RNA folding pathway functional intermediates: their prediction and analysis

Kinetic pathway of HIV-1 TAR cotranscriptional folding

The Trans-Activator Receptor (TAR) RNA, located at the 5'-end untranslated region (5' UTR) of the human immunodeficiency virus type 1 (HIV-1), is pivotal in the virus's life cycle. As the initial functional domain, it folds during the transcription of viral mRNA. Although TAR's role in recruiting the Tat protein for trans-activation is established, the detailed kinetic mechanisms at play during early transcription, especially at points of temporary transcriptional pausing, remain elusive. Moreover, the precise physical processes of transcriptional pause and subsequent escape are not fully elucidated. This study focuses on the folding kinetics of TAR and the biological implications by integrating computer simulations of RNA folding during transcription with nuclear magnetic resonance (NMR) spectroscopy data. The findings reveal insights into the folding mechanism of a non-native intermediate that triggers transcriptional pause, along with different folding pathways leading to transcriptional pause and readthrough. The profiling of the cotranscriptional folding pathway and identification of kinetic structural intermediates reveal a novel mechanism for viral transcriptional regulation, which could pave the way for new antiviral drug designs targeting kinetic cotranscriptional folding pathways in viral RNAs.

Landscape Zooming toward the Prediction of RNA Cotranscriptional Folding

RNA molecules fold as they are transcribed. Cotranscriptional folding of RNA plays a critical role in RNA functions in vivo. Present computational strategies focus on simulations where large structural changes may not be completely sampled. Here, we describe an alternative approach to predicting cotranscriptional RNA folding by zooming in and out of the RNA folding energy landscape. By classifying the RNA structural ensemble into "partitions" based on long, stable helices, we zoom out of the landscape and predict the overall slow folding kinetics from the interpartition kinetic network, and for each interpartition transition, we zoom in on the landscape to simulate the kinetics. Applications of the model to the 117-nucleotide E. coli SRP RNA and the 59-nucleotide HIV-1 TAR RNA show agreements with the experimental data and new structural and kinetic insights into biologically significant conformational switches and pathways for these important systems. This approach, by zooming in/out of an RNA folding landscape at different resolutions, might allow us to treat large RNAs in vivo with transcriptional pause, transcription speed, and other in vivo effects.

Prediction of the RNA Secondary Structure Using a Multi-Population Assisted Quantum Genetic Algorithm

Quantum-inspired genetic algorithms (QGAs) were recently introduced for the prediction of RNA secondary structures, and they showed some superiority over the existing popular strategies. In this paper, for RNA secondary structure prediction, we introduce a new QGA named multi-population assisted quantum genetic algorithm (MAQGA). In contrast to the existing QGAs, our strategy involves multi-populations which evolve together in a cooperative way in each iteration, and the genetic exchange between various populations is performed by an operator transfer operation. The numerical results show that the performances of existing genetic algorithms (evolutionary algorithms [EAs]), including traditional EAs and QGAs, can be significantly improved by using our approach. Moreover, for RNA sequences with middle-short length, the MAQGA improves even this state-of-the-art software in terms of both prediction accuracy and sensitivity.

Predicting Cotranscriptional Folding Kinetics For Riboswitch

On the basis of a helix-based transition rate model, we developed a new method for sampling cotranscriptional RNA conformational ensemble and the prediction of cotranscriptional folding kinetics. Applications to E. coli. SRP RNA and pbuE riboswitch indicate that the model may provide reliable predictions for the cotranscriptional folding pathways and population kinetics. For E. coli. SRP RNA, the predicted population kinetics and the folding pathway are consistent with the SHAPE profiles in the recent cotranscriptional SHAPE-seq experiments. For the pbuE riboswitch, the model predicts the transcriptional termination efficiency as a function of the force. The theoretical results show (a) a force-induced transition from the aptamer (antiterminator) to the terminator structure and (b) the different folding pathways for the riboswitch with and without the ligand (adenine). More specifically, without adenine, the aptamer structure emerges as a short-lived kinetic transient state instead of a thermodynamically stable intermediate state. Furthermore, from the predicted extension-time curves, the model identifies a series of conformational switches in the pulling process, where the predicted relative residence times for the different structures are in accordance with the experimental data. The model may provide a new tool for quantitative predictions of cotranscriptional folding kinetics, and results can offer useful insights into cotranscriptional folding-related RNA functions such as regulation of gene expression with riboswitches.

Comparative Visualization of the RNA Suboptimal Conformational Ensemble In Vivo

When a ribonucleic acid (RNA) molecule folds, it often does not adopt a single, well-defined conformation. The folding energy landscape of an RNA is highly dependent on its nucleotide sequence and molecular environment. Cellular molecules sometimes alter the energy landscape, thereby changing the ensemble of likely low-energy conformations. The effects of these energy landscape changes on the conformational ensemble are particularly challenging to visualize for large RNAs. We have created a robust approach for visualizing the conformational ensemble of RNAs that is well suited for in vitro versus in vivo comparisons. Our method creates a stable map of conformational space for a given RNA sequence. We first identify single point mutations in the RNA that maximally sample suboptimal conformational space based on the ensemble’s partition function. Then, we cluster these diverse ensembles to identify the most diverse partition functions for Boltzmann stochastic sampling. By using, to our knowledge, a novel nestedness distance metric, we iteratively add mutant suboptimal ensembles to converge on a stable 2D map of conformational space. We then compute the selective 2′ hydroxyl acylation by primer extension (SHAPE)-directed ensemble for the RNA folding under different conditions, and we project these ensembles on the map to visualize. To validate our approach, we established a conformational map of the Vibrio vulnificus add adenine riboswitch that reveals five classes of structures. In the presence of adenine, projection of the SHAPE-directed sampling correctly identified the on-conformation; without the ligand, only off-conformations were visualized. We also collected the whole-transcript in vitro and in vivo SHAPE-MaP for human β-actin messenger RNA that revealed similar global folds in both conditions. Nonetheless, a comparison of in vitro and in vivo data revealed that specific regions exhibited significantly different SHAPE-MaP profiles indicative of structural rearrangements, including rearrangement consistent with binding of the zipcode protein in a region distal to the stop codon.

