Prediction of hydrogen and carbon chemical shifts from RNA using database mining and support vector regression

Role of RNA structural plasticity in modulating HIV-1 genome packaging and translation

HIV-1 transcript function is controlled in part by twinned transcriptional start site usage, where 5' capped RNAs beginning with a single guanosine (1G) are preferentially packaged into progeny virions as genomic RNA (gRNA) whereas those beginning with three sequential guanosines (3G) are retained in cells as mRNAs. In 3G transcripts, one of the additional guanosines base pairs with a cytosine located within a conserved 5' polyA element, resulting in formation of an extended 5' polyA structure as opposed to the hairpin structure formed in 1G RNAs. To understand how this remodeling influences overall transcript function, we applied in vitro biophysical studies with in-cell genome packaging and competitive translation assays to native and 5' polyA mutant transcripts generated with promoters that differentially produce 1G or 3G RNAs. We identified mutations that stabilize the 5' polyA hairpin structure in 3G RNAs, which promote RNA dimerization and Gag binding without sequestering the 5' cap. None of these 3G transcripts were competitively packaged, confirming that cap exposure is a dominant negative determinant of viral genome packaging. For all RNAs examined, conformations that favored 5' cap exposure were both poorly packaged and more efficiently translated than those that favored 5' cap sequestration. We propose that structural plasticity of 5' polyA and other conserved RNA elements place the 5' leader on a thermodynamic tipping point for low-energetic (~3 kcal/mol) control of global transcript structure and function.

Machine Learning Isotropic g Values of Radical Polymers

Methods for electronic structure computations, such as density functional theory (DFT), are routinely used for the calculation of spectroscopic parameters to establish and validate structure-parameter correlations. DFT calculations, however, are computationally expensive for large systems such as polymers. This work explores the machine learning (ML) of isotropic g values, giso, obtained from electron paramagnetic resonance (EPR) experiments of an organic radical polymer. An ML model based on regression trees is trained on DFT-calculated g values of poly(2,2,6,6-tetramethylpiperidinyloxy-4-yl methacrylate) (PTMA) polymer structures extracted from different time frames of a molecular dynamics trajectory. The DFT-derived g values, gisocalc, for different radical densities of PTMA, are compared against experimentally derived g values obtained from in operando EPR measurements of a PTMA-based organic radical battery. The ML-predicted giso values, gisopred, were compared with gisocalc to evaluate the performance of the model. Mean deviations of gisopred from gisocalc were found to be on the order of 0.0001. Furthermore, a performance evaluation on test structures from a separate MD trajectory indicated that the model is sensitive to the radical density and efficiently learns to predict giso values even for radical densities that were not part of the training data set. Since our trained model can reproduce the changes in giso along the MD trajectory and is sensitive to the extent of equilibration of the polymer structure, it is a promising alternative to computationally more expensive DFT methods, particularly for large systems that cannot be easily represented by a smaller model system.

MIM-ML: A Novel Quantum Chemical Fragment-Based Random Forest Model for Accurate Prediction of NMR Chemical Shifts of Nucleic Acids

We developed a random forest machine learning (ML) model for the prediction of 1H and 13C NMR chemical shifts of nucleic acids. Our ML model is trained entirely on reproducing computed chemical shifts obtained previously on 10 nucleic acids using a Molecules-in-Molecules (MIM) fragment-based density functional theory (DFT) protocol including microsolvation effects. Our ML model includes structural descriptors as well as electronic descriptors from an inexpensive low-level semiempirical calculation (GFN2-xTB) and trained on a relatively small number of DFT chemical shifts (2080 1H chemical shifts and 1780 13C chemical shifts on the 10 nucleic acids). The ML model is then used to make chemical shift predictions on 8 new nucleic acids ranging in size from 600 to 900 atoms and compared directly to experimental data. Though no experimental data was used in the training, the performance of our model is excellent (mean absolute deviation of 0.34 ppm for 1H chemical shifts and 2.52 ppm for 13C chemical shifts for the test set), despite having some nonstandard structures. A simple analysis suggests that both structural and electronic descriptors are critical for achieving reliable predictions. This is the first attempt to combine ML from fragment-based DFT calculations to predict experimental chemical shifts accurately, making the MIM-ML model a valuable tool for NMR predictions of nucleic acids.

Crystal structure of a highly conserved enteroviral 5' cloverleaf RNA replication element

The extreme 5'-end of the enterovirus RNA genome contains a conserved cloverleaf-like domain that recruits 3CD and PCBP proteins required for initiating genome replication. Here, we report the crystal structure at 1.9 Å resolution of this domain from the CVB3 genome in complex with an antibody chaperone. The RNA folds into an antiparallel H-type four-way junction comprising four subdomains with co-axially stacked sA-sD and sB-sC helices. Long-range interactions between a conserved A40 in the sC-loop and Py-Py helix within the sD subdomain organize near-parallel orientations of the sA-sB and sC-sD helices. Our NMR studies confirm that these long-range interactions occur in solution and without the chaperone. The phylogenetic analyses indicate that our crystal structure represents a conserved architecture of enteroviral cloverleaf-like domains, including the A40 and Py-Py interactions. The protein binding studies further suggest that the H-shape architecture provides a ready-made platform to recruit 3CD and PCBP2 for viral replication.

NMR chemical shift assignments of RNA oligonucleotides to expand the RNA chemical shift database

RNAs play myriad functional and regulatory roles in the cell. Despite their significance, three-dimensional structure elucidation of RNA molecules lags significantly behind that of proteins. NMR-based studies are often rate-limited by the assignment of chemical shifts. Automation of the chemical shift assignment process can greatly facilitate structural studies, however, accurate chemical shift predictions rely on a robust and complete chemical shift database for training. We searched the Biological Magnetic Resonance Data Bank (BMRB) to identify sequences that had no (or limited) chemical shift information. Here, we report the chemical shift assignments for 12 RNA hairpins designed specifically to help populate the BMRB.

Probabilistic Modeling of RNA Ensembles Using NMR Chemical Shifts

NMR-derived chemical shifts are structural fingerprints that are sensitive to the underlying conformational distributions of molecules. Thus, chemical shift data are now routinely used to infer the dynamical or conformational ensembles of peptides and proteins. However, for RNAs, techniques for inferring their conformational ensembles from chemical shift data have received less attention. Here, we used chemical shift data and the Bayesian/maximum entropy (BME) approach to model the secondary structure ensembles of several single-stranded RNAs. Inspection of the resulting ensembles indicates that the secondary structure of the highest weighted (most probable) conformer in the ensemble typically resembled the known NMR structure. Furthermore, using apo chemical shifts measured for the HIV-1 TAR RNA, we found that our framework reproduces the expected structure yet predicts the existence of a previously unobserved base pair, which we speculate may be sampled transiently. We expect that the chemical shift-based BME (CS-BME) framework we describe here should find utility as a general strategy for modeling RNA ensembles using chemical shift data.

CS-Annotate: A Tool for Using NMR Chemical Shifts to Annotate RNA Structure

Here, we introduce CS-Annotate, a tool that uses assigned NMR chemical shifts to annotate structural features in RNA. At its core, CS-Annotate is a deployment of a multitask deep learning model that simultaneously classifies the solvent exposure, base-stacking and -pairing status, and conformation of individual RNA residues from their chemical shift fingerprint. Here, we briefly describe how we trained and tested the classifier and demonstrate its application to a model RNA system. CS-Annotate can be accessed via the SMALTR (Structure-based MAchine Learning Tools for RNA) Science Gateway (smaltr.org).

Chemical shift prediction of RNA imino groups: application toward characterizing RNA excited states

NH groups in proteins or nucleic acids are the most challenging target for chemical shift prediction. Here we show that the RNA base pair triplet motif dictates imino chemical shifts in its central base pair. A lookup table is established that links each type of base pair triplet to experimental chemical shifts of the central base pair, and can be used to predict imino chemical shifts of RNAs to remarkable accuracy. Strikingly, the semiempirical method can well interpret the variations of chemical shifts for different base pair triplets, and is even applicable to non-canonical motifs. This finding opens an avenue for predicting chemical shifts of more complicated RNA motifs. Furthermore, we combine the imino chemical shift prediction with NMR relaxation dispersion experiments targeting both ¹⁵N and ¹H^N of the imino group, and verify a previously characterized excited state of P5abc subdomain including an earlier speculated non-native G•G mismatch.

Prediction of chemical shifts is critical for extracting structural and dynamic information from biomolecular NMR data. Here the authors report an RNA imino group chemical shift predictor, showing that the imino chemical shifts of a residue are dictated by the surrounding base pair triplet.

Chemical shifts-based similarity restraints improve accuracy of RNA structures determined via NMR

Determination of structure of RNA via NMR is complicated in large part by the lack of a precise parameterization linking the observed chemical shifts to the underlying geometric parameters. In contrast to proteins, where numerous high-resolution crystal structures serve as coordinate templates for this mapping, such models are rarely available for smaller oligonucleotides accessible via NMR, or they exhibit crystal packing and counter-ion binding artifacts that prevent their use for the chemical shifts analysis. On the other hand, NMR-determined structures of RNA often are not solved at the density of restraints required to precisely define the variable degrees of freedom. In this study we sidestep the problems of direct parameterization of the RNA chemical shifts/structure relationship and examine the effects of imposing local fragmental coordinate similarity restraints based on similarities of the experimental secondary ribose ¹³C/¹H chemical shifts instead. The effect of such chemical shift similarity (CSS) restraints on the structural accuracy is assessed via residual dipolar coupling (RDC)-based cross-validation. Improvements in the coordinate accuracy are observed for all of the six RNA constructs considered here as test cases, which argues for routine inclusion of these terms during NMR-based oligonucleotide structure determination. Such accuracy improvements are expected to facilitate derivation of the chemical shift/structure relationships for RNA.

NMR Studies of Retroviral Genome Packaging

Nearly all retroviruses selectively package two copies of their unspliced RNA genomes from a cellular milieu that contains a substantial excess of non-viral and spliced viral RNAs. Over the past four decades, combinations of genetic experiments, phylogenetic analyses, nucleotide accessibility mapping, in silico RNA structure predictions, and biophysical experiments were employed to understand how retroviral genomes are selected for packaging. Genetic studies provided early clues regarding the protein and RNA elements required for packaging, and nucleotide accessibility mapping experiments provided insights into the secondary structures of functionally important elements in the genome. Three-dimensional structural determinants of packaging were primarily derived by nuclear magnetic resonance (NMR) spectroscopy. A key advantage of NMR, relative to other methods for determining biomolecular structure (such as X-ray crystallography), is that it is well suited for studies of conformationally dynamic and heterogeneous systems—a hallmark of the retrovirus packaging machinery. Here, we review advances in understanding of the structures, dynamics, and interactions of the proteins and RNA elements involved in retroviral genome selection and packaging that are facilitated by NMR.

Structural basis for transcriptional start site control of HIV-1 RNA fate

Heterogeneous transcriptional start site usage by HIV-1 produces 5'-capped RNAs beginning with one, two, or three 5'-guanosines (Cap1G, Cap2G, or Cap3G, respectively) that are either selected for packaging as genomes (Cap1G) or retained in cells as translatable messenger RNAs (mRNAs) (Cap2G and Cap3G). To understand how 5'-guanosine number influences fate, we probed the structures of capped HIV-1 leader RNAs by deuterium-edited nuclear magnetic resonance. The Cap1G transcript adopts a dimeric multihairpin structure that sequesters the cap, inhibits interactions with eukaryotic translation initiation factor 4E, and resists decapping. The Cap2G and Cap3G transcripts adopt an alternate structure with an elongated central helix, exposed splice donor residues, and an accessible cap. Extensive remodeling, achieved at the energetic cost of a G-C base pair, explains how a single 5'-guanosine modifies the function of a ~9-kilobase HIV-1 transcript.

Assigning NMR spectra of RNA, peptides and small organic molecules using molecular network visualization software

NMR assignment typically involves analysis of peaks across multiple NMR spectra. Chemical shifts of peaks are measured before being assigned to atoms using a variety of methods. These approaches quickly become complicated by overlap, ambiguity, and the complexity of correlating assignments among multiple spectra. Here we propose an alternative approach in which a network of linked peak-boxes is generated at the predicted positions of peaks across all spectra. These peak-boxes correlate known relationships and can be matched to the observed spectra. The method is illustrated with RNA, but a variety of molecular types should be readily tractable with this approach.

Classification of RNA backbone conformations into rotamers using 13C' chemical shifts: exploring how far we can go

The conformational space of the ribose-phosphate backbone is very complex as it is defined in terms of six torsional angles. To help delimit the RNA backbone conformational preferences, 46 rotamers have been defined in terms of these torsional angles. In the present work, we use the ribose experimental and theoretical ¹³C′ chemical shifts data and machine learning methods to classify RNA backbone conformations into rotamers and families of rotamers. We show to what extent the experimental ¹³C′ chemical shifts can be used to identify rotamers and discuss some problem with the theoretical computations of ¹³C′ chemical shifts.

Advances that facilitate the study of large RNA structure and dynamics by nuclear magnetic resonance spectroscopy

The characterization of functional yet nonprotein coding (nc) RNAs has expanded the role of RNA in the cell from a passive player in the central dogma of molecular biology to an active regulator of gene expression. The misregulation of ncRNA function has been linked with a variety of diseases and disorders ranging from cancers to neurodegeneration. However, a detailed molecular understanding of how ncRNAs function has been limited; due, in part, to the difficulties associated with obtaining high-resolution structures of large RNAs. Tertiary structure determination of RNA as a whole is hampered by various technical challenges, all of which are exacerbated as the size of the RNA increases. Namely, RNAs tend to be highly flexible and dynamic molecules, which are difficult to crystallize. Biomolecular nuclear magnetic resonance (NMR) spectroscopy offers a viable alternative to determining the structure of large RNA molecules that do not readily crystallize, but is itself hindered by some technical limitations. Recently, a series of advancements have allowed the biomolecular NMR field to overcome, at least in part, some of these limitations. These advances include improvements in sample preparation strategies as well as methodological improvements. Together, these innovations pave the way for the study of ever larger RNA molecules that have important biological function. This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems.

13Check_RNA: a tool to evaluate 13C chemical shift assignments of RNA

Motivation

Chemical shifts (CS) are an important source of structural information of macromolecules such as RNA. In addition to the scarce availability of CS for RNA, the observed values are prone to errors due to a wrong re-calibration or miss assignments. Different groups have dedicated their efforts to correct CS systematic errors on RNA. Despite this, there are not automated and freely available algorithms for evaluating the referencing of RNA 13 C CS before their deposition to the BMRB or re-reference already deposited CS with systematic errors.

Results

Based on an existent method we have implemented an open source python module to correct 13 C CS (from here on 13Cexp) systematic errors of RNAs and then return the results in 3 formats including the nmrstar one.

Availability and implementation

This software is available on GitHub at https://github.com/BIOS-IMASL/13Check_RNA under a MIT license.

Supplementary information

Supplementary data are available at Bioinformatics online.

NMR probing of invisible excited states using selectively labeled RNAs

Carr-Purcell-Meiboom-Gill (CPMG) relaxation dispersion NMR experiments are invaluable for probing sparsely and transiently populated biomolecular states that cannot be directly detected by traditional NMR experiments and that are invisible by other biophysical approaches. A notable gap for RNA is the absence of CPMG experiments for measurement of methine base ¹H and methylene C5' chemical shifts of ribose moieties in the excited state, partly because of complications from homonuclear ¹³C-¹³C scalar couplings. Here we present site-specific ¹³C labeling that makes possible the design of pulse sequences for recording accurate ¹H-¹³C MQ and SQ CPMG experiments for ribose methine H1'-C1' and H2'-C2', base and ribose ¹H CPMG, as well as a new ¹H-¹³C TROSY-detected methylene (CH₂) C5' CPMG relaxation pulse schemes. We demonstrate the utility of these experiments for two RNAs, the A-Site RNA known to undergo exchange and the IRE RNA suspected of undergoing exchange on microseconds to millisecond time-scale. We anticipate the new labeling approaches will facilitate obtaining structures of invisible states and provide insights into the relevance of such states for RNA-drug interactions.

