall, the results highlight how environmental changes that stabilize structures can also alter the ruggedness of fitness landscapes and alter evolutionary processes.

Element coding based accurate evaluation of CRISPR/Cas9 initial cleavage

As a powerful gene editing tool, the kinetic mechanism of CRISPR/Cas9 has been the focus for its further application. Initial cleavage events as the first domino followed by nuclease end trimming significantly affect the final on-target rate. Here we propose EC-CRISPR, element coding CRISPR, an accurate evaluation platform for initial cleavage that directly characterizes the cleavage efficiency and breaking sites. We benchmarked the influence of 19 single mismatch and 3 multiple mismatch positions of DNA-sgRNA on initial cleavage, as well as various reaction conditions. Results from EC-CRISPR demonstrate that the PAM-distal single mismatch is relatively acceptable compared to the proximal one. And multiple mismatches will not only affect the cleavage efficiency, but also generate more non-site #3 cleavage. Through in-depth research of kinetic behavior, we uncovered an abnormally higher non-#3 proportion at the initial stage of cleavage by using EC-CRISPR. Together, our results provided insights into cleavage efficiency and breaking sites, demonstrating that EC-CRISPR as a novel quantitative platform for initial cleavage enables accurate comparison of efficiencies and specificities among multiple CRISPR/Cas enzymes.

Initial cleavage events as the first domino of CRISPR/Cas9 kinetic behaviors. To accurately evaluate the initial cleavage of Cas9, element coding CRISPR platform-enabled direct characterization of the cleavage efficiency and cleavage sites was proposed.

The many faces of RNA-based RNase P, an RNA-world relic

RNase P is an essential enzyme that catalyzes removal of the 5' leader from precursor transfer RNAs. The ribonucleoprotein (RNP) form of RNase P is present in all domains of life and comprises a single catalytic RNA (ribozyme) and a variable number of protein cofactors. Recent cryo-electron microscopy structures of representative archaeal and eukaryotic (nuclear) RNase P holoenzymes bound to tRNA substrate/product provide high-resolution detail on subunit organization, topology, and substrate recognition in these large, multisubunit catalytic RNPs. These structures point to the challenges in understanding how proteins modulate the RNA functional repertoire and how the structure of an ancient RNA-based catalyst was reshaped during evolution by new macromolecular associations that were likely necessitated by functional/regulatory coupling.

A prometastatic splicing program regulated by SNRPA1 interactions with structured RNA elements

Aberrant alternative splicing is a hallmark of cancer, yet the underlying regulatory programs that control this process remain largely unknown. Here, we report a systematic effort to decipher the RNA structural code that shapes pathological splicing during breast cancer metastasis. We discovered a previously unknown structural splicing enhancer that is enriched near cassette exons with increased inclusion in highly metastatic cells. We show that the spliceosomal protein small nuclear ribonucleoprotein polypeptide A' (SNRPA1) interacts with these enhancers to promote cassette exon inclusion. This interaction enhances metastatic lung colonization and cancer cell invasion, in part through SNRPA1-mediated regulation of PLEC alternative splicing, which can be counteracted by splicing modulating morpholinos. Our findings establish a noncanonical regulatory role for SNRPA1 as a prometastatic splicing enhancer in breast cancer.

Protein-DNA/RNA Interactions: An Overview of Investigation Methods in the -Omics Era

The fields of application of functional proteomics are not limited to the study of protein–protein interactions; they also extend to those involving protein complexes that bind DNA or RNA. These interactions affect fundamental processes such as replication, transcription, and repair in the case of DNA, as well as transport, translation, splicing, and silencing in the case of RNA. Analytical or preparative experimental approaches, both in vivo and in vitro, have been developed to isolate and identify DNA/RNA binding proteins by exploiting the advantage of the affinity shown by these proteins toward a specific oligonucleotide sequence. The present review proposes an overview of the approaches most commonly employed in proteomics applications for the identification of nucleic acid-binding proteins, such as affinity purification (AP) protocols, EMSA, chromatin purification methods, and CRISPR-based chromatin affinity purification, which are generally associated with mass spectrometry methodologies for the unbiased protein identification.

Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks

Deep neural networks have demonstrated improved performance at predicting the sequence specificities of DNA- and RNA-binding proteins compared to previous methods that rely on k-mers and position weight matrices. To gain insights into why a DNN makes a given prediction, model interpretability methods, such as attribution methods, can be employed to identify motif-like representations along a given sequence. Because explanations are given on an individual sequence basis and can vary substantially across sequences, deducing generalizable trends across the dataset and quantifying their effect size remains a challenge. Here we introduce global importance analysis (GIA), a model interpretability method that quantifies the population-level effect size that putative patterns have on model predictions. GIA provides an avenue to quantitatively test hypotheses of putative patterns and their interactions with other patterns, as well as map out specific functions the network has learned. As a case study, we demonstrate the utility of GIA on the computational task of predicting RNA-protein interactions from sequence. We first introduce a convolutional network, we call ResidualBind, and benchmark its performance against previous methods on RNAcompete data. Using GIA, we then demonstrate that in addition to sequence motifs, ResidualBind learns a model that considers the number of motifs, their spacing, and sequence context, such as RNA secondary structure and GC-bias.

Massively parallel kinetic profiling of natural and engineered CRISPR nucleases

Engineered SpCas9s and AsCas12a cleave fewer off-target genomic sites than wild-type (wt) Cas9. However, understanding their fidelity, mechanisms and cleavage outcomes requires systematic profiling across mispaired target DNAs. Here we describe NucleaSeq-nuclease digestion and deep sequencing-a massively parallel platform that measures the cleavage kinetics and time-resolved cleavage products for over 10,000 targets containing mismatches, insertions and deletions relative to the guide RNA. Combining cleavage rates and binding specificities on the same target libraries, we benchmarked five SpCas9 variants and AsCas12a. A biophysical model built from these data sets revealed mechanistic insights into off-target cleavage. Engineered Cas9s, especially Cas9-HF1, dramatically increased cleavage specificity but not binding specificity compared to wtCas9. Surprisingly, AsCas12a cleavage specificity differed little from that of wtCas9. Initial DNA cleavage sites and end trimming varied by nuclease, guide RNA and the positions of mispaired nucleotides. More broadly, NucleaSeq enables rapid, quantitative and systematic comparisons of specificity and cleavage outcomes across engineered and natural nucleases.

Heterogeneous Dynamics of Protein-RNA Interactions across Transcriptome-Derived Messenger RNA Populations

Dynamic RNA-protein interactions underpin numerous molecular control mechanisms in biology. However, relatively little is known about the kinetic landscape of protein interactions with full-length RNAs. The extent to which interaction kinetics vary for the same RNA element across the transcriptome and the molecular determinants of variability therefore remain poorly defined. Moreover, it is unclear how one protein-RNA interaction might be transduced by RNA to kinetically impact a second. We report a parallelized, real-time single-molecule fluorescence assay for protein interaction kinetics on eukaryotic mRNA populations obtained from cells. We observed ∼100-fold heterogeneity for interactions of the translation initiation factor eIF4E with the universal mRNA 5' cap structure, dominated by steric effects on barrier-height variability for association. We also found that an RNA helicase, eIF4A, independently accelerated eIF4E-cap association. These data support a kinetic mechanism for how mRNA can determine the sensitivity of its translation to reduction in cellular eIF4E concentrations. They also support the view that global RNA structure significantly modulates protein-RNA interaction dynamics and can facilitate real-time communication between protein interactions at distinct sites.

The Thermus thermophilus DEAD-box protein Hera is a general RNA binding protein and plays a key role in tRNA metabolism

RNA helicases catalyze the ATP-dependent destabilization of RNA duplexes. DEAD-box helicases share a helicase core that mediates ATP binding and hydrolysis, RNA binding and unwinding. Most members of this family contain domains flanking the core that can confer RNA substrate specificity and guide the helicase to a specific RNA. However, the in vivo RNA substrates of most helicases are currently not defined. The DEAD-box helicase Hera from Thermus thermophilus contains a helicase core, followed by a dimerization domain and an RNA binding domain that folds into an RNA recognition motif (RRM). The RRM mediates high affinity binding to an RNA hairpin, and an adjacent duplex is then unwound by the helicase core. Hera is a cold-shock protein, and has been suggested to act as an RNA chaperone under cold-shock conditions. Using crosslinking immunoprecipitation of Hera/RNA complexes and sequencing, we show that Hera binds to a large fraction of T. thermophilus RNAs under normal-growth and cold-shock conditions without a strong sequence preference, in agreement with a structure-specific recognition of RNAs and a general function in RNA metabolism. Under cold-shock conditions, Hera is recruited to RNAs with high propensities to form stable secondary structures. We show that selected RNAs identified, including a set of tRNAs, bind to Hera in vitro, and activate the Hera helicase core. Gene ontology analysis reveals an enrichment of genes related to translation, including mRNAs of ribosomal proteins, tRNAs, tRNA ligases, and tRNA-modifying enzymes, consistent with a key role of Hera in ribosome and tRNA metabolism.

Measurements drive progress in directed evolution for precise engineering of biological systems

Precise engineering of biological systems requires quantitative, high-throughput measurements, exemplified by progress in directed evolution. New approaches allow high-throughput measurements of phenotypes and their corresponding genotypes. When integrated into directed evolution, these quantitative approaches enable the precise engineering of biological function. At the same time, the increasingly routine availability of large, high-quality data sets supports the integration of machine learning with directed evolution. Together, these advances herald striking capabilities for engineering biology.

High throughput approaches to study RNA-protein interactions in vitro

To understand the regulation of gene expression it is critical to determine how proteins interact with and discriminate between different RNAs. In this review, we discuss experimental techniques that utilize high throughput approaches to characterize the interactions of proteins with large numbers of RNAs in vitro. We describe the underlying principles for the main methods, briefly discuss their scope and limitations, and outline how insight from the techniques contributes to our understanding of specificity for RNA-protein interactions.

Approaches for measuring the dynamics of RNA-protein interactions

RNA-protein interactions are pivotal for the regulation of gene expression from bacteria to human. RNA-protein interactions are dynamic; they change over biologically relevant timescales. Understanding the regulation of gene expression at the RNA level therefore requires knowledge of the dynamics of RNA-protein interactions. Here, we discuss the main experimental approaches to measure dynamic aspects of RNA-protein interactions. We cover techniques that assess dynamics of cellular RNA-protein interactions that accompany biological processes over timescales of hours or longer and techniques measuring the kinetic dynamics of RNA-protein interactions in vitro. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes RNA Evolution and Genomics > Ribonomics.

Substrate selectivity by the exonuclease Rrp6p

The exoribonuclease Rrp6p is critical for RNA decay in the nucleus. While Rrp6p acts on a large range of diverse substrates, it does not indiscriminately degrade all RNAs. How Rrp6p accomplishes this task is not understood. Here, we measure Rrp6p-RNA binding and degradation kinetics in vitro at single-nucleotide resolution and find an intrinsic substrate selectivity that enables Rrp6p to discriminate against specific RNAs. RNA length and the four 3'-terminal nucleotides contribute most to substrate selectivity and collectively enable Rrp6p to discriminate between different RNAs by several orders of magnitude. The most pronounced discrimination is seen against RNAs ending with CCA-3'. These RNAs correspond to 3' termini of uncharged tRNAs, which are not targeted by Rrp6p in cells. The data show that in contrast to many other proteins that use substrate selectivity to preferentially interact with specific RNAs, Rrp6p utilizes its selectivity to discriminate against specific RNAs. This ability allows Rrp6p to target diverse substrates while avoiding a subset of RNAs.

Reengineering a tRNA Methyltransferase To Covalently Capture New RNA Substrates

Covalent RNA modifications can alter the function and metabolism of RNA transcripts. Altering the RNA substrate specificities of the enzymes that install these modifications can provide powerful tools to study and manipulate RNA. To develop new tools and probe the plasticity of the substrate specificity of one of these enzymes, we examined the engineerability of the uridine-54 tRNA methyltransferase, TrmA. Starting from a mutant that remains covalently bound to its substrate RNA (E358Q, TrmA*), we were able to use both rational design and a high-throughput sequencing assay to examine the RNA substrates of TrmA*. Although rational engineering substantially changed TrmA* specificity, the rationally designed substrate mutants we developed still retained activity with the wild-type protein. Using high-throughput substrate screening of additional TrmA* mutants, we identified a triple mutant of the substrate RNA (C56A;A58G;C60U) that is efficiently trapped by a TrmA* double mutant (E49R;R51E) but not by the wild-type TrmA*. This work establishes a foundation for using protein engineering to reconfigure substrate specificities of RNA-modifying enzymes and covalently trap RNAs with engineered proteins.

Hidden sequence specificity in loading of single-stranded RNAs onto Drosophila Argonautes

Argonaute proteins play important roles in gene regulation with small RNAs (sRNAs) serving as guides to targets. Argonautes are believed to bind sRNAs in a sequence non-specific manner. However, we recently discovered that Argonautes selectively load endogenous single-stranded (ss) RNAs, suggesting that Argonaute loading may conform to sequence specificity. To identify sequences preferred for Argonaute loading, we have developed HIgh-throughput Sequencing mediated Specificity Analysis (HISSA). HISSA allows massively parallel analysis of RNA binding efficiency by using randomized oligos in in vitro binding assays and quantifying RNAs by deep-sequencing. We chose Drosophila as a model system to take advantage of the presence of two biochemically distinct Argonautes, AGO1 and AGO2. Our results revealed AGO2 loading to be strongly favored by G-rich sequences. In contrast, AGO1 showed an enrichment of the ‘GAC’ motif in loaded species. Reanalysis of published sRNA sequencing data from fly tissues detected enrichment of the GAC motif in ssRNA-derived small RNAs in the immunopurified AGO1-complex under certain conditions, suggesting that the sequence preference of AGO1-loading may influence the repertoire of AGO1-bound endogenous sRNAs. Finally, we showed that human Ago2 also exhibited selectivity in loading ssRNAs in cell lysates. These findings may have implications for therapeutic ssRNA-mediated gene silencing.

Recent insights into the genotype-phenotype relationship from massively parallel genetic assays

With the molecular revolution in Biology, a mechanistic understanding of the genotype-phenotype relationship became possible. Recently, advances in DNA synthesis and sequencing have enabled the development of deep mutational scanning assays, capable of scoring comprehensive libraries of genotypes for fitness and a variety of phenotypes in massively parallel fashion. The resulting empirical genotype-fitness maps pave the way to predictive models, potentially accelerating our ability to anticipate the behaviour of pathogen and cancerous cell populations from sequencing data. Besides from cellular fitness, phenotypes of direct application in industry (e.g. enzyme activity) and medicine (e.g. antibody binding) can be quantified and even selected directly by these assays. This review discusses the technological basis of and recent developments in massively parallel genetics, along with the trends it is uncovering in the genotype-phenotype relationship (distribution of mutation effects, epistasis), their possible mechanistic bases and future directions for advancing towards the goal of predictive genetics.

RNA Binding: Getting Specific about Specificity

Sequence-specific nucleic acid binding proteins do not recognize one sequence out of all possibilities; rather, they bind to many sequences with a range of affinities. In this issue of Cell Chemical Biology, Lin et al. (2016), describe the entire landscape of affinities between different RNA molecules and an RNA-binding protein, thus providing a comprehensive description of the factors affecting specificity.

Combinatorial recognition of clustered RNA elements by the multidomain RNA-binding protein IMP3

How multidomain RNA-binding proteins recognize their specific target sequences, based on a combinatorial code, represents a fundamental unsolved question and has not been studied systematically so far. Here we focus on a prototypical multidomain RNA-binding protein, IMP3 (also called IGF2BP3), which contains six RNA-binding domains (RBDs): four KH and two RRM domains. We establish an integrative systematic strategy, combining single-domain-resolved SELEX-seq, motif-spacing analyses, in vivo iCLIP, functional validation assays, and structural biology. This approach identifies the RNA-binding specificity and RNP topology of IMP3, involving all six RBDs and a cluster of up to five distinct and appropriately spaced CA-rich and GGC-core RNA elements, covering a >100 nucleotide-long target RNA region. Our generally applicable approach explains both specificity and flexibility of IMP3-RNA recognition, allows the prediction of IMP3 targets, and provides a paradigm for the function of multivalent interactions with multidomain RNA-binding proteins in gene regulation.

Massively Parallel Assays and Quantitative Sequence-Function Relationships

Over the last decade, a rich variety of massively parallel assays have revolutionized our understanding of how biological sequences encode quantitative molecular phenotypes. These assays include deep mutational scanning, high-throughput SELEX, and massively parallel reporter assays. Here, we review these experimental methods and how the data they produce can be used to quantitatively model sequence-function relationships. In doing so, we touch on a diverse range of topics, including the identification of clinically relevant genomic variants, the modeling of transcription factor binding to DNA, the functional and evolutionary landscapes of proteins, and cis-regulatory mechanisms in both transcription and mRNA splicing. We further describe a unified conceptual framework and a core set of mathematical modeling strategies that studies in these diverse areas can make use of. Finally, we highlight key aspects of experimental design and mathematical modeling that are important for the results of such studies to be interpretable and reproducible.

Mapping a Systematic Ribozyme Fitness Landscape Reveals a Frustrated Evolutionary Network for Self-Aminoacylating RNA

Molecular evolution can be conceptualized as a walk over a “fitness landscape”, or the function of fitness (e.g., catalytic activity) over the space of all possible sequences. Understanding evolution requires knowing the structure of the fitness landscape and identifying the viable evolutionary pathways through the landscape. However, the fitness landscape for any catalytic biomolecule is largely unknown. The evolution of catalytic RNA is of special interest because RNA is believed to have been foundational to early life. In particular, an essential activity leading to the genetic code would be the reaction of ribozymes with activated amino acids, such as 5(4H)-oxazolones, to form aminoacyl-RNA. Here we combine in vitro selection with a massively parallel kinetic assay to map a fitness landscape for self-aminoacylating RNA, with nearly complete coverage of sequence space in a central 21-nucleotide region. The method (SCAPE: sequencing to measure catalytic activity paired with in vitro evolution) shows that the landscape contains three major ribozyme families (landscape peaks). An analysis of evolutionary pathways shows that, while local optimization within a ribozyme family would be possible, optimization of activity over the entire landscape would be frustrated by large valleys of low activity. The sequence motifs associated with each peak represent different solutions to the problem of catalysis, so the inability to traverse the landscape globally corresponds to an inability to restructure the ribozyme without losing activity. The frustrated nature of the evolutionary network suggests that chance emergence of a ribozyme motif would be more important than optimization by natural selection.

The AGO proteins: an overview

Small RNAs govern almost every biological process in eukaryotes associating with the Argonaute (AGO) proteins to form the RNA-induced silencing complex (mRISC). AGO proteins constitute the core of RISCs with different members having variety of protein-binding partners and biochemical properties. This review focuses on the AGO subfamily of the AGOs that are ubiquitously expressed and are associated with small RNAs. The structure, function and role of the AGO proteins in the cell is discussed in detail.

Partner-specific prediction of RNA-binding residues in proteins: A critical assessment

RNA-protein interactions play essential roles in regulating gene expression. While some RNA-protein interactions are "specific", that is, the RNA-binding proteins preferentially bind to particular RNA sequence or structural motifs, others are "non-RNA specific." Deciphering the protein-RNA recognition code is essential for comprehending the functional implications of these interactions and for developing new therapies for many diseases. Because of the high cost of experimental determination of protein-RNA interfaces, there is a need for computational methods to identify RNA-binding residues in proteins. While most of the existing computational methods for predicting RNA-binding residues in RNA-binding proteins are oblivious to the characteristics of the partner RNA, there is growing interest in methods for partner-specific prediction of RNA binding sites in proteins. In this work, we assess the performance of two recently published partner-specific protein-RNA interface prediction tools, PS-PRIP, and PRIdictor, along with our own new tools. Specifically, we introduce a novel metric, RNA-specificity metric (RSM), for quantifying the RNA-specificity of the RNA binding residues predicted by such tools. Our results show that the RNA-binding residues predicted by previously published methods are oblivious to the characteristics of the putative RNA binding partner. Moreover, when evaluated using partner-agnostic metrics, RNA partner-specific methods are outperformed by the state-of-the-art partner-agnostic methods. We conjecture that either (a) the protein-RNA complexes in PDB are not representative of the protein-RNA interactions in nature, or (b) the current methods for partner-specific prediction of RNA-binding residues in proteins fail to account for the differences in RNA partner-specific versus partner-agnostic protein-RNA interactions, or both.

Global pairwise RNA interaction landscapes reveal core features of protein recognition

RNA–protein interactions permeate biology. Transcription, translation, and splicing all hinge on the recognition of structured RNA elements by RNA-binding proteins. Models of RNA–protein interactions are generally limited to short linear motifs and structures because of the vast sequence sampling required to access longer elements. Here, we develop an integrated approach that calculates global pairwise interaction scores from in vitro selection and high-throughput sequencing. We examine four RNA-binding proteins of phage, viral, and human origin. Our approach reveals regulatory motifs, discriminates between regulated and non-regulated RNAs within their native genomic context, and correctly predicts the consequence of mutational events on binding activity. We design binding elements that improve binding activity in cells and infer mutational pathways that reveal permissive versus disruptive evolutionary trajectories between regulated motifs. These coupling landscapes are broadly applicable for the discovery and characterization of protein–RNA recognition at single nucleotide resolution.

RNA–protein interactions often depend on the recognition of extended RNA elements but the identification of these motifs is challenging. Here, the authors present a global integrated approach to analyze RNA–protein binding landscapes, mapping extended RNA interaction motifs for four RNA-binding proteins.

A General Small-Angle X-ray Scattering-Based Screening Protocol Validated for Protein-RNA Interactions

We present a screening protocol utilizing small-angle X-ray scattering (SAXS) to obtain structural information on biomolecular interactions independent of prior knowledge, so as to complement affinity-based screening and provide leads for further exploration. This protocol categorizes ligand titrations by computing pairwise agreement between curves, and separately estimates affinities by quantifying complex formation as a departure from the linear sum properties of solution SAXS. The protocol is validated by sparse sequence search around the native poly uridine RNA motifs of the two-RRM domain Sex-lethal protein (Sxl). The screening of 35 RNA motifs between 4 to 10 nucleotides reveals a strong variation of resulting complexes, revealed to be preference-switching between 1:1 and 2:2 binding stoichiometries upon addition of structural modeling. Validation of select sequences in isothermal calorimetry and NMR titration retrieves domain-specific roles and function of a guanine anchor. These findings reinforce the suitability of SAXS as a complement in lead identification.

NMR resonance assignments of RNase P protein from Thermotoga maritima

Ribonuclase P (RNase P) is an essential metallo-endonuclease that catalyzes 5' precursor-tRNA (ptRNA) processing and exists as an RNA-based enzyme in bacteria, archaea, and eukaryotes. In bacteria, a large catalytic RNA and a small protein component assemble to recognize and accurately cleave ptRNA and tRNA-like molecular scaffolds. Substrate recognition of ptRNA by bacterial RNase P requires RNA-RNA shape complementarity, intermolecular base pairing, and a dynamic protein-ptRNA binding interface. To gain insight into the binding specificity and dynamics of the bacterial protein-ptRNA interface, we report the backbone and side chain 1H, 13C, and 15N resonance assignments of the hyperthermophilic Thermatoga maritima RNase P protein in solution at 318 K. Our data confirm the formation of a stable RNA recognition motif (RRM) with intrinsic heterogeneity at both the N- and C-terminus of the protein, consistent with available structural information. Comprehensive resonance assignments of the bacterial RNase P protein serve as an important first step in understanding how coupled RNA binding and protein-RNA conformational changes give rise to ribonucleoprotein function.

RNase H sequence preferences influence antisense oligonucleotide efficiency

RNase H cleaves RNA in RNA-DNA duplexes. It is present in all domains of life as well as in multiple viruses and is essential for mammalian development and for human immunodeficiency virus replication. Here, we developed a sequencing-based method to measure the cleavage of thousands of different RNA-DNA duplexes and thereby comprehensively characterized the sequence preferences of HIV-1, human and Escherichia coli RNase H enzymes. We find that the catalytic domains of E. coli and human RNase H have nearly identical sequence preferences, which correlate with the efficiency of RNase H-recruiting antisense oligonucleotides. The sequences preferred by HIV-1 RNase H are distributed in the HIV genome in a way suggesting selection for efficient RNA cleavage during replication. Our findings can be used to improve the design of RNase H-recruiting antisense oligonucleotides and show that sequence preferences of HIV-1 RNase H may have shaped evolution of the viral genome and contributed to the use of tRNA-Lys3 as primer during viral replication.

