Secondary Structure Prediction Programs Research Articles

Evaluating Performance of Different RNA Secondary Structure Prediction Programs Using Self-cleaving Ribozymes.

Accurate identification of the correct, biologically relevant RNA structures is critical to understanding various aspects of RNA biology since proper folding represents the key to the functionality of all types of RNA molecules and plays pivotal roles in many essential biological processes. Thus, a plethora of approaches have been developed to predict, identify, or solve RNA structures based on various computational, molecular, genetic, chemical, or physicochemical strategies. Purely computational approaches hold distinct advantages over all other strategies in terms of the ease of implementation, time, speed, cost, and throughput, but they strongly underperform in terms of accuracy that significantly limits their broader application. Nonetheless, the advantages of these methods led to a steady development of multiple in silico RNA secondary structure prediction approaches including recent deep learning-based programs. Here, we compared the accuracy of predictions of biologically relevant secondary structures of dozens of self-cleaving ribozyme sequences using seven in silico RNA folding prediction tools with tasks of varying complexity. We found that while many programs performed well in relatively simple tasks, their performance varied significantly in more complex RNA folding problems. However, in general, a modern deep learning method outperformed the other programs in the complex tasks in predicting the RNA secondary structures, at least based on the specific class of sequences tested, suggesting that it may represent the future of RNA structure prediction algorithms.

In silico design of short hairpin RNA (shRNA)Molecules for DNA pol gene of Contagious Ecthyma virus (ORFV)

Contagious ecthyma is an infectious skin disease of ruminants caused by the ORF virus (ORFV) that is a member of genus Parapoxvirus of the Poxviridae family. In addition to the significant effects on lambs and human, ORFVs have been recently shown to infect other hosts. The disease causes significant economic damages to the sheep industry, so attempts to eliminate it must be taken into.RNA interference (RNAi) is an evolutionarily conserved mechanism in which the expression of homologous target genesis is suppressed by means of double-stranded RNA molecules. Since RNAi can be considered as a therapeutic method for viral gene silencing, we tend to make the most of this capability. The present study aims to design potential shRNAs to knockdown the DNA-polymerase gene coded by ORF025.A significant number of computational methods such as clustal omega website to target alignment, BLAST-NCBI to similarity search, CLC software to secondary structure prediction, BLOCK-iTRNAi Designer and WI siRNA Selection Program and Software to design of shRNA molecules and scoring have been applied for shRNA molecules designing against the ORF025-DNA pol gene of ORFV. Then three shRNA molecules were logically designed against the ORF025-DNA pol gene. In conclusion the present study provides a strong and superior approach for achieving a validated strategy to design an antiviral shRNA molecule that meets many sequence features for efficient ORFV knockdown and treatment at the mRNA level. The efficiency of these anti ORFV shRNAs need to be tested

Abstract 2444: Do all 4 NCL RNA Binding Domains play a role in mRNA target specificity? An in silico study to decipher NCL-mRNA interactions