Accurate Measurement of Residual Dipolar Couplings in Large RNAs by Variable Flip Angle NMR

NMR approaches using nucleotide-specific deuterium labeling schemes have enabled structural studies of biologically relevant RNAs of increasing size and complexity. Although local structure is well-determined using these methods, definition of global structural features, including relative orientations of independent helices, remains a challenge. Residual dipolar couplings, a potential source of orientation information, have not been obtainable for large RNAs due to poor sensitivity resulting from rapid heteronuclear signal decay. Here we report a novel multiple quantum NMR method for RDC determination that employs flip angle variation rather than a coupling evolution period. The accuracy of the method and its utility for establishing interhelical orientations are demonstrated for a 36-nucleotide RNA, for which comparative data could be obtained. Applied to a 78 kDa Rev response element from the HIV-1 virus, which has an effective rotational correlation time of ca. 160 ns, the method yields sensitivity gains of an order of magnitude or greater over existing approaches. Solution-state access to structural organization in RNAs of at least 230 nucleotides is now possible.

Atomic structures of excited state A-T Hoogsteen base pairs in duplex DNA by combining NMR relaxation dispersion, mutagenesis, and chemical shift calculations

NMR relaxation dispersion studies indicate that in canonical duplex DNA, Watson-Crick base pairs (bps) exist in dynamic equilibrium with short-lived low abundance excited state Hoogsteen bps. N1-methylated adenine (m¹A) and guanine (m¹G) are naturally occurring forms of damage that stabilize Hoogsteen bps in duplex DNA. NMR dynamic ensembles of DNA duplexes with m¹A-T Hoogsteen bps reveal significant changes in sugar pucker and backbone angles in and around the Hoogsteen bp, as well as kinking of the duplex towards the major groove. Whether these structural changes also occur upon forming excited state Hoogsteen bps in unmodified duplexes remains to be established because prior relaxation dispersion probes provided limited information regarding the sugar-backbone conformation. Here, we demonstrate measurements of C3' and C4' spin relaxation in the rotating frame (R1ρ) in uniformly ¹³C/¹⁵N labeled DNA as sensitive probes of the sugar-backbone conformation in DNA excited states. The chemical shifts, combined with structure-based predictions using an automated fragmentation quantum mechanics/molecular mechanics method, show that the dynamic ensemble of DNA duplexes containing m¹A-T Hoogsteen bps accurately model the excited state Hoogsteen conformation in two different sequence contexts. Formation of excited state A-T Hoogsteen bps is accompanied by changes in sugar-backbone conformation that allow the flipped syn adenine to form hydrogen-bonds with its partner thymine and this in turn results in overall kinking of the DNA toward the major groove. Results support the assignment of Hoogsteen bps as the excited state observed in canonical duplex DNA, provide an atomic view of DNA dynamics linked to formation of Hoogsteen bps, and lay the groundwork for a potentially general strategy for solving structures of nucleic acid excited states.

A Novel Approach of Weighted Support Vector Machine with Applied Chance Theory for Forecasting Air Pollution Phenomenon in Egypt

The particulate matter air pollutant of diameter less than 10 micrometers (PM[Formula: see text]), a category of pollutants including solid and liquid particles, can be a health hazard for several reasons: it can harm lung tissues and throat, aggravate asthma and increase respiratory illness. Accurate prediction models of PM[Formula: see text] concentrations are essential for proper management, control, and making public warning strategies. Therefore, machine learning techniques have the capability to develop methods or tools that can be used to discover unseen patterns from given data to solve a particular task or problem. The chance theory has advanced concepts pertinent to treat cases where both randomness and fuzziness play simultaneous roles at one time. The main objective is to study the modification of a single machine learning algorithm — support vector machine (SVM) — applying the chance weight of the target variable, based on the chance theory, to the corresponding dataset point to be superior to the ensemble machine learning algorithms. The results of this study are outperforming of the SVM algorithms when modifying and combining with the right theory/technique, especially the chance theory over other modern ensemble learning algorithms.