Network Properties of the Ensemble of RNA Structures

We describe the first dynamic programming algorithm that computes the expected degree for the network, or graph G = (V, E) of all secondary structures of a given RNA sequence a = a₁, …, a_n. Here, the nodes V correspond to all secondary structures of a, while an edge exists between nodes s, t if the secondary structure t can be obtained from s by adding, removing or shifting a base pair. Since secondary structure kinetics programs implement the Gillespie algorithm, which simulates a random walk on the network of secondary structures, the expected network degree may provide a better understanding of kinetics of RNA folding when allowing defect diffusion, helix zippering, and related conformation transformations. We determine the correlation between expected network degree, contact order, conformational entropy, and expected number of native contacts for a benchmarking dataset of RNAs. Source code is available at http://bioinformatics.bc.edu/clotelab/RNAexpNumNbors.

Computational prediction of riboswitches

Riboswitches present a ubiquitous genetic regulatory mechanism for prokaryotes and have been found in HIV1, fungi, plants, and even H. sapiens. We present an overview of approaches to predict riboswitch aptamers and, more generally, RNA conformational switches.

Fast, approximate kinetics of RNA folding

In this article, we introduce the software suite Hermes, which provides fast, novel algorithms for RNA secondary structure kinetics. Using the fast Fourier transform to efficiently compute the Boltzmann probability that a secondary structure S of a given RNA sequence has base pair distance x (resp. y) from reference structure A (resp. B), Hermes computes the exact kinetics of folding from A to B in this coarse-grained model. In particular, Hermes computes the mean first passage time from the transition probability matrix by using matrix inversion, and also computes the equilibrium time from the rate matrix by using spectral decomposition. Due to the model granularity and the speed of Hermes, it is capable of determining secondary structure refolding kinetics for large RNA sequences, beyond the range of other methods. Comparative benchmarking of Hermes with other methods indicates that Hermes provides refolding kinetics of accuracy suitable for use in the computational design of RNA, an important area of synthetic biology. Source code and documentation for Hermes are available.

MPGAfold in dengue secondary structure prediction

This chapter presents the computational prediction of the secondary structures within the 5' and 3' untranslated regions of the dengue virus serotype 2 (DENV2), with the focus on the conformational prediction of the two dumbbell-like structures, 5' DB and 3' DB, found in the core region of the 3' untranslated region of DENV2. For secondary structure prediction purposes we used a 719 nt-long subgenomic RNA construct from DENV2, which we refer to as the minigenome. The construct combines the 5'-most 226 nt from the 5' UTR and a fragment of the capsid coding region with the last 42 nt from the non-structural protein NS5 coding region and the 451 nt of the 3' UTR. This minigenome has been shown to contain the elements needed for translation, as well as negative strand RNA synthesis. We present the Massively Parallel Genetic Algorithm MPGAfold, a non-deterministic algorithm, that was used to predict the secondary structures of the DENV2 719 nt long minigenome construct, as well as our computational workbench called StructureLab that was used to interactively explore the solution spaces produced by MPGAfold. The MPGAfold algorithm is first introduced at the conceptual level. Then specific parameters guiding its performance are discussed and illustrated with a representative selection of the results from the study. Plots of the solution spaces generated by MPGAfold illustrate the algorithm, while selected secondary structures focus on variable formation of the dumbbell structures and other identified structural motifs. They also serve as illustrations of some of the capabilities of the StructureLab workbench. Results of the computational structure determination calculations are discussed and compared to the experimental data.

Structural complexity of Dengue virus untranslated regions: cis-acting RNA motifs and pseudoknot interactions modulating functionality of the viral genome

The Dengue virus (DENV) genome contains multiple cis-acting elements required for translation and replication. Previous studies indicated that a 719-nt subgenomic minigenome (DENV-MINI) is an efficient template for translation and (−) strand RNA synthesis in vitro. We performed a detailed structural analysis of DENV-MINI RNA, combining chemical acylation techniques, Pb²⁺ ion-induced hydrolysis and site-directed mutagenesis. Our results highlight protein-independent 5′–3′ terminal interactions involving hybridization between recognized cis-acting motifs. Probing analyses identified tandem dumbbell structures (DBs) within the 3′ terminus spaced by single-stranded regions, internal loops and hairpins with embedded GNRA-like motifs. Analysis of conserved motifs and top loops (TLs) of these dumbbells, and their proposed interactions with downstream pseudoknot (PK) regions, predicted an H-type pseudoknot involving TL1 of the 5′ DB and the complementary region, PK2. As disrupting the TL1/PK2 interaction, via ‘flipping’ mutations of PK2, previously attenuated DENV replication, this pseudoknot may participate in regulation of RNA synthesis. Computer modeling implied that this motif might function as autonomous structural/regulatory element. In addition, our studies targeting elements of the 3′ DB and its complementary region PK1 indicated that communication between 5′–3′ terminal regions strongly depends on structure and sequence composition of the 5′ cyclization region.

RNA secondary structure prediction using soft computing

Prediction of RNA structure is invaluable in creating new drugs and understanding genetic diseases. Several deterministic algorithms and soft computing-based techniques have been developed for more than a decade to determine the structure from a known RNA sequence. Soft computing gained importance with the need to get approximate solutions for RNA sequences by considering the issues related with kinetic effects, cotranscriptional folding, and estimation of certain energy parameters. A brief description of some of the soft computing-based techniques, developed for RNA secondary structure prediction, is presented along with their relevance. The basic concepts of RNA and its different structural elements like helix, bulge, hairpin loop, internal loop, and multiloop are described. These are followed by different methodologies, employing genetic algorithms, artificial neural networks, and fuzzy logic. The role of various metaheuristics, like simulated annealing, particle swarm optimization, ant colony optimization, and tabu search is also discussed. A relative comparison among different techniques, in predicting 12 known RNA secondary structures, is presented, as an example. Future challenging issues are then mentioned.

Identification of cis-acting elements in the 3'-untranslated region of the dengue virus type 2 RNA that modulate translation and replication

Using the massively parallel genetic algorithm for RNA folding, we show that the core region of the 3'-untranslated region of the dengue virus (DENV) RNA can form two dumbbell structures (5'- and 3'-DBs) of unequal frequencies of occurrence. These structures have the propensity to form two potential pseudoknots between identical five-nucleotide terminal loops 1 and 2 (TL1 and TL2) and their complementary pseudoknot motifs, PK2 and PK1. Mutagenesis using a DENV2 replicon RNA encoding the Renilla luciferase reporter indicated that all four motifs and the conserved sequence 2 (CS2) element within the 3'-DB are important for replication. However, for translation, mutation of TL1 alone does not have any effect; TL2 mutation has only a modest effect in translation, but translation is reduced by ∼60% in the TL1/TL2 double mutant, indicating that TL1 exhibits a cooperative synergy with TL2 in translation. Despite the variable contributions of individual TL and PK motifs in translation, WT levels are achieved when the complementarity between TL1/PK2 and TL2/PK1 is maintained even under conditions of inhibition of the translation initiation factor 4E function mediated by LY294002 via a noncanonical pathway. Taken together, our results indicate that the cis-acting RNA elements in the core region of DENV2 RNA that include two DB structures are required not only for RNA replication but also for optimal translation.