Abstract RNA binding proteins (RBPs) are post-transcriptional regulators that associate with RNA molecules through one or more of their RNA-binding domains (RBDs). The dysregulation of RBPs, and the RNA metabolism pathways they intersect, are known drivers of tumorigenesis. Nucleolin (NCL) is a stress responsive RBP with four RBDs that directly impacts gene expression by altering rRNA processing, RNA localization, miRNA biogenesis, mRNA decay, and translation. NCL is known to interact with both the 3' and 5' untranslated regions (UTR) and coding regions of target mRNAs to either modulate their stability or translation via recognition of AU-rich or G-rich elements in the RNA. However, the consequences of NCL-mRNA interactions are target-specific as well as context and location dependent. While NCL interacts with both 3' and 5' UTR of TP53 mRNA and suppresses its translation, interactions of NCL with 5' UTR of an anti-apoptotic gene, BCL2, stabilizes this transcript. NCL-mediated enhanced biogenesis of tumorigenic mir-15a and mir-16, on the other hand, results in the translational repression of BCL2. Because NCL exerts its functions in such a complex manner, elucidation of its roles in tumorigenic pathways is paramount in the development of therapeutical applications targeting NCL-RNA interactions. Mechanisms driving NCL-mRNA interactions are currently unknown and this knowledge gap is further compounded by the incomplete nature of the current structural information for NCL and its mRNA targets. In this study, we aim to elucidate NCL RBD-mRNA interactions using computational tools. We investigated interactions of NCL with a specific subset of mRNAs that are implicated in breast cancer progression. We have previously generated and validated in silico models for all the four NCL-RBDs (1-4) in various combinations. Using several secondary and tertiary structure prediction programs along with structure refinement programs, we have generated 3D models of TP53, BCL2, CSF-1, and GADD45α mRNA UTR molecules. Our docking analyses suggests that RBD1-2 and RBD3-4 collaborate in most NCL-mRNA interactions using RNA binding motifs (RNP1 & RNP2) on NCL. We further identify additional contact points in the bridging linker regions between the RBDs that might be crucial for the structural stability of these complexes. This study provides details previously not reported in the literature and provides a comprehensive map of NCL RBD-mRNA interactions that have direct pathophysiological relevance in breast cancer. Altered levels of NCL expression are linked with poor prognosis in breast cancers and targeting NCL results in a better survival outcome. Understanding how NCL associates with its mRNA targets will reveal binding pockets and hence potential sites for drug interventions in addition to characterizing the complex nature of NCL-mRNA interactions/specificity in multiple cancer types. Citation Format: Avdar San, Anjana Saxena, Shaneen Singh. Do all 4 NCL RNA Binding Domains play a role in mRNA target specificity? An in silico study to decipher NCL-mRNA interactions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2444.

Exploring Nucleic Acid Structure‐Function Relationships in Remote Biochemistry Instruction

The covid-19 pandemic has forced universities around the world to transition to online, remote, or hybrid instruction. This challenge is exacerbated in the laboratory sciences as the limitations on in-person instruction force instructors to conduct curriculum triage and develop new creative avenues for providing meaningful experiential learning opportunities. However, some learning objectives such as nucleic acid structure function relationships can still be pursued with the rigor necessary to train future biochemists and biophysicists using web-based RNA and DNA structural analysis programs. The decades of research in nucleic acid structure prediction algorithms and modeling programs have laid an exceptional foundation for remote teaching and learning in the biosciences. We evaluate the various nucleic acid secondary and tertiary structure prediction programs and their capabilities for remote laboratory activities and research projects. We identify the particular strengths of each program and learning objectives each one can be used to pursue. We also present a series of guided and independent inquiry activities using these prediction programs exploring nucleic acid secondary and tertiary structure suitable for introductory or advanced biochemistry courses using asynchronous or synchronous modalities. Activities include an exploration of nucleic acid secondary structures and relative stabilities, identifying non-canonical interactions within RNA folds, predicting tertiary interactions, comparing predicted structures to published experimentally-determined secondary structures, and investigating how mutations in the nucleic acid sequence can affect secondary and tertiary structure. Tools to increase engagement and discussion in synchronous virtual laboratory sessions are also presented. These activities focus on the SARS-CoV-2 viral RNA structure but can be modified for any particular RNA or DNA. Available biophysical technology resources provide an avenue for continuing high-level instruction despite the challenges of classroom and laboratory de-densification.

Sequence–function correlation of the transmembrane domains in NS4B of HCV using a computational approach

An algorithm is applied to propose a sequence–function correlation of the transmembrane domains (TMDs) of the non-structural protein 4B (NS4B) of hepatitis C virus (HCV). The putative sequence of the TMDs is obtained using 20 available secondary structure prediction programs (SSPPs) with different lengths of the overall amino acid sequence of the protein as input. The results support the notion of four helical TMDs. Whilst the region of the first TMDs leaves room for speculation about an additional TMD, the other three TMDs are consistently predicted. Structural features and the role of each of the TMDs is proposed by applying pairwise sequence alignment using BLAST on the level (i) protein sequence alignment and consequent (ii) function-related alignment. Sequence identity with those TMDs of proteins involved in Ca-homeostasis and generation of replication vesicles, such as Nsp3 of corona viruses, murine coronavirus especially mouse hepatitis virus (MHV), middle east respiratory syndrome coronavirus (MERS), severe acute respiratory syndrome coronavirus (SARS-CoV) and SARS-CoV-2, are suggested. Focusing the search on those proteins in particular and their TMDs playing an active role in their mechanism of function, such as transporters, pumps, viral channel forming protein Vpu of human immunodeficiency virus type 1 (HIV-1) and mediators, suggests TMDs 2 and 4 to have functional roles in NS4B, as well as additionally TMD1 and 3 in case of vesicle formation.

