Secondary Structure Prediction Tools Research Articles

Prediction of the effects of the top 10 synonymous mutations from 26645 SARS-CoV-2 genomes

Background The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had led to a global pandemic since December 2019. SARS-CoV-2 is a single-stranded RNA virus, which mutates at a higher rate. Multiple works had been done to study nonsynonymous mutations, which change protein sequences. However, there is little study on the effects of SARS-CoV-2 synonymous mutations, which may affect viral fitness. This study aims to predict the effect of synonymous mutations on the SARS-CoV-2 genome. Methods A total of 26645 SARS-CoV-2 genomic sequences retrieved from Global Initiative on Sharing all Influenza Data (GISAID) database were aligned using MAFFT. Then, the mutations and their respective frequency were identified. Multiple RNA secondary structures prediction tools, namely RNAfold, IPknot++ and MXfold2 were applied to predict the effect of the mutations on RNA secondary structure and their base pair probabilities was estimated using MutaRNA. Relative synonymous codon usage (RSCU) analysis was also performed to measure the codon usage bias (CUB) of SARS-CoV-2. Results A total of 150 synonymous mutations were identified. The synonymous mutation identified with the highest frequency is C3037U mutation in the nsp3 of ORF1a. Of these top 10 highest frequency synonymous mutations, C913U, C3037U, U16176C and C18877U mutants show pronounced changes between wild type and mutant in all 3 RNA secondary structure prediction tools, suggesting these mutations may have some biological impact on viral fitness. These four mutations show changes in base pair probabilities. All mutations except U16176C change the codon to a more preferred codon, which may result in higher translation efficiency. Conclusion Synonymous mutations in SARS-CoV-2 genome may affect RNA secondary structure, changing base pair probabilities and possibly resulting in a higher translation rate. However, lab experiments are required to validate the results obtained from prediction analysis.

Comparative Study of Single-stranded Oligonucleotides Secondary Structure Prediction Tools

BackgroundSingle-stranded nucleic acids (ssNAs) have important biological roles and a high biotechnological potential linked to their ability to bind to numerous molecular targets. This depends on the different spatial conformations they can assume. The first level of ssNAs spatial organisation corresponds to their base pairs pattern, i.e. their secondary structure. Many computational tools have been developed to predict the ssNAs secondary structures, making the choice of the appropriate tool difficult, and an up-to-date guide on the limits and applicability of current secondary structure prediction tools is missing. Therefore, we performed a comparative study of the performances of 9 freely available tools (mfold, RNAfold, CentroidFold, CONTRAfold, MC-Fold, LinearFold, UFold, SPOT-RNA, and MXfold2) on a dataset of 538 ssNAs with known experimental secondary structure.ResultsThe minimum free energy-based tools, namely mfold and RNAfold, and some tools based on artificial intelligence, namely CONTRAfold and MXfold2, provided the best results, with sim 50% of exact predictions, whilst MC-fold seemed to be the worst performing tool, with only sim 11% of exact predictions. In addition, UFold and SPOT-RNA are the only options for pseudoknots prediction. Including in the analysis of mfold and RNAfold results 5–10 suboptimal solutions further improved the performances of these tools. Nevertheless, we could observe issues in predicting particular motifs, such as multiple-ways junctions and mini-dumbbells, or the ssNAs whose structure has been determined in complex with a protein. In addition, our benchmark shows that some effort has to be paid for ssDNA secondary structure predictions.ConclusionsIn general, Mfold, RNAfold, and MXfold2 seem to currently be the best choice for the ssNAs secondary structure prediction, although they still show some limits linked to specific structural motifs. Nevertheless, actual trends suggest that artificial intelligence has a high potential to overcome these remaining issues, for example the recently developed UFold and SPOT-RNA have a high success rate in predicting pseudoknots.

Prediction of the effects of the top 10 synonymous mutations from 26645 SARS-CoV-2 genomes

Background: The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had led to a global pandemic since December 2019. SARS-CoV-2 is a single-stranded RNA virus, which mutates at a higher rate. Multiple works had been done to study nonsynonymous mutations, which change protein sequences. However, there is little study on the effects of SARS-CoV-2 synonymous mutations, which may affect viral fitness. This study aims to predict the effect of synonymous mutations on the SARS-CoV-2 genome. Methods: A total of 26645 SARS-CoV-2 genomic sequences retrieved from Global Initiative on Sharing all Influenza Data (GISAID) database were aligned using MAFFT. Then, the mutations and their respective frequency were identified. Multiple RNA secondary structures prediction tools, namely RNAfold, IPknot++ and MXfold2 were applied to predict the effect of the mutations on RNA secondary structure and their base pair probabilities was estimated using MutaRNA. Relative synonymous codon usage (RSCU) analysis was also performed to measure the codon usage bias (CUB) of SARS-CoV-2. Results: A total of 150 synonymous mutations were identified. The synonymous mutation identified with the highest frequency is C3037U mutation in the nsp3 of ORF1a.. Of these top 10 highest frequency synonymous mutations, C913U, C3037U, U16176C and C18877U mutants show pronounced changes between wild type and mutant in all 3 RNA secondary structure prediction tools, suggesting these mutations may have some biological impact on viral fitness. These four mutations show changes in base pair probabilities. All mutations except U16176C change the codon to a more preferred codon, which may result in higher translation efficiency. Conclusion: Synonymous mutations in SARS-CoV-2 genome may affect RNA secondary structure, changing base pair probabilities and possibly resulting in a higher translation rate. However, lab experiments are required to validate the results obtained from prediction analysis.

The Structural Annotations of The Mir-122 Non-Coding RNA from The Tilapia Fish (Oreochromis niloticus)

Tilapia (Oreochromis niloticus) is an important fisheries commodity. Scientific efforts have been done to increase its quality. One of them is staging a premium diet such as a fat-enriched diet. The transcriptomics approach is able to provide the signatures of the diet outcomes by observing the micro(mi)RNA signature in transcriptional regulation. Hence, it was found that the availability of mir-122 is essential in the regulation of a high-fat diet in tilapia. However, this transcriptomics signature is lacking structural annotations and the complete interaction annotations with its silencing(si)RNA. RNAcentral website was navigated for the latest annotation of mir-122 from tilapia and other species as a comparison. MEGA X was employed to comprehend the miRNA evolutionary repertoire. The RNA secondary structure prediction tools from the Vienna RNA package and the RNA tertiary structure prediction tools from simRNA and modeRNA are secured with default parameters. The HNADOCK tools were leveraged to observe the interaction between mir-122 and its siRNA. The post-processing was conducted with the Chimera visualization tool. The secondary and tertiary structure of the mir-122 and its siRNA could be elucidated, docked, and visualized. In this end, further effort to develop a comprehensive molecular breeding tool could be secