Interrogating RNA-small molecule interactions with structure probing and AI augmented-molecular simulations

While there is increasing interest in the study of RNA as a therapeutic target, efforts to understand RNA-ligand recognition at the molecular level lag far behind our understanding of protein-ligand recognition. This problem is complicated due to the more than ten orders of magnitude in timescales involved in RNA dynamics and ligand binding events, making it not straightforward to design experiments or simulations. Here we make use of artificial intelligence (AI)-augmented molecular dynamics simulations to directly observe ligand dissociation for cognate and synthetic ligands from a riboswitch system. The site-specific flexibility profiles from our simulations are in excellent agreement with in vitro measurements of flexibility using Selective 2' Hydroxyl Acylation analyzed by Primer Extension and Mutational Profiling (SHAPE-MaP). Our simulations reproduce known binding affinity profiles for the cognate and synthetic ligands, and pinpoint how both ligands make use of different aspects of riboswitch flexibility. On the basis of our dissociation trajectories, we also make and validate predictions of pairs of mutations for both the ligand systems that would show differing binding affinities. These mutations are distal to the binding site and could not have been predicted solely on the basis of structure. The methodology demonstrated here shows how molecular dynamics simulations with all-atom force-fields have now come of age in making predictions that complement existing experimental techniques and illuminate aspects of systems otherwise not trivial to understand.

Differential Analysis of RNA Structure Probing Experiments at Nucleotide Resolution: Uncovering Regulatory Functions of RNA Structure

RNAs perform their function by forming specific structures, which can change across cellular conditions. Structure probing experiments combined with next generation sequencing technology have enabled transcriptome-wide analysis of RNA secondary structure in various cellular conditions. Differential analysis of structure probing data in different conditions can reveal the RNA structurally variable regions (SVRs), which is important for understanding RNA functions. Here, we propose DiffScan, a computational framework for normalization and differential analysis of structure probing data in high resolution. DiffScan preprocesses structure probing datasets to remove systematic bias, and then scans the transcripts to identify SVRs and adaptively determines their lengths and locations. The proposed approach is compatible with most structure probing platforms (e.g., icSHAPE, DMS-seq). When evaluated with simulated and benchmark datasets, DiffScan identifies structurally variable regions at nucleotide resolution, with substantial improvement in accuracy compared with existing SVR detection methods. Moreover, the improvement is robust when tested in multiple structure probing platforms. Application of DiffScan in a dataset of multi-subcellular RNA structurome identified multiple regions that form different structures in nucleus and cytoplasm, linking RNA structural variation to regulation of mRNAs encoding mitochondria-associated proteins. This work provides an effective tool for differential analysis of RNA secondary structure, reinforcing the power of structure probing experiments in deciphering the dynamic RNA structurome.

Rapid structure-function insights via hairpin-centric analysis of big RNA structure probing datasets

The functions of RNA are often tied to its structure, hence analyzing structure is of significant interest when studying cellular processes. Recently, large-scale structure probing (SP) studies have enabled assessment of global structure-function relationships via standard data summarizations or local folding. Here, we approach structure quantification from a hairpin-centric perspective where putative hairpins are identified in SP datasets and used as a means to capture local structural effects. This has the advantage of rapid processing of big (e.g. transcriptome-wide) data as RNA folding is circumvented, yet it captures more information than simple data summarizations. We reformulate a statistical learning algorithm we previously developed to significantly improve precision of hairpin detection, then introduce a novel nucleotide-wise measure, termed the hairpin-derived structure level (HDSL), which captures local structuredness by accounting for the presence of likely hairpin elements. Applying HDSL to data from recent studies recapitulates, strengthens and expands on their findings which were obtained by more comprehensive folding algorithms, yet our analyses are orders of magnitude faster. These results demonstrate that hairpin detection is a promising avenue for global and rapid structure-function analysis, furthering our understanding of RNA biology and the principal features which drive biological insights from SP data.

RNA structure probing reveals the structural basis of Dicer binding and cleavage

It is known that an RNA’s structure determines its biological function, yet current RNA structure probing methods only capture partial structure information. The ability to measure intact (i.e., full length) RNA structures will facilitate investigations of the functions and regulation mechanisms of small RNAs and identify short fragments of functional sites. Here, we present icSHAPE-MaP, an approach combining in vivo selective 2′-hydroxyl acylation and mutational profiling to probe intact RNA structures. We further showcase the RNA structural landscape of substrates bound by human Dicer based on the combination of RNA immunoprecipitation pull-down and icSHAPE-MaP small RNA structural profiling. We discover distinct structural categories of Dicer substrates in correlation to both their binding affinity and cleavage efficiency. And by tertiary structural modeling constrained by icSHAPE-MaP RNA structural data, we find the spatial distance measuring as an influential parameter for Dicer cleavage-site selection.

diffBUM-HMM: a robust statistical modeling approach for detecting RNA flexibility changes in high-throughput structure probing data

Advancing RNA structural probing techniques with next-generation sequencing has generated demands for complementary computational tools to robustly extract RNA structural information amidst sampling noise and variability. We present diffBUM-HMM, a noise-aware model that enables accurate detection of RNA flexibility and conformational changes from high-throughput RNA structure-probing data. diffBUM-HMM is widely compatible, accounting for sampling variation and sequence coverage biases, and displays higher sensitivity than existing methods while robust against false positives. Our analyses of datasets generated with a variety of RNA probing chemistries demonstrate the value of diffBUM-HMM for quantitatively detecting RNA structural changes and RNA-binding protein binding sites.

OmpA, a Common Virulence Factor, Is Under RNA Thermometer Control in Yersinia pseudotuberculosis

The outer membrane protein OmpA is a virulence factor in many mammalian pathogens. In previous global RNA structure probing studies, we found evidence for a temperature-modulated RNA structure in the 5'-untranslated region (5'-UTR) of the Yersinia pseudotuberculosis ompA transcript suggesting that opening of the structure at host-body temperature might relieve translational repression. Here, we support this hypothesis by quantitative reverse transcription PCR, translational reporter gene fusions, enzymatic RNA structure probing, and toeprinting assays. While ompA transcript levels decreased at 37°C compared to 25°C, translation of the transcript increased with increasing temperature. Biochemical experiments show that this is due to melting of the RNA structure, which permits ribosome binding to the 5'-UTR. A point mutation that locks the RNA structure in a closed conformation prevents translation by impairing ribosome access. Our findings add another common virulence factor to the growing list of pathogen-associated genes that are under RNA thermometer control.