Predictions and analyses of RNA nearest neighbor parameters for modified nucleotides.

The most popular RNA secondary structure prediction programs utilize free energy (ΔG°37) minimization and rely upon thermodynamic parameters from the nearest neighbor (NN) model. Experimental parameters are derived from a series of optical melting experiments; however, acquiring enough melt data to derive accurate NN parameters with modified base pairs is expensive and time consuming. Given the multitude of known natural modifications and the continuing use and development of unnatural nucleotides, experimentally characterizing all modified NNs is impractical. This dilemma necessitates a computational model that can predict NN thermodynamics where experimental data is scarce or absent. Here, we present a combined molecular dynamics/quantum mechanics protocol that accurately predicts experimental NN ΔG°37 parameters for modified nucleotides with neighboring Watson–Crick base pairs. NN predictions for Watson-Crick and modified base pairs yielded an overall RMSD of 0.32 kcal/mol when compared with experimentally derived parameters. NN predictions involving modified bases without experimental parameters (N6-methyladenosine, 2-aminopurineriboside, and 5-methylcytidine) demonstrated promising agreement with available experimental melt data. This procedure not only yields accurate NN ΔG°37 predictions but also quantifies stacking and hydrogen bonding differences between modified NNs and their canonical counterparts, allowing investigators to identify energetic differences and providing insight into sources of (de)stabilization from nucleotide modifications.

Sequence-Based Prediction of Metamorphic Behavior in Proteins

An increasing number of proteins have been demonstrated in recent years to adopt multiple three-dimensional folds with different functions. These metamorphic proteins are characterized by having two or more folds with significant differences in their secondary structure, in which each fold is stabilized by a distinct local environment. So far, ∼90 metamorphic proteins have been identified in the Protein Databank, but we and others hypothesize that a far greater number of metamorphic proteins remain undiscovered. In this work, we introduce a computational model to predict metamorphic behavior in proteins using only knowledge of the sequence. In this model, secondary structure prediction programs are used to calculate diversity indices, which are measures of uncertainty in predicted secondary structure at each position in the sequence; these are then used to assign protein sequences as likely to be metamorphic versus monomorphic (i.e., having just one fold). We constructed a reference data set to train our classification method, which includes a novel compilation of 136 likely monomorphic proteins and a set of 201 metamorphic protein structures taken from the literature. Our model is able to classify proteins as metamorphic versus monomorphic with a Matthews correlation coefficient of ∼0.36 and true positive/true negative rates of ∼65%/80%, suggesting that it is possible to predict metamorphic behavior in proteins using only sequence information.

Redundancy-weighting the PDB for detailed secondary structure prediction using deep-learning models

The Protein Data Bank (PDB), the ultimate source for data in structural biology, is inherently imbalanced. To alleviate biases, virtually all structural biology studies use nonredundant (NR) subsets of the PDB, which include only a fraction of the available data. An alternative approach, dubbed redundancy-weighting (RW), down-weights redundant entries rather than discarding them. This approach may be particularly helpful for machine-learning (ML) methods that use the PDB as their source for data. Methods for secondary structure prediction (SSP) have greatly improved over the years with recent studies achieving above 70% accuracy for eight-class (DSSP) prediction. As these methods typically incorporate ML techniques, training on RW datasets might improve accuracy, as well as pave the way toward larger and more informative secondary structure classes. This study compares the SSP performances of deep-learning models trained on either RW or NR datasets. We show that training on RW sets consistently results in better prediction of 3- (HCE), 8- (DSSP) and 13-class (STR2) secondary structures. The ML models, the datasets used for their derivation and testing, and a stand-alone SSP program for DSSP and STR2 predictions, are freely available under LGPL license in http://meshi1.cs.bgu.ac.il/rw. Supplementary data are available at Bioinformatics online.

RNAknot: A new algorithm for RNA secondary structure prediction based on genetic algorithm and GRASP method.

The prediction of the optimal secondary structure for a given RNA sequence represents a challenging computational problem in bioinformatics. This challenge becomes harder especially with the discovery of different pseudoknot classes, which is a complex topology that plays diverse roles in biological processes. Many recent studies have been proposed to predict RNA secondary structure with some pseudoknot classes, but only a few of them have reached satisfying results in terms of both complexity and accuracy. Here we present RNAknot, a new method for predicting RNA secondary structure that contains the following components: stems, hairpin loops, multi-branched loops or multi-loops, bulge loops, and internal loops, in addition to two types of pseudoknots, H-type pseudoknot and Hairpin kissing. RNAknot is based on a genetic algorithm and Greedy Randomized Adaptive Search Procedure (GRASP), and it uses the free energy as fitness function to evaluate the obtained structures. In order to validate the performance of the presented method 131 tests have been performed using two datasets of 26 and 105 RNA sequences, which have been taken from the two data bases RNAstrand and Pseudobase respectively. The obtained results are compared with those of some RNA secondary structure prediction programs such as Vs_subopt, CyloFold, IPknot, Kinefold, RNAstructure, and Sfold. The results of this comparative study show that the prediction accuracy of our proposed approach is significantly improved compared to those obtained by the other programs. For the first dataset, RNAknot has the highest specificity (SP) (71.23%) and sensitivity (SN) (72.15%) averages compared to the other programs. Concerning the second dataset, the RNA secondary structure predictions obtained by the RNAknot correspond to the highest averages of SP (85.49%) and F-measure (79.97%) compared to the other programs. The program is available as a jar file in the link: www.bachmek.umi.ac.ma/wp-content/uploads/RNAknot.0.0.2.rar.

Thermodynamic stabilities of three-way junction nanomotifs in prohead RNA.

The thermodynamic stabilities of four natural prohead or packaging RNA (pRNA) three-way junction (3WJ) nanomotifs and seven phi29 pRNA 3WJ deletion mutant nanomotifs were investigated using UV optical melting on a three-component RNA system. Our data reveal that some pRNA 3WJs are more stable than the phi29 pRNA 3WJ. The stability of the 3WJ contributes to the unique self-assembly properties of pRNA. Thus, ultrastable pRNA 3WJ motifs suggest new scaffolds for pRNA-based nanotechnology. We present data demonstrating that pRNA 3WJs differentially respond to the presence of metal ions. A comparison of our data with free energies predicted by currently available RNA secondary structure prediction programs shows that these programs do not accurately predict multibranch loop stabilities. These results will expand the existing parameters used for RNA secondary structure prediction from sequence in order to better inform RNA structure–function hypotheses and guide the rational design of functional RNA supramolecular assemblies.

RNA folding with hard and soft constraints.

BackgroundA large class of RNA secondary structure prediction programs uses an elaborate energy model grounded in extensive thermodynamic measurements and exact dynamic programming algorithms. External experimental evidence can be in principle be incorporated by means of hard constraints that restrict the search space or by means of soft constraints that distort the energy model. In particular recent advances in coupling chemical and enzymatic probing with sequencing techniques but also comparative approaches provide an increasing amount of experimental data to be combined with secondary structure prediction.ResultsResponding to the increasing needs for a versatile and user-friendly inclusion of external evidence into diverse flavors of RNA secondary structure prediction tools we implemented a generic layer of constraint handling into the ViennaRNA Package. It makes explicit use of the conceptual separation of the “folding grammar” defining the search space and the actual energy evaluation, which allows constraints to be interleaved in a natural way between recursion steps and evaluation of the standard energy function.ConclusionsThe extension of the ViennaRNA Package provides a generic way to include diverse types of constraints into RNA folding algorithms. The computational overhead incurred is negligible in practice. A wide variety of application scenarios can be accommodated by the new framework, including the incorporation of structure probing data, non-standard base pairs and chemical modifications, as well as structure-dependent ligand binding.Electronic supplementary materialThe online version of this article (doi:10.1186/s13015-016-0070-z) contains supplementary material, which is available to authorized users.

Prediction of 5-Fluorouracil Toxicity Associated With Dihydropyrimidine Dehydrogenase Gene (DPYD) Polymorphism Using the Secondary Structure Prediction Programs

5-fluorouracil (5-FU) is rapidly degraded by dihydropyrimidine dehydrogenase (DPD), the first and rate-limiting enzyme in the catabolic pathway of 5-FU and pyrimidines. A tolerable therapeutic dose of 5-FU for a DPD-normal patient can make a DPD-deficient patient intolerable. These inter-individual variations in the body’s response and tolerance to drugs can be attributed to dihydropyrimidine dehydrogenase gene (DPYD) single nucleotide polymorphisms (SNPs), among others. To save a patient’s life and money, health professionals need a convenient and reliable way to find out a patient’s tolerance to 5-FU prior to clinical trials or 5-FU therapy. Here I present a simple, easy and fast way to predict an individual’s intolerance to 5-FU using the secondary structure prediction programs, YASPIN, PSIPRED and Jpred 3, freely available to anyone. These programs predict the DPD secondary structure with and without mutation (s) within DPYD so that an impact of the mutation-induced structural changes on functional sites of human DPD domains can be deduced. Among 11 SNPs analyzed as samples, two missense mutations, D949V (SNP A2846T) and C953S (SNP G2858C), in the DPD domain V are predicted to cause disruption of the domain core responsible for [4Fe-4S] clusters. Furthermore, a point mutation in a splicing region (In 14 G1A) in DPYD is predicted to produce truncated DPD mRNAs (exon 14 skipping) and disabled DPD proteins (missing 55 amino acids from D581 to N635) which cause a complete loss of DPD activity. SWISS-MODEL predicts significant change in the three dimensional structures of human DPD in the presence of exon 14 skipping, D949V and C953S mutation. Thus, prediction by these secondary structure prediction programs provides useful and reliable information about toxicity associated 5-FU due to mutation (s) in DPYD.

Modeling Structure of Human Papillomavirus Type 16 E5 Protein - a Molecular Dynamics Simulation Study

Human papillomaviruses (HPV) infect mucosal and cutaneous epithelial cells leading to precancerous lesions. The HPV genome encodes three oncoproteins: E5, E6 and E7 from which E5 is the least understood. E5 of HPV-16, one of the “high risk” types of HPV strains, is an 83 amino acid hydrophobic membrane protein, with three hydrophobic transmembrane domains (TMDs). It oligomerizes into dimers or higher oligomers which form ion channels most likely by forming hexameric bundles. Computational modeling is used to obtain structural and functional features of this protein.The three TMDs of E5 are identified using secondary structure prediction programs. The TMDs are assembled into a monomer by a ‘Sequential’ and ‘Simultaneous’ docking approach in which the conformational space of the three helices is screened by simultaneously altering distance, tilt and rotational angle between them. In a consequent step, loops linking the three helices are added using the program Loopy. Finally six monomers are assembled into a hexameric bundle. The bundle with TMD2 lining the pore remains intact allowing formation water filled pocket during entire 100 ns MD simulations. The water pocket formed by the six TMD2s of the bundle is mostly mantled by hydrophilic residues such as Ser-35, −37 and Thr-38, −40. Bundles with the other two TMDs, TMD1 and TMD3, mantling the pore are energetically almost undistiguishable from the bundle with TMD2 facing the pore.With TMD2s facing the pore, ion channel activity possible. All asymmetric bundle architectures account for interactions of E5 with host proteins.

Improving RNA secondary structure prediction with structure mapping data.

Methods to probe RNA secondary structure, such as small molecule modifying agents, secondary structure-specific nucleases, inline probing, and SHAPE chemistry, are widely used to study the structure of functional RNA. Computational secondary structure prediction programs can incorporate probing data to predict structure with high accuracy. In this chapter, an overview of current methods for probing RNA secondary structure is provided, including modern high-throughput methods. Methods for guiding secondary structure prediction algorithms using these data are explained, and best practices for using these data are provided. This chapter concludes by listing a number of open questions about how to best use probing data, and what these data can provide.