The contribution of the C5 protein subunit of Escherichia coli ribonuclease P to specificity for precursor tRNA is modulated by proximal 5' leader sequences

Recognition of RNA by RNA processing enzymes and RNA binding proteins often involves cooperation between multiple subunits. However, the interdependent contributions of RNA and protein subunits to molecular recognition by ribonucleoproteins are relatively unexplored. RNase P is an endonuclease that removes 5' leaders from precursor tRNAs and functions in bacteria as a dimer formed by a catalytic RNA subunit (P RNA) and a protein subunit (C5 in E. coli). The P RNA subunit contacts the tRNA body and proximal 5' leader sequences [N(-1) and N(-2)] while C5 binds distal 5' leader sequences [N(-3) to N(-6)]. To determine whether the contacts formed by P RNA and C5 contribute independently to specificity or exhibit cooperativity or anti-cooperativity, we compared the relative k_cat/K_m values for all possible combinations of the six proximal 5' leader nucleotides (n = 4096) for processing by the E. coli P RNA subunit alone and by the RNase P holoenzyme. We observed that while the P RNA subunit shows specificity for 5' leader nucleotides N(-2) and N(-1), the presence of the C5 protein reduces the contribution of P RNA to specificity, but changes specificity at N(-2) and N(-3). The results reveal that the contribution of C5 protein to RNase P processing is controlled by the identity of N(-2) in the pre-tRNA 5' leader. The data also clearly show that pairing of the 5' leader with the 3' ACCA of tRNA acts as an anti-determinant for RNase P cleavage. Comparative analysis of genomically encoded E. coli tRNAs reveals that both anti-determinants are subject to negative selection in vivo.

Mapping specificity landscapes of RNA-protein interactions by high throughput sequencing

To function in a biological setting, RNA binding proteins (RBPs) have to discriminate between alternative binding sites in RNAs. This discrimination can occur in the ground state of an RNA-protein binding reaction, in its transition state, or in both. The extent by which RBPs discriminate at these reaction states defines RBP specificity landscapes. Here, we describe the HiTS-Kin and HiTS-EQ techniques, which combine kinetic and equilibrium binding experiments with high throughput sequencing to quantitatively assess substrate discrimination for large numbers of substrate variants at ground and transition states of RNA-protein binding reactions. We discuss experimental design, practical considerations and data analysis and outline how a combination of HiTS-Kin and HiTS-EQ allows the mapping of RBP specificity landscapes.

Rules of RNA specificity of hnRNP A1 revealed by global and quantitative analysis of its affinity distribution

Heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) is a multipurpose RNA-binding protein (RBP) involved in normal and pathological RNA metabolism. Transcriptome-wide mapping and in vitro evolution identify consensus hnRNP A1 binding motifs; however, such data do not reveal how surrounding RNA sequence and structural context modulate affinity. We determined the affinity of hnRNP A1 for all possible sequence variants (n = 16,384) of the HIV exon splicing silencer 3 (ESS3) 7-nt apical loop. Analysis of the affinity distribution identifies the optimal motif 5'-YAG-3' and shows how its copy number, position in the loop, and loop structure modulate affinity. For a subset of ESS3 variants, we show that specificity is determined by association rate constants and that variants lacking the minimal sequence motif bind competitively with consensus RNA. Thus, the results reveal general rules of specificity of hnRNP A1 and provide a quantitative framework for understanding how it discriminates between alternative competing RNA ligands in vivo.

Structural Roles of Noncoding RNAs in the Heart of Enzymatic Complexes

Over billions of years of evolution, nature has embraced proteins as the major workhorse molecules of the cell. However, nearly every aspect of metabolism is dependent upon how structured RNAs interact with proteins, ligands, and other nucleic acids. Key processes, including telomere maintenance, RNA processing, and protein synthesis, require large RNAs that assemble into elaborate three-dimensional shapes. These RNAs can (i) act as flexible scaffolds for protein subunits, (ii) participate directly in substrate recognition, and (iii) serve as catalytic components. Here, we juxtapose the near atomic level interactions of three ribonucleoprotein complexes: ribonuclease P (involved in 5' pre-tRNA processing), the spliceosome (responsible for pre-mRNA splicing), and telomerase (an RNA-directed DNA polymerase that extends the ends of chromosomes). The focus of this perspective is profiling the structural and dynamic roles of RNAs at the core of these enzymes, highlighting how large RNAs contribute to molecular recognition and catalysis.