Computing the partition function for kinetically trapped RNA secondary structures

An RNA secondary structure is locally optimal if there is no lower energy structure that can be obtained by the addition or removal of a single base pair, where energy is defined according to the widely accepted Turner nearest neighbor model. Locally optimal structures form kinetic traps, since any evolution away from a locally optimal structure must involve energetically unfavorable folding steps. Here, we present a novel, efficient algorithm to compute the partition function over all locally optimal secondary structures of a given RNA sequence. Our software, RNAlocopt runs in time and space. Additionally, RNAlocopt samples a user-specified number of structures from the Boltzmann subensemble of all locally optimal structures. We apply RNAlocopt to show that (1) the number of locally optimal structures is far fewer than the total number of structures – indeed, the number of locally optimal structures approximately equal to the square root of the number of all structures, (2) the structural diversity of this subensemble may be either similar to or quite different from the structural diversity of the entire Boltzmann ensemble, a situation that depends on the type of input RNA, (3) the (modified) maximum expected accuracy structure, computed by taking into account base pairing frequencies of locally optimal structures, is a more accurate prediction of the native structure than other current thermodynamics-based methods. The software RNAlocopt constitutes a technical breakthrough in our study of the folding landscape for RNA secondary structures. For the first time, locally optimal structures (kinetic traps in the Turner energy model) can be rapidly generated for long RNA sequences, previously impossible with methods that involved exhaustive enumeration. Use of locally optimal structure leads to state-of-the-art secondary structure prediction, as benchmarked against methods involving the computation of minimum free energy and of maximum expected accuracy. Web server and source code available at http://bioinformatics.bc.edu/clotelab/RNAlocopt/.

Computational approaches to 3D modeling of RNA

Many exciting discoveries have recently revealed the versatility of RNA and its importance in a variety of functions within the cell. Since the structural features of RNA are of major importance to their biological function, there is much interest in predicting RNA structure, either in free form or in interaction with various ligands, including proteins, metabolites and other molecules. In recent years, an increasing number of researchers have developed novel RNA algorithms for predicting RNA secondary and tertiary structures. In this review, we describe current experimental and computational advances and discuss recent ideas that are transforming the traditional view of RNA folding. To evaluate the performance of the most recent RNA 3D folding algorithms, we provide a comparative study in order to test the performance of available 3D structure prediction algorithms for an RNA data set of 43 structures of various lengths and motifs. We find that the algorithms vary widely in terms of prediction quality across different RNA lengths and topologies; most predictions have very large root mean square deviations from the experimental structure. We conclude by outlining some suggestions for future RNA folding research.

CyloFold: secondary structure prediction including pseudoknots

Computational RNA secondary structure prediction approaches differ by the way RNA pseudoknot interactions are handled. For reasons of computational efficiency, most approaches only allow a limited class of pseudoknot interactions or are not considering them at all. Here we present a computational method for RNA secondary structure prediction that is not restricted in terms of pseudoknot complexity. The approach is based on simulating a folding process in a coarse-grained manner by choosing helices based on established energy rules. The steric feasibility of the chosen set of helices is checked during the folding process using a highly coarse-grained 3D model of the RNA structures. Using two data sets of 26 and 241 RNA sequences we find that this approach is competitive compared to the existing RNA secondary structure prediction programs pknotsRG, HotKnots and UnaFold. The key advantages of the new method are that there is no algorithmic restriction in terms of pseudoknot complexity and a test is made for steric feasibility.

AVAILABILITY

The program is available as web server at the site: http://cylofold.abcc.ncifcrf.gov.

Computing folding pathways between RNA secondary structures

Given an RNA sequence and two designated secondary structures A, B, we describe a new algorithm that computes a nearly optimal folding pathway from A to B. The algorithm, RNAtabupath, employs a tabu semi-greedy heuristic, known to be an effective search strategy in combinatorial optimization. Folding pathways, sometimes called routes or trajectories, are computed by RNAtabupath in a fraction of the time required by the barriers program of Vienna RNA Package. We benchmark RNAtabupath with other algorithms to compute low energy folding pathways between experimentally known structures of several conformational switches. The RNApathfinder web server, source code for algorithms to compute and analyze pathways and supplementary data are available at http://bioinformatics.bc.edu/clotelab/RNApathfinder.

An intergenic non-coding rRNA correlated with expression of the rRNA and frequency of an rRNA single nucleotide polymorphism in lung cancer cells

BACKGROUND

Ribosomal RNA (rRNA) is a central regulator of cell growth and may control cancer development. A cis noncoding rRNA (nc-rRNA) upstream from the 45S rRNA transcription start site has recently been implicated in control of rRNA transcription in mouse fibroblasts. We investigated whether a similar nc-rRNA might be expressed in human cancer epithelial cells, and related to any genomic characteristics.

METHODOLOGY/PRINCIPAL FINDINGS

Using quantitative rRNA measurement, we demonstrated that a nc-rRNA is transcribed in human lung epithelial and lung cancer cells, starting from approximately -1000 nucleotides upstream of the rRNA transcription start site (+1) and extending at least to +203. This nc-rRNA was significantly more abundant in the majority of lung cancer cell lines, relative to a nontransformed lung epithelial cell line. Its abundance correlated negatively with total 45S rRNA in 12 of 13 cell lines (P = 0.014). During sequence analysis from -388 to +306, we observed diverse, frequent intercopy single nucleotide polymorphisms (SNPs) in rRNA, with a frequency greater than predicted by chance at 12 sites. A SNP at +139 (U/C) in the 5' leader sequence varied among the cell lines and correlated negatively with level of the nc-rRNA (P = 0.014). Modelling of the secondary structure of the rRNA 5'-leader sequence indicated a small increase in structural stability due to the +139 U/C SNP and a minor shift in local configuration occurrences.

CONCLUSIONS/SIGNIFICANCE

The results demonstrate occurrence of a sense nc-rRNA in human lung epithelial and cancer cells, and imply a role in regulation of the rRNA gene, which may be affected by a +139 SNP in the 5' leader sequence of the primary rRNA transcript.