Viral IRES Prediction System - a Web Server for Prediction of the IRES Secondary Structure In Silico

The internal ribosomal entry site (IRES) functions as cap-independent translation initiation sites in eukaryotic cells. IRES elements have been applied as useful tools for bi-cistronic expression vectors. Current RNA structure prediction programs are unable to predict precisely the potential IRES element. We have designed a viral IRES prediction system (VIPS) to perform the IRES secondary structure prediction. In order to obtain better results for the IRES prediction, the VIPS can evaluate and predict for all four different groups of IRESs with a higher accuracy. RNA secondary structure prediction, comparison, and pseudoknot prediction programs were implemented to form the three-stage procedure for the VIPS. The backbone of VIPS includes: the RNAL fold program, aimed to predict local RNA secondary structures by minimum free energy method; the RNA Align program, intended to compare predicted structures; and pknotsRG program, used to calculate the pseudoknot structure. VIPS was evaluated by using UTR database, IRES database and Virus database, and the accuracy rate of VIPS was assessed as 98.53%, 90.80%, 82.36% and 80.41% for IRES groups 1, 2, 3, and 4, respectively. This advance useful search approach for IRES structures will facilitate IRES related studies. The VIPS on-line website service is available at http://140.135.61.250/vips/.

Enhancement of accuracy and efficiency for RNA secondary structure prediction by sequence segmentation and MapReduce

BackgroundRibonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation. Their secondary structures are crucial for the RNA functionality, and the prediction of the secondary structures is widely studied. Our previous research shows that cutting long sequences into shorter chunks, predicting secondary structures of the chunks independently using thermodynamic methods, and reconstructing the entire secondary structure from the predicted chunk structures can yield better accuracy than predicting the secondary structure using the RNA sequence as a whole. The chunking, prediction, and reconstruction processes can use different methods and parameters, some of which produce more accurate predictions than others. In this paper, we study the prediction accuracy and efficiency of three different chunking methods using seven popular secondary structure prediction programs that apply to two datasets of RNA with known secondary structures, which include both pseudoknotted and non-pseudoknotted sequences, as well as a family of viral genome RNAs whose structures have not been predicted before. Our modularized MapReduce framework based on Hadoop allows us to study the problem in a parallel and robust environment.ResultsOn average, the maximum accuracy retention values are larger than one for our chunking methods and the seven prediction programs over 50 non-pseudoknotted sequences, meaning that the secondary structure predicted using chunking is more similar to the real structure than the secondary structure predicted by using the whole sequence. We observe similar results for the 23 pseudoknotted sequences, except for the NUPACK program using the centered chunking method. The performance analysis for 14 long RNA sequences from the Nodaviridae virus family outlines how the coarse-grained mapping of chunking and predictions in the MapReduce framework exhibits shorter turnaround times for short RNA sequences. However, as the lengths of the RNA sequences increase, the fine-grained mapping can surpass the coarse-grained mapping in performance.ConclusionsBy using our MapReduce framework together with statistical analysis on the accuracy retention results, we observe how the inversion-based chunking methods can outperform predictions using the whole sequence. Our chunk-based approach also enables us to predict secondary structures for very long RNA sequences, which is not feasible with traditional methods alone.

Membrane undulation induced by NS4A of Dengue virus: a molecular dynamics simulation study

Nonstructural protein 4A (NS4A) of Dengue virus (DENV) is a membrane protein involved in rearrangements of the endoplasmic reticulum membrane that are required for formation of replication vesicles. NS4A is composed most likely of three membrane domains. The N- and C-terminal domains are supposed to traverse the lipid membrane whereas the central one is thought to reside on the membrane surface, thus forming a u-shaped protein. All three membrane domains are proposed to be helical by secondary structure prediction programs. After performing multi nanosecond molecular dynamics (MD) simulations at various temperatures (300, 310, and 315.15 K) with each of the individual domains, they are used in a docking approach to define putative association motifs of the transmembrane domains (TMDs). Two structures of the u-shaped protein are generated by separating two assembled TMDs linking them with the membrane-attached domain. Lipid undulation is monitored with the structures embedded in a fully hydrated lipid bilayer applying multiple 200 ns MD simulations at 310 K. An intact structure of the protein supports membrane undulation. The strong unwinding of the helices in the domain-linking section of one of the structures lowers its capability to induce membrane curvature. Unwinding of the link region is due to interactions of two tryptophan residues, Trp-96 and 104. These results provide first insights into the membrane-altering properties of DENV NS4A.