Beyond the Hypercube: Evolutionary Accessibility of Fitness Landscapes with Realistic Mutational Networks

Evolutionary pathways describe trajectories of biological evolution in the space of different variants of organisms (genotypes). The probability of existence and the number of evolutionary pathways that lead from a given genotype to a better-adapted genotype are important measures of accessibility of local fitness optima and the reproducibility of evolution. Both quantities have been studied in simple mathematical models where genotypes are represented as binary sequences of two types of basic units, and the network of permitted mutations between the genotypes is a hypercube graph. However, it is unclear how these results translate to the biologically relevant case in which genotypes are represented by sequences of more than two units, for example four nucleotides (DNA) or 20 amino acids (proteins), and the mutational graph is not the hypercube. Here we investigate accessibility of the best-adapted genotype in the general case of K > 2 units. Using computer generated and experimental fitness landscapes we show that accessibility of the global fitness maximum increases with K and can be much higher than for binary sequences. The increase in accessibility comes from the increase in the number of indirect trajectories exploited by evolution for higher K. As one of the consequences, the fraction of genotypes that are accessible increases by three orders of magnitude when the number of units K increases from 2 to 16 for landscapes of size N ∼ 10⁶ genotypes. This suggests that evolution can follow many different trajectories on such landscapes and the reconstruction of evolutionary pathways from experimental data might be an extremely difficult task.

Biological evolution is driven by heritable, genetic alterations that affect the fitness of organisms. However, the pool of “fitter” variants (genotypes) is often restricted and it is not at all obvious how evolution finds its way from low-fitness to high-fitness genotypes in a complex, multidimensional “fitness landscapes” with many peaks (fit organisms) and valleys (unfit ones). To address this question we investigate how likely it is for biological evolution to find a way “uphill” from a lower-fitness organism to the best adapted organism. We discover that the accessibility of the fittest organism depends on the number of types of basic “units” used to encode genotypes. These units can be, for example, the four DNA nucleotides A,T,C,G, or the ∼20 amino acids used for synthesizing proteins, and the choice of the most appropriate unit is dictated by how the genotypes and the fitnesses are related—a relationship that researchers have begun to unveil only recently. We find that increasing the number of units strongly increases the probability that there will be at least one uphill path to the best-adapted genotype, and the number of evolutionary pathways leading to it. Our findings suggest that biological evolution can follow many more pathways than previously thought.

Many Activities, One Structure: Functional Plasticity of Ribozyme Folds

Catalytic RNAs, or ribozymes, are involved in a number of essential biological processes, such as replication of RNA genomes and mobile genetic elements, RNA splicing, translation, and RNA degradation. The function of ribozymes requires the formation of active sites decorated with RNA functional groups within defined three-dimensional (3D) structures. The genotype (sequence) of RNAs ultimately determines what 3D structures they adopt (as a function of their environmental conditions). These 3D structures, in turn, give rise to biochemical activity, which can further elaborate them by catalytic rearrangements or association with other molecules. The fitness landscape of a non-periodic linear polymer, such as RNA, relates its primary structure to a phenotype. Two major challenges in the analysis of ribozymes is to map all possible genotypes to their corresponding catalytic activity (that is, to determine their fitness landscape experimentally), and to understand whether their genotypes and three-dimensional structures can support multiple different catalytic functions. Recently, the combined results of experiments that employ in vitro evolution methods, high-throughput sequencing and crystallographic structure determination have hinted at answers to these two questions: while the fitness landscape of ribozymes is rugged, meaning that their catalytic activity cannot be optimized by a smooth trajectory in sequence space, once an RNA achieves a stable three-dimensional fold, it can be endowed with distinctly different biochemical activities through small changes in genotype. This functional plasticity of highly structured RNAs may be particularly advantageous for the adaptation of organisms to drastic changes in selective pressure, or for the development of new biotechnological tools.

Analysis of the RNA Binding Specificity Landscape of C5 Protein Reveals Structure and Sequence Preferences that Direct RNase P Specificity

RNA binding proteins (RBPs) are typically involved in non-equilibrium cellular processes, and specificity can arise from differences in ground state, transition state, or product states of the binding reactions for alternative RNAs. Here, we use high-throughput methods to measure and analyze the RNA association kinetics and equilibrium binding affinity for all possible sequence combinations in the precursor tRNA binding site of C5, the essential protein subunit of Escherichia coli RNase P. The results show that the RNA sequence specificity of C5 arises due to favorable RNA-protein interactions that stabilize the transition state for association and bound enzyme-substrate complex. Specificity is further impacted by unfavorable RNA structure involving the C5 binding site in the ground state. The results illustrate a comprehensive quantitative approach for analysis of RNA binding specificity, and show how both RNA structure and sequence preferences of an essential protein subunit direct the specificity of a ribonucleoprotein enzyme.