Analysis of riboswitch structure and function by an energy landscape framework

The thiamine pyrophosphate (TPP) riboswitch employs modular domains for binding TPP to form a platform for gene expression regulation. Specifically, TPP binding triggers a conformational switch in the RNA from a transcriptionally active "on" state to an inactive "off" state that concomitantly causes the formation of a terminator hairpin and halting of transcription. Here, clustering analysis of energy landscapes at different nucleotide lengths suggests a novel computational tool for analysis of the mechanics of transcription elongation in the presence or absence of the ligand. Namely, we suggest that the riboswitch's kinetics are tightly governed by a length-dependent switch, whereby the energy landscape has two clusters available during transcription elongation and where TPP's binding shifts the preference to one form. Significantly, the biologically active and inactive structures determined experimentally matched well the structures predominant in each computational set. These clustering/structural analyses combined with modular computational design suggest design principles that exploit the above features to analyze as well as create new functions and structures of RNA systems.

RNA conformational changes in the life cycles of RNA viruses, viroids, and virus-associated RNAs

The rugged nature of the RNA structural free energy landscape allows cellular RNAs to respond to environmental conditions or fluctuating levels of effector molecules by undergoing dynamic conformational changes that switch on or off activities such as catalysis, transcription or translation. Infectious RNAs must also temporally control incompatible activities and rapidly complete their life cycle before being targeted by cellular defenses. Viral genomic RNAs must switch between translation and replication, and untranslated subviral RNAs must control other activities such as RNA editing or self-cleavage. Unlike well characterized riboswitches in cellular RNAs, the control of infectious RNA activities by altering the configuration of functional RNA domains has only recently been recognized. In this review, we will present some of these molecular rearrangements found in RNA viruses, viroids and virus-associated RNAs, relating how these dynamic regions were discovered, the activities that might be regulated, and what factors or conditions might cause a switch between conformations.

Predicting consensus structures for RNA alignments via pseudo-energy minimization

Thermodynamic processes with free energy parameters are often used in algorithms that solve the free energy minimization problem to predict secondary structures of single RNA sequences. While results from these algorithms are promising, an observation is that single sequence-based methods have moderate accuracy and more information is needed to improve on RNA secondary structure prediction, such as covariance scores obtained from multiple sequence alignments. We present in this paper a new approach to predicting the consensus secondary structure of a set of aligned RNA sequences via pseudo-energy minimization. Our tool, called RSpredict, takes into account sequence covariation and employs effective heuristics for accuracy improvement. RSpredict accepts, as input data, a multiple sequence alignment in FASTA or ClustalW format and outputs the consensus secondary structure of the input sequences in both the Vienna style Dot Bracket format and the Connectivity Table format. Our method was compared with some widely used tools including KNetFold, Pfold and RNAalifold. A comprehensive test on different datasets including Rfam sequence alignments and a multiple sequence alignment obtained from our study on the Drosophila X chromosome reveals that RSpredict is competitive with the existing tools on the tested datasets. RSpredict is freely available online as a web server and also as a jar file for download at http://datalab.njit.edu/biology/RSpredict.

The fraction of RNA that folds into the correct branched secondary structure determines hepatitis delta virus type 3 RNA editing levels

RNA editing by the host RNA adenosine deaminase ADAR1 at the amber/W site of hepatitis delta virus RNA plays a central role in the viral replication cycle by affecting the balance between viral RNA synthesis and packaging. Previously, we found that HDV genotype III (HDV-3) RNA can form two secondary structures following transcription: an unbranched rod structure, which is characteristic of HDV, and a metastable branched structure that serves as the substrate for editing. The unstable nature of the branched editing substrate structure raised the possibility that structural dynamics of the RNA following transcription could determine the rate at which editing occurs. Here, editing and its control are examined in two HDV-3 isolates, from Peru and Ecuador. Analysis of editing in vitro by ADAR1 indicated that the branched structure formed by RNA derived from the Peruvian isolate is edited more efficiently than that from the Ecuadorian isolate. In contrast, in the context of replication, Peruvian RNA is edited less efficiently than RNA containing Ecuadorian sequences. Computational analyses of RNA folding using the massively parallel genetic algorithm (MPGAfold) indicated that the Peruvian RNA is less likely to form the branched structure required for editing than the Ecuadorian isolate. This difference was confirmed by in vitro transcription of these RNAs. Overall, our data indicate that HDV-3 controls RNA editing levels via (1) the fraction of the RNA that folds, during transcription, into the metastable branched structure required for editing and (2) the efficiency with which ADAR1 edits this branched substrate RNA.

Structural plasticity and rapid evolution in a viral RNA revealed by in vivo genetic selection

Satellite RNAs usually lack substantial homology with their helper viruses. The 356-nucleotide satC of Turnip crinkle virus (TCV) is unusual in that its 3'-half shares high sequence similarity with the TCV 3' end. Computer modeling, structure probing, and/or compensatory mutagenesis identified four hairpins and three pseudoknots in this TCV region that participate in replication and/or translation. Two hairpins and two pseudoknots have been confirmed as important for satC replication. One portion of the related 3' end of satC that remains poorly characterized corresponds to juxtaposed TCV hairpins H4a and H4b and pseudoknot psi(3), which are required for the TCV-specific requirement of translation (V. A. Stupina et al., RNA 14:2379-2393, 2008). Replacement of satC H4a with randomized sequence and scoring for fitness in plants by in vivo genetic selection (SELEX) resulted in winning sequences that contain an H4a-like stem-loop, which can have additional upstream sequence composing a portion of the stem. SELEX of the combined H4a and H4b region in satC generated three distinct groups of winning sequences. One group models into two stem-loops similar to H4a and H4b of TCV. However, the selected sequences in the other two groups model into single hairpins. Evolution of these single-hairpin SELEX winners in plants resulted in satC that can accumulate to wild-type (wt) levels in protoplasts but remain less fit in planta when competed against wt satC. These data indicate that two highly distinct RNA conformations in the H4a and H4b region can mediate satC fitness in protoplasts.

Structural domains within the 3' untranslated region of Turnip crinkle virus

The genomes of positive-strand RNA viruses undergo conformational shifts that complicate efforts to equate structures with function. We have initiated a detailed analysis of secondary and tertiary elements within the 3' end of Turnip crinkle virus (TCV) that are required for viral accumulation in vivo. MPGAfold, a massively parallel genetic algorithm, suggested the presence of five hairpins (H4a, H4b, and previously identified hairpins H4, H5, and Pr) and one H-type pseudoknot (Psi(3)) within the 3'-terminal 194 nucleotides (nt). In vivo compensatory mutagenesis analyses confirmed the existence of H4a, H4b, Psi(3) and a second pseudoknot (Psi(2)) previously identified in a TCV satellite RNA. In-line structure probing of the 194-nt fragment supported the coexistence of H4, H4a, H4b, Psi(3) and a pseudoknot that connects H5 and the 3' end (Psi(1)). Stepwise replacements of TCV elements with the comparable elements from Cardamine chlorotic fleck virus indicated that the complete 142-nt 3' end, and subsets containing Psi(3), H4a, and H4b or Psi(3), H4a, H4b, H5, and Psi(2), form functional domains for virus accumulation in vivo. A new 3-D molecular modeling protocol (RNA2D3D) predicted that H4a, H4b, H5, Psi(3), and Psi(2) are capable of simultaneous existence and bears some resemblance to a tRNA. The related Japanese iris necrotic ring virus does not have comparable domains. These results provide a framework for determining how interconnected elements participate in processes that require 3' untranslated region sequences such as translation and replication.

RNA2D3D: a program for generating, viewing, and comparing 3-dimensional models of RNA

Using primary and secondary structure information of an RNA molecule, the program RNA2D3D automatically and rapidly produces a first-order approximation of a 3-dimensional conformation consistent with this information. Applicable to structures of arbitrary branching complexity and pseudoknot content, it features efficient interactive graphical editing for the removal of any overlaps introduced by the initial generating procedure and for making conformational changes favorable to targeted features and subsequent refinement. With emphasis on fast exploration of alternative 3D conformations, one may interactively add or delete base-pairs, adjacent stems can be coaxially stacked or unstacked, single strands can be shaped to accommodate special constraints, and arbitrary subsets can be defined and manipulated as rigid bodies. Compaction, whereby base stacking within stems is optimally extended into connecting single strands, is also available as a means of strategically making the structures more compact and revealing folding motifs. Subsequent refinement of the first-order approximation, of modifications, and for the imposing of tertiary constraints is assisted with standard energy refinement techniques. Previously determined coordinates for any part of the molecule are readily incorporated, and any part of the modeled structure can be output as a PDB or XYZ file. Illustrative applications in the areas of ribozymes, viral kissing loops, viral internal ribosome entry sites, and nanobiology are presented.

Simulating RNA folding kinetics on approximated energy landscapes

We present a general computational approach to simulate RNA folding kinetics that can be used to extract population kinetics, folding rates and the formation of particular substructures that might be intermediates in the folding process. Simulating RNA folding kinetics can provide unique insight into RNA whose functions are dictated by folding kinetics and not always by nucleotide sequence or the structure of the lowest free-energy state. The method first builds an approximate map (or model) of the folding energy landscape from which the population kinetics are analyzed by solving the master equation on the map. We present results obtained using an analysis technique, map-based Monte Carlo simulation, which stochastically extracts folding pathways from the map. Our method compares favorably with other computational methods that begin with a comprehensive free-energy landscape, illustrating that the smaller, approximate map captures the major features of the complete energy landscape. As a result, our method scales to larger RNAs. For example, here we validate kinetics of RNA of more than 200 nucleotides. Our method accurately computes the kinetics-based functional rates of wild-type and mutant ColE1 RNAII and MS2 phage RNAs showing excellent agreement with experiment.

RnaPredict--an evolutionary algorithm for RNA secondary structure prediction

This paper presents two in-depth studies on RnaPredict, an evolutionary algorithm for RNA secondary structure prediction. The first study is an analysis of the performance of two thermodynamic models, Individual Nearest Neighbor (INN) and Individual Nearest Neighbor Hydrogen Bond (INN-HB). The correlation between the free energy of predicted structures and the sensitivity is analyzed for 19 RNA sequences. Although some variance is shown, there is a clear trend between a lower free energy and an increase in true positive base pairs. With increasing sequence length, this correlation generally decreases. In the second experiment, the accuracy of the predicted structures for these 19 sequences are compared against the accuracy of the structures generated by the mfold dynamic programming algorithm (DPA) and also to known structures. RnaPredict is shown to outperform the minimum free energy structures produced by mfold and has comparable performance when compared to sub-optimal structures produced by mfold.

Protocols for the in silico design of RNA nanostructures

Recent developments in the field of nanobiology have significantly expanded the possibilities for new modalities in the treatment of many diseases, including cancer. Ribonucleic acid (RNA) represents a relatively new molecular material for the development of these biologically oriented nanodevices. In addition, RNA nanobiology presents a relatively new approach for the development of RNA-based nanoparticles that can be used as crystallization substrates and scaffolds for RNA-based nanoarrays. Presented in this chapter are some methodological shaped-based protocols for the design of such RNA nanostructures. Included are descriptions and background materials describing protocols that use a database of three-dimensional RNA structure motifs; designed RNA secondary structure motifs; and a combination of the two approaches. An example is also given illustrating one of the protocols.

Finding a common motif of RNA sequences using genetic programming: the GeRNAMo system

We focus on finding a consensus motif of a set of homologous or functionally related RNA molecules. Recent approaches to this problem have been limited to simple motifs, require sequence alignment, and make prior assumptions concerning the data set. We use genetic programming to predict RNA consensus motifs based solely on the data set. Our system -- dubbed GeRNAMo (Genetic programming of RNA Motifs) -- predicts the most common motifs without sequence alignment and is capable of dealing with any motif size. Our program only requires the maximum number of stems in the motif, and if prior knowledge is available the user can specify other attributes of the motif (e.g., the range of the motif's minimum and maximum sizes), thereby increasing both sensitivity and speed. We describe several experiments using either ferritin iron response element (IRE); signal recognition particle (SRP); or microRNA sequences, showing that the most common motif is found repeatedly, and that our system offers substantial advantages over previous methods.

Computational design of an RNA hexagonal nanoring and an RNA nanotube

The combination of computer modeling, RNA structure versatility, and siRNA function can be efficiently used to design an all-RNA nanoparticle capable of siRNA delivery. Here, we present a computational design of an RNA nanoring and a nanotube. An RNA nanoring consists of six simple linear building blocks that are assembled together via known noncovalent loop-loop contacts based on RNAI/RNAII inverse sequences. The helical sequences of the building blocks can include siRNAs for drug delivery.

RADAR: a web server for RNA data analysis and research

RADAR is a web server that provides a multitude of functionality for RNA data analysis and research. It can align structure-annotated RNA sequences so that both sequence and structure information are taken into consideration during the alignment process. This server is capable of performing pairwise structure alignment, multiple structure alignment, database search and clustering. In addition, RADAR provides two salient features: (i) constrained alignment of RNA secondary structures, and (ii) prediction of the consensus structure for a set of RNA sequences. RADAR will be able to assist scientists in performing many important RNA mining operations, including the understanding of the functionality of RNA sequences, the detection of RNA structural motifs and the clustering of RNA molecules, among others. The web server together with a software package for download is freely accessible at http://datalab.njit.edu/biodata/rna/RSmatch/server.htm and http://www.ccrnp.ncifcrf.gov/~bshapiro/

Distinct contribution of electrostatics, initial conformational ensemble, and macromolecular stability in RNA folding

We distinguish the contribution of the electrostatic environment, initial conformational ensemble, and macromolecular stability on the folding mechanism of a large RNA using a combination of time-resolved "Fast Fenton" hydroxyl radical footprinting and exhaustive kinetic modeling. This integrated approach allows us to define the folding landscape of the L-21 Tetrahymena thermophila group I intron structurally and kinetically from its earliest steps with unprecedented accuracy. Distinct parallel pathways leading the RNA to its native form upon its Mg(2+)-induced folding are observed. The structures of the intermediates populating the pathways are not affected by variation of the concentration and type of background monovalent ions (electrostatic environment) but are altered by a mutation that destabilizes one domain of the ribozyme. Experiments starting from different conformational ensembles but folding under identical conditions show that whereas the electrostatic environment modulates molecular flux through different pathways, the initial conformational ensemble determines the partitioning of the flux. This study showcases a robust approach for the development of kinetic models from collections of local structural probes.

Bridging the gap in RNA structure prediction

The field of RNA structure prediction has experienced significant advances in the past several years, thanks to the availability of new experimental data and improved computational methodologies. These methods determine RNA secondary structures and pseudoknots from sequence alignments, thermodynamics-based dynamic programming algorithms, genetic algorithms and combined approaches. Computational RNA three-dimensional modeling uses this information in conjunction with manual manipulation, constraint satisfaction methods, molecular mechanics and molecular dynamics. The ultimate goal of automatically producing RNA three-dimensional models from given secondary and tertiary structure data, however, is still not fully realized. Recent developments in the computational prediction of RNA structure have helped bridge the gap between RNA secondary structure prediction, including pseudoknots, and three-dimensional modeling of RNA.

A pseudoknot in a preactive form of a viral RNA is part of a structural switch activating minus-strand synthesis

RNA can adopt different conformations in response to changes in the metabolic status of cells, which can regulate processes such as transcription, translation, and RNA cleavage. We previously proposed that an RNA conformational switch in an untranslated satellite RNA (satC) of Turnip crinkle virus (TCV) regulates initiation of minus-strand synthesis (G. Zhang, J. Zhang, A. T. George, T. Baumstark, and A. E. Simon, RNA 12:147-162, 2006). This model was based on the lack of phylogenetically inferred hairpins or a known pseudoknot in the "preactive" structure assumed by satC transcripts in vitro. We now provide evidence that a second pseudoknot (Psi(2)), whose disruption reduces satC accumulation in vivo and enhances transcription by the TCV RNA-dependent RNA polymerase in vitro, stabilizes the preactive satC structure. Alteration of either Psi(2) partner caused nearly identical structural changes, including single-stranded-specific cleavages in the pseudoknot sequences and strong cleavages in a distal element previously proposed to mediate the conformational switch. These results indicate that the preactive structure identified in vitro has biological relevance in vivo and support a requirement for this alternative structure and a conformational switch in high-level accumulation of satC in vivo.

Searching for IRES

The cell has many ways to regulate the production of proteins. One mechanism is through the changes to the machinery of translation initiation. These alterations favor the translation of one subset of mRNAs over another. It was first shown that internal ribosome entry sites (IRESes) within viral RNA genomes allowed the production of viral proteins more efficiently than most of the host proteins. The RNA secondary structure of viral IRESes has sometimes been conserved between viral species even though the primary sequences differ. These structures are important for IRES function, but no similar structure conservation has yet to be shown in cellular IRES. With the advances in mathematical modeling and computational approaches to complex biological problems, is there a way to predict an IRES in a data set of unknown sequences? This review examines what is known about cellular IRES structures, as well as the data sets and tools available to examine this question. We find that the lengths, number of upstream AUGs, and %GC content of 5'-UTRs of the human transcriptome have a similar distribution to those of published IRES-containing UTRs. Although the UTRs containing IRESes are on the average longer, almost half of all 5'-UTRs are long enough to contain an IRES. Examination of the available RNA structure prediction software and RNA motif searching programs indicates that while these programs are useful tools to fine tune the empirically determined RNA secondary structure, the accuracy of de novo secondary structure prediction of large RNA molecules and subsequent identification of new IRES elements by computational approaches, is still not possible.

The role of a metastable RNA secondary structure in hepatitis delta virus genotype III RNA editing

RNA editing plays a critical role in the life cycle of hepatitis delta virus (HDV). The host editing enzyme ADAR1 recognizes specific RNA secondary structure features around the amber/W site in the HDV antigenome and deaminates the amber/W adenosine. A previous report suggested that a branched secondary structure is necessary for editing in HDV genotype III. This branched structure, which is distinct from the characteristic unbranched rod structure required for HDV replication, was only partially characterized, and knowledge concerning its formation and stability was limited. Here, we examine the secondary structures, conformational dynamics, and amber/W site editing of HDV genotype III RNA using a miniaturized HDV genotype III RNA in vitro. Computational analysis of this RNA using the MPGAfold algorithm indicated that the RNA has a tendency to form both metastable and stable unbranched secondary structures. Moreover, native polyacrylamide gel electrophoresis demonstrated that this RNA forms both branched and unbranched rod structures when transcribed in vitro. As predicted, the branched structure is a metastable structure that converts readily to the unbranched rod structure. Only branched RNA was edited at the amber/W site by ADAR1 in vitro. The structural heterogeneity of HDV genotype III RNA is significant because not only are both conformations of the RNA functionally important for viral replication, but the ratio of the two forms could modulate editing by determining the amount of substrate RNA available for modification.