Abstract 023: Development Of Molecular Beacons Targeting Atrial Natriuretic Factor Mrna For Live Cell Sorting

Introduction: Human embryonic stem cell (hESC) differentiation protocols generate both working and nodal cardiomyocytes, however this mixed cell population is not ideal for clinical applications. No surface markers have been used for the separation of live nodal or working cells to date. Molecular beacons are hairpin oligonucleotide probes with a reporter fluorophore at one end and a quencher at the other; they are designed to fluoresce when hybridized to a complementary target and can be used to detect mRNA in live cells. Atrial natriuretic factor (ANF) is expressed significantly more in working cardiomyocytes than nodal cardiomyocytes. We hypothesize that molecular beacons targeting ANF mRNA may be used to separate working cells from differentiated hESCs. Methods and Results: We designed molecular beacons to target regions of ANF mRNA predicted to be accessible using RNA secondary structure prediction programs. We then developed a positive control cell line using chinese hamster ovary (CHO) cells to select functional beacons. CHO cells were transfected with a plasmid containing the ANF gene and puromycin resistance. Transfected cells were sorted into separate wells and clonal populations were exposed to puromycin for 2 weeks. Surviving colonies were assayed for ANF expression and used as a positive control for beacon characterization. We used this system to screen 10 molecular beacons targeting ANF and found 3 beacons that increased in fluorescence in more than 60% of ANF-CHO cells but less than 5% of untransfected cells. To examine whether molecular beacons are suitable for differentiated cultures, we carried out cardiomyocyte differentiation from hESCs using growth factors. ANF expression was up-regulated in differentiated cultures compared with undifferentiated cells. Transfection of differentiated hESCs using the Neon system was optimized using fluorophore labeled oligonucleotides, a positive control for beacon delivery. Studies are currently underway to determine ANF beacon sensitivity and specificity in hESCs. Conclusions: We have developed several molecular beacons with potential for the use of separating live cells based on the expression of ANF mRNA which could represent a powerful technique for stem cell sorting.

Conserved Structural Domains in FoxD4L1, a Neural Forkhead Box Transcription Factor, Are Required to Repress or Activate Target Genes

FoxD4L1 is a forkhead transcription factor that expands the neural ectoderm by down-regulating genes that promote the onset of neural differentiation and up-regulating genes that maintain proliferative neural precursors in an immature state. We previously demonstrated that binding of Grg4 to an Eh-1 motif enhances the ability of FoxD4L1 to down-regulate target neural genes but does not account for all of its repressive activity. Herein we analyzed the protein sequence for additional interaction motifs and secondary structure. Eight conserved motifs were identified in the C-terminal region of fish and frog proteins. Extending the analysis to mammals identified a high scoring motif downstream of the Eh-1 domain that contains a tryptophan residue implicated in protein-protein interactions. In addition, secondary structure prediction programs predicted an α-helical structure overlapping with amphibian-specific Motif 6 in Xenopus, and similarly located α-helical structures in other vertebrate FoxD proteins. We tested functionality of this site by inducing a glutamine-to-proline substitution expected to break the predicted α-helical structure; this significantly reduced FoxD4L1’s ability to repress zic3 and irx1. Because this mutation does not interfere with Grg4 binding, these results demonstrate that at least two regions, the Eh-1 motif and a more C-terminal predicted α-helical/Motif 6 site, additively contribute to repression. In the N-terminal region we previously identified a 14 amino acid motif that is required for the up-regulation of target genes. Secondary structure prediction programs predicted a short β-strand separating two acidic domains. Mutant constructs show that the β-strand itself is not required for transcriptional activation. Instead, activation depends upon a glycine residue that is predicted to provide sufficient flexibility to bring the two acidic domains into close proximity. These results identify conserved predicted motifs with secondary structures that enable FoxD4L1 to carry out its essential functions as both a transcriptional repressor and activator of neural genes.

Predictive Understanding of RNA Dynamic Behavior: Bringing Order to Disorder

We describe how simple topological constraints can enable the quantitative description of RNA global structure and dynamics based on secondary structure. In particular, the topology of helix‐junction‐helix motifs encodes tunable restrictions on the orientation of helices, providing a basis for narrowing the range of conformations that can be sampled robustly by a given RNA. We also describe new NMR data showing that RNA molecules often adopt alternative secondary structures that exist in very low abundance (populations <10%) and for very short periods of time (lifetimes as low as microseconds). These transient states of RNA can also be predicted based on secondary structure prediction programs. Finally, we describe efforts towards engineering RNAs with prescribed dynamic properties.