Determination of the Specificity Landscape for Ribonuclease P Processing of Precursor tRNA 5' Leader Sequences

Maturation of tRNA depends on a single endonuclease, ribonuclease P (RNase P), to remove highly variable 5' leader sequences from precursor tRNA transcripts. Here, we use high-throughput enzymology to report multiple-turnover and single-turnover kinetics for Escherichia coli RNase P processing of all possible 5' leader sequences, including nucleotides contacting both the RNA and protein subunits of RNase P. The results reveal that the identity of N(-2) and N(-3) relative to the cleavage site at N(1) primarily control alternative substrate selection and act at the level of association not the cleavage step. As a consequence, the specificity for N(-1), which contacts the active site and contributes to catalysis, is suppressed. This study demonstrates high-throughput RNA enzymology as a means to globally determine RNA specificity landscapes and reveals the mechanism of substrate discrimination by a widespread and essential RNA-processing enzyme.

Functional role of putative critical residues in Mycobacterium tuberculosis RNase P protein

RNase P is involved in processing the 5' end of pre-tRNA molecules. Bacterial RNase P contains a catalytic RNA subunit and a protein subunit. In this study, we have analyzed the residues in RNase P protein of M. tuberculosis that differ from the residues generally conserved in other bacterial RNase Ps. The residues investigated in the current study include the unique residues, Val27, Ala70, Arg72, Ala77, and Asp124, and also Phe23 and Arg93 which have been found to be important in the function of RNase P protein components of other bacteria. The selected residues were individually mutated either to those present in other bacterial RNase P protein components at respective positions or in some cases to alanine. The wild type and mutant M. tuberculosis RNase P proteins were expressed in E. coli, purified, used to reconstitute holoenzymes with wild type RNA component in vitro, and functionally characterized. The Phe23Ala and Arg93Ala mutants showed very poor catalytic activity when reconstituted with the RNA component. The catalytic activity of holoenzyme with Val27Phe, Ala70Lys, Arg72Leu and Arg72Ala was also significantly reduced, whereas with Ala77Phe and Asp124Ser the activity of holoenzyme was similar to that with the wild type protein. Although the mutants did not suffer from any binding defects, Val27Phe, Ala70Lys, Arg72Ala and Asp124Ser were less tolerant towards higher temperatures as compared to the wild type protein. The Km of Val27Phe, Ala70Lys, Arg72Ala and Ala77Phe were >2-fold higher than that of the wild type, indicating the substituted residues to be involved in substrate interaction. The study demonstrates that residues Phe23, Val27 and Ala70 are involved in substrate interaction, while Arg72 and Arg93 interact with other residues within the protein to provide it a functional conformation.

Determinants of affinity and specificity in RNA-binding proteins

Emerging data suggest that the mechanisms by which RNA-binding proteins (RBPs) interact with RNA and the rules governing specificity might be substantially more complex than those underlying their DNA-binding counterparts. Even our knowledge of what constitutes the RNA-bound proteome is contentious; recent studies suggest that 10-30% of RBPs contain no known RNA-binding domain. Adding to this situation is a growing disconnect between the avalanche of identified interactions between proteins and long noncoding RNAs and the absence of biophysical data on these interactions. RNA-protein interactions are also at the centre of what might emerge as one of the biggest shifts in thinking about cell and molecular biology this century, following from recent reports of ribonucleoprotein complexes that drive reversible membrane-free phase separation events within the cell. Unexpectedly, low-complexity motifs are important in the formation of these structures. Here we briefly survey recent advances in our understanding of the specificity of RBPs.

Optimization of high-throughput sequencing kinetics for determining enzymatic rate constants of thousands of RNA substrates

Quantification of the specificity of RNA binding proteins and RNA processing enzymes is essential to understanding their fundamental roles in biological processes. High-throughput sequencing kinetics (HTS-Kin) uses high-throughput sequencing and internal competition kinetics to simultaneously monitor the processing rate constants of thousands of substrates by RNA processing enzymes. This technique has provided unprecedented insight into the substrate specificity of the tRNA processing endonuclease ribonuclease P. Here, we investigated the accuracy and robustness of measurements associated with each step of the HTS-Kin procedure. We examine the effect of substrate concentration on the observed rate constant, determine the optimal kinetic parameters, and provide guidelines for reducing error in amplification of the substrate population. Importantly, we found that high-throughput sequencing and experimental reproducibility contribute to error, and these are the main sources of imprecision in the quantified results when otherwise optimized guidelines are followed.

The Diversity of Ribonuclease P: Protein and RNA Catalysts with Analogous Biological Functions

Ribonuclease P (RNase P) is an essential endonuclease responsible for catalyzing 5' end maturation in precursor transfer RNAs. Since its discovery in the 1970s, RNase P enzymes have been identified and studied throughout the three domains of life. Interestingly, RNase P is either RNA-based, with a catalytic RNA subunit, or a protein-only (PRORP) enzyme with differential evolutionary distribution. The available structural data, including the active site data, provides insight into catalysis and substrate recognition. The hydrolytic and kinetic mechanisms of the two forms of RNase P enzymes are similar, yet features unique to the RNA-based and PRORP enzymes are consistent with different evolutionary origins. The various RNase P enzymes, in addition to their primary role in tRNA 5' maturation, catalyze cleavage of a variety of alternative substrates, indicating a diversification of RNase P function in vivo. The review concludes with a discussion of recent advances and interesting research directions in the field.