The role of cytokine mRNA stability in the pathogenesis of autoimmune disease

Inflammatory cytokines such as interferon (IFN)-gamma, interleukin (IL)-2, tumor-necrosis factor (TNF)-alpha and IL-17 play an important role in the pathogenesis of cell-mediated autoimmune diseases. Cytokine gene expression is tightly regulated at the post-transcriptional level. Cytokine mRNA decay is dependent not only upon cis-elements in the RNA but also upon trans-acting factors such as the RNA binding proteins TTP, HuR, AUF-1, Nucleolin and YB-1. Physiologic signals, for instance signaling through the CD28 receptor on T cells, can modulate the half-life of a select subset of cytokine mRNAs, such as IL-2. Distinct cis- and trans-acting elements in human and mouse IL-2 mRNA may account for the different pattern of CD28-mediated mRNA stabilization in these two species. TTP-deficient mice or mice with a deletion of the TNF-alpha mRNA ARE element develop a complex inflammatory syndrome that is associated with a prolonged TNF-alpha mRNA half-life and elevated levels of circulating TNF-alpha. This syndrome can be prevented by treatment with TNF-alpha blocking antibodies. Evidence from mice with altered cytokine mRNA stability, along with human data, suggests that imbalance between the stability and decay of inflammatory cytokine mRNAs could represent a basic mechanism leading to autoimmunity.

Graphical exploratory data analysis of RNA secondary structure dynamics predicted by the massively parallel genetic algorithm

Studies indicate that RNA may enter intermediate and multiple conformational states, which may impact gene expression and molecular function. It is known that the biologically functional states of RNA molecules may not correspond to their minimum energy conformations, that kinetic barriers may trap the molecule in a local minimum, that folding often occurs during transcription, and that cases exist in which a molecule will transition between one or more functional conformations. Thus, methods for simulating the folding pathway and dynamic behavior of an RNA molecule are important for the prediction of RNA structure and its associated functions. We have developed several data mining techniques guided by interactive visualization tools associated with our massively parallel genetic algorithm for RNA/DNA secondary structure prediction, MPGAfold, and StructureLab analysis workbench. Most of the methods and tools are also applicable to dynamic programming algorithm (DPA) folding data analysis. When applied to MPGAfold results these methodologies are used to determine the significant intermediate and final structures associated with co-transcriptional and full length RNA folding. Since the genetic algorithm is essentially stochastic, multiple runs are required to develop a consensus understanding of an RNA structure. The interactive visualizations facilitate interpretation of results from sequential or full length individual MPGAfold runs, final results of multiple folding runs, including multiple population sizes, and the results from multiple RNA sequences of one family. This paper describes several of these techniques and shows how they are used to help solve this highly combinatoric problem.

Structural differentiation of the HIV-1 polyA signals

The Human Immunodeficiency Virus Type 1 (HIV-1) encodes the polyadenylation (polyA) signal (AAUAAA) within the highly conserved untranslated region (UTR) at both 5' and 3' terminals of the viral transcript. In polyadenylation, an RNA transcript is cleaved and then elongated with adenine nucleotides while repression of the 5' signal and utilization of the 3' signal occurs. Because experimental studies have yet to analyze the structures of both 5' and 3' signals from a global perspective, other structural conformations involving these signals may exist and could be pivotal to understanding key functional processes. To distinguish the differential regulation of the 5' and 3' polyA signals, we studied the structural tendencies of both the 5' and 3' UTR in HIV-1. Through computational folding predictions of multiple HIV-1 strains using the Massively Parallel Genetic Algorithm (MPGAfold) capable of dynamically elucidating key alternative conformations, the 5' polyA signal was found to be dominantly occluded in a hairpin loop while the 3' polyA signal showed variability between hairpin and linear conformations with a propensity for the linear structure with an asymmetric internal loop. Furthermore, the energies and predictions of these structures indicate that the polyA signals have some metastable characteristics indicating an ability to switch into different conformations that can regulate viral function.

A base-specific recognition signal in the 5' consensus sequence of rotavirus plus-strand RNAs promotes replication of the double-stranded RNA genome segments

Replication of the segmented double-stranded (ds)RNA genome of rotavirus requires the viral RNA-dependent RNA polymerase (RdRP) to use 11 different (+)RNAs as templates for (-) strand synthesis. Complementary sequences proximal to the 5' and 3' termini are predicted to direct cyclization of the (+)RNAs by forming panhandle structures from which short highly conserved terminal sequences protrude as single-stranded tails. Cell-free replication assays indicate that such structural organization of the 5'- and 3'-ends is required for efficient dsRNA synthesis. Multiple specifically recognized elements exist at the 3'-end that promote dsRNA synthesis including RdRP-recruitment signals and a (-) strand initiation sequence. In contrast to the 3'-end, the role of the 5'-end has been less well defined. In this study, we determined that the 5'-end contains a base-specific recognition signal that plays an important role in the assembly of the RdRP and cofactors into a stable initiation complex for (-) strand synthesis. The 5' recognition signal is associated with the G2 residue of the 5'-consensus sequence, a residue that shows absolute conservation among all rotavirus groups (A, B, and C) examined to date. From our results, we suggest that rotavirus (+)RNA cyclization, although likely initiated by 5'- 3' nucleotide complementarity, may be stabilized by RdRP-dependent bridging. Given that synthesis of the (-) strand on the (+)RNA template will disrupt 5'-3' nucleotide interactions, RdRP-dependent bridging may be the sole mechanism by which the dsRNA product can be held in the necessary cyclized conformation required for efficient multiple rounds of transcription.

Structural polymorphism of the HIV-1 leader region explored by computational methods

Experimental studies revealed that the elements of the human immunodeficiency virus type 1 (HIV-1) 5′-untranslated leader region (5′-UTR) can fold in vitro into two alternative conformations, branched (BMH) and ‘linearized’ (LDI) and switch between them to achieve different functionality. In this study we computationally explored in detail, with our massively parallel genetic algorithm (MPGAfold), the propensity of 13 HIV-1 5′-UTRs to fold into the BMH and the LDI conformation types. Besides the BMH conformations these results predict the existence of two functionally equivalent types of LDI conformations. One is similar to what has been shown in vitro to exist in HIV-1 LAI, the other is a novel conformation exemplified by HIV-1 MAL long-distance interactions. These novel MPGAfold results are further corroborated by a consensus probability matrix algorithm applied to a set of 155 HIV-1 sequences. We also have determined in detail the impact of various strain mutations, domain sizes and folds of elongating sequences simulating folding during transcription on HIV-1 RNA secondary structure folding dynamics.