Structure of the 30 kDa HIV-1 RNA Dimerization Signal by a Hybrid Cryo-EM, NMR, and Molecular Dynamics Approach

Cryoelectron microscopy (cryo-EM) and nuclear magnetic resonance (NMR) spectroscopy are routinely used to determine structures of macromolecules with molecular weights over 65 and under 25 kDa, respectively. We combined these techniques to study a 30 kDa HIV-1 dimer initiation site RNA ([DIS]2; 47 nt/strand). A 9 Å cryo-EM map clearly shows major groove features of the double helix and a right-handed superhelical twist. Simulated cryo-EM maps generated from time-averaged molecular dynamics trajectories (10 ns) exhibited levels of detail similar to those in the experimental maps, suggesting internal structural flexibility limits the cryo-EM resolution. Simultaneous inclusion of the cryo-EM map and 2H-edited NMR-derived distance restraints during structure refinement generates a structure consistent with both datasets and supporting a flipped-out base within a conserved purine-rich bulge. Our findings demonstrate the power of combining global and local structural information from these techniques for structure determination of modest-sized RNAs.

Air Pollution Forecasting Model Based on Chance Theory and Intelligent Techniques

A novel approach of weighted support vector regression (WSVR) technique with applied chance theory was proposed to build a robust forecasting model, called the chance weighted support vector regression (chWSVR) model. In order to forecast the particulate matter air pollutant of diameter less than 10 micrometers (PM10) one hour advance in the Greater Cairo Metropolitan Area (GCMA) in Egypt. The chance theory has advanced concepts pertinent to treat cases where both randomness and fuzziness play simultaneous roles at one time. The basic idea based on the proposed chWSVR model is assigning the chance weight value of the target variable, based on the chance theory, to its corresponding dataset point to become minimized in the objective function making that point more significant during the training process. Measuring data were collected and reprocessed from four monitoring stations located in GCMA and relative to the springs during the period from 2007 to 2010. The results of such model compared to similar ones built by other machine learning techniques, Random Forest and Bootstrap aggregating techniques. In all stations, comparing such models revealed that the proposed chWSVR model findings were promising in the forecasting of PM10 hourly concentration.

Combining asymmetric 13C-labeling and isotopic filter/edit NOESY: a novel strategy for rapid and logical RNA resonance assignment

Although ∼98% of the human genomic output is transcribed as non-protein coding RNA, <2% of the protein data bank structures comprise RNA. This huge structural disparity stems from combined difficulties of crystallizing RNA for X-ray crystallography along with extensive chemical shift overlap and broadened linewidths associated with NMR of RNA. While half of the deposited RNA structures in the PDB were solved by NMR methods, the usefulness of NMR is still limited by the high cost of sample preparation and challenges of resonance assignment. Here we propose a novel strategy for resonance assignment that combines new strategic 13C labeling technologies with filter/edit type NOESY experiments to greatly reduce spectral complexity and crowding. This new strategy allowed us to assign important non-exchangeable resonances of proton and carbon (1', 2', 2, 5, 6 and 8) nuclei using only one sample and <24 h of NMR instrument time for a 27 nt model RNA. The method was further extended to assigning a 6 nt bulge from a 61 nt viral RNA element justifying its use for a wide range RNA chemical shift resonance assignment problems.

Structure and Dynamics of RNA Repeat Expansions That Cause Huntington's Disease and Myotonic Dystrophy Type 1

RNA repeat expansions cause a host of incurable, genetically defined diseases. The most common class of RNA repeats consists of trinucleotide repeats. These long, repeating transcripts fold into hairpins containing 1 × 1 internal loops that can mediate disease via a variety of mechanism(s) in which RNA is the central player. Two of these disorders are Huntington's disease and myotonic dystrophy type 1, which are caused by r(CAG) and r(CUG) repeats, respectively. We report the structures of two RNA constructs containing three copies of a r(CAG) [r(3×CAG)] or r(CUG) [r(3×CUG)] motif that were modeled with nuclear magnetic resonance spectroscopy and simulated annealing with restrained molecular dynamics. The 1 × 1 internal loops of r(3×CAG) are stabilized by one-hydrogen bond (cis Watson-Crick/Watson-Crick) AA pairs, while those of r(3×CUG) prefer one- or two-hydrogen bond (cis Watson-Crick/Watson-Crick) UU pairs. Assigned chemical shifts for the residues depended on the identity of neighbors or next nearest neighbors. Additional insights into the dynamics of these RNA constructs were gained by molecular dynamics simulations and a discrete path sampling method. Results indicate that the global structures of the RNA are A-form and that the loop regions are dynamic. The results will be useful for understanding the dynamic trajectory of these RNA repeats but also may aid in the development of therapeutics.

NMR solution structure determination of large RNA-protein complexes

Structure determination of RNA-protein complexes is essential for our understanding of the multiple layers of RNA-mediated posttranscriptional regulation of gene expression. Over the past 20years, NMR spectroscopy became a key tool for structural studies of RNA-protein interactions. Here, we review the progress being made in NMR structure determination of large ribonucleoprotein assemblies. We discuss approaches for the design of RNA-protein complexes for NMR structural studies, established and emerging isotope and segmental labeling schemes suitable for large RNPs and how to gain distance restraints from NOEs, PREs and EPR and orientational information from RDCs and SAXS/SANS in such systems. The new combination of NMR measurements with MD simulations and its potential will also be discussed. Application and combination of these various methods for structure determination of large RNPs will be illustrated with three large RNA-protein complexes (>40kDa) and other interesting complexes determined in the past six and a half years.

NMR detection of intermolecular interaction sites in the dimeric 5'-leader of the HIV-1 genome

HIV type-1 (HIV-1) contains a pseudodiploid RNA genome that is selected for packaging and maintained in virions as a noncovalently linked dimer. Genome dimerization is mediated by conserved elements within the 5'-leader of the RNA, including a palindromic dimer initiation signal (DIS) that has been proposed to form kissing hairpin and/or extended duplex intermolecular contacts. Here, we have applied a ²H-edited NMR approach to directly probe for intermolecular interactions in the full-length, dimeric HIV-1 5'-leader (688 nucleotides; 230 kDa). The interface is extensive and includes DIS:DIS base pairing in an extended duplex state as well as intermolecular pairing between elements of the upstream Unique-5' (U5) sequence and those near the gag start site (AUG). Other pseudopalindromic regions of the leader, including the transcription activation (TAR), polyadenylation (PolyA), and primer binding (PBS) elements, do not participate in intermolecular base pairing. Using a ²H-edited one-dimensional NMR approach, we also show that the extended interface structure forms on a time scale similar to that of overall RNA dimerization. Our studies indicate that a kissing dimer-mediated structure, if formed, exists only transiently and readily converts to the extended interface structure, even in the absence of the HIV-1 nucleocapsid protein or other RNA chaperones.

NMRFx Processor: a cross-platform NMR data processing program

NMRFx Processor is a new program for the processing of NMR data. Written in the Java programming language, NMRFx Processor is a cross-platform application and runs on Linux, Mac OS X and Windows operating systems. The application can be run in both a graphical user interface (GUI) mode and from the command line. Processing scripts are written in the Python programming language and executed so that the low-level Java commands are automatically run in parallel on computers with multiple cores or CPUs. Processing scripts can be generated automatically from the parameters of NMR experiments or interactively constructed in the GUI. A wide variety of processing operations are provided, including methods for processing of non-uniformly sampled datasets using iterative soft thresholding. The interactive GUI also enables the use of the program as an educational tool for teaching basic and advanced techniques in NMR data analysis.

Nuclear Magnetic Resonance-Assisted Prediction of Secondary Structure for RNA: Incorporation of Direction-Dependent Chemical Shift Constraints

Knowledge of RNA structure is necessary to determine structure–function relationships and to facilitate design of potential therapeutics. RNA secondary structure prediction can be improved by applying constraints from nuclear magnetic resonance (NMR) experiments to a dynamic programming algorithm. Imino proton walks from NOESY spectra reveal double-stranded regions. Chemical shifts of protons in GH1, UH3, and UH5 of GU pairs, UH3, UH5, and AH2 of AU pairs, and GH1 of GC pairs were analyzed to identify constraints for the 5′ to 3′ directionality of base pairs in helices. The 5′ to 3′ directionality constraints were incorporated into an NMR-assisted prediction of secondary structure (NAPSS-CS) program. When it was tested on 18 structures, including nine pseudoknots, the sensitivity and positive predictive value were improved relative to those of three unrestrained programs. The prediction accuracy for the pseudoknots improved the most. The program also facilitates assignment of chemical shifts to individual nucleotides, a necessary step for determining three-dimensional structure.