Using motion planning to study RNA folding kinetics

We propose a novel, motion planning based approach to approximately map the energy landscape of an RNA molecule. A key feature of our method is that it provides a sparse map that captures the main features of the energy landscape which can be analyzed to compute folding kinetics. Our method is based on probabilistic roadmap motion planners that we have previously successfully applied to protein folding. In this paper, we provide evidence that this approach is also well suited to RNA. We compute population kinetics and transition rates on our roadmaps using the master equation for a few moderately sized RNA and show that our results compare favorably with results of other existing methods.

Spectral decomposition for the search and analysis of RNA secondary structure

Scales in RNA, based on geometrical considerations, can be exploited for the analysis and prediction of RNA structures. By using spectral decomposition, geometric information that relates to a given RNA fold can be reduced to a single positive scalar number, the second eigenvalue of the Laplacian matrix corresponding to the tree-graph representation of the RNA secondary structure. Along with the free energy of the structure, being the most important scalar number in the prediction of RNA folding by energy minimization methods, the second eigenvalue of the Laplacian matrix can be used as an effective signature for locating a target folded structure given a set of RNA folds. Furthermore, the second eigenvector of the Laplacian matrix can be used to partition large RNA structures into smaller fragments. An illustrative example is given for the use of the second eigenvalue to predict mutations that may cause structural rearrangements, thereby disrupting stable motifs.

Discovery of RNA structural elements using evolutionary computation

RNA molecules fold into characteristic secondary and tertiary structures that account for their diverse functional activities. Many of these RNA structures, or certain structural motifs within them, are thought to recur in multiple genes within a single organism or across the same gene in several organisms and provide a common regulatory mechanism. Search algorithms, such as RNAMotif, can be used to mine nucleotide sequence databases for these repeating motifs. RNAMotif allows users to capture essential features of known structures in detailed descriptors and can be used to identify, with high specificity, other similar motifs within the nucleotide database. However, when the descriptor constraints are relaxed to provide more flexibility, or when there is very little a priori information about hypothesized RNA structures, the number of motif 'hits' may become very large. Exhaustive methods to search for similar RNA structures over these large search spaces are likely to be computationally intractable. Here we describe a powerful new algorithm based on evolutionary computation to solve this problem. A series of experiments using ferritin IRE and SRP RNA stem-loop motifs were used to verify the method. We demonstrate that even when searching extremely large search spaces, of the order of 10(23) potential solutions, we could find the correct solution in a fraction of the time it would have taken for exhaustive comparisons.

Self-induced structural switches in RNA

Many biologically active RNAs show a switch in their secondary structure, which is accompanied by changes in their function. Such changes in secondary structure often require trans-acting factors, e.g. RNA chaperones. However, several biologically active RNAs do not require trans-acting factors for this structural switch, which is therefore indicated here as a "self-induced switch". These self-induced structural switches have several characteristics in common. They all start from a metastable structure, which is maintained for some time allowing or blocking a particular function of the RNA. Hereafter, a structural element becomes available, e.g. during transcription, triggering a rapid transition into a stable conformation, which again is accompanied by either a gain or loss of function. A further common element of this type of switches is the involvement of a branch migration or strand displacement reaction, which lowers the energy barrier of the reaction sufficiently to allow rapid refolding. Here, we review a number of these self-induced switches in RNA secondary structure as proposed for several systems. A general model for this type of switches is presented, showing its importance in the biology of functionally active RNAs.

Molecular dynamics simulations of the denaturation and refolding of an RNA tetraloop

Tetraloops are very abundant structural elements of RNA that are formed by four nucleotides in a hairpin loop which is closed by a double stranded helical stem with some Watson-Crick base pairs. A tetraloop r(GCGAAGGC) was identified from the crystal structure of the central domain of 16S rRNA (727-730) in the Thermus thermophilus 30S ribosomal complex. The crystal structure of the 30S complex includes a total of 104 nucleotides from the central domain of the 16S rRNA and three ribosomal proteins S6, S15 and S18. Independent biochemical experiments have demonstrated that protein S15 plays the role in initiating the formation of the central domain of this complex. In the crystal, the tetraloop interacts with the protein S15 at two sites: one of them is associated with hydrogen bond interactions between residue His50 and nucleotide G730, and the other is associated with the occurrence of residue Arg53 beside A728. This paper uses molecular dynamics (MD) simulations to investigate the protein-dependent structural stability of the tetraloop and demonstrates the folding pathway of this tetraloop via melting denaturation and its subsequent refolding. Three important results are derived from these simulations: (i) The stability of nucleotide A728 appears to be protein dependent. Without the interaction with S15, A728 flips away from stacking with A729. (ii) The melting temperature demonstrated by the simulations is analogous to the results of thermodynamic experiments. In addition, the simulated folding of the tetraloop is stepwise: the native shape of the backbone is formed first; this is then followed by the formation of the Watson- Crick base pairs in the stem; and finally the hydrogen bonds and base stacking in the loop are formed. (iii) The tetraloop structure is similar to the crystal structure at salt concentrations of 0.1 M and 1.0 M used for the simulations, but the refolded structure at 0.1 M salt is more comparable to the crystal structure than at 1.0 M. The results from the simulations using both the Generalized Born continuum model and explicit solvent model (Particle Mesh Ewald) generate a similar pathway for unfolding/refolding of the tetraloop.

Prediction of DNA single-strand conformation polymorphism: analysis by capillary electrophoresis and computerized DNA modeling

We have analyzed previously three representative p53 single-point mutations by capillary-electrophoresis single-strand conformation polymorphism (CE-SSCP). In the current study, we compared our CE-SSCP results with the potential secondary structures predicted by an RNA/DNA-folding algorithm with DNA energy rules, used in conjunction with a computer analysis workbench called STRUCTURELAB. Each of these mutations produces measurable shifts in CE migration times relative to wild type. Using computerized folding analysis, each of the mutations was found to have a conformational difference relative to wild type, which accounts for the observed differences in CE migration. Additional properties exhibited in the CE electropherograms were also explained using the computerized analysis. These include the appearance of secondary peaks and the temperature dependence of the electrophoretic patterns. The results yield insight into the mechanism of SSCP and how the conditions of this measurement, especially temperature, may be optimized to improve the sensitivity of the SSCP method. The results may also impact other diagnostic methods, which would benefit by a better understanding of DNA single-strand conformation polymorphisms to optimize conditions for enzymatic cleavage and DNA hybridization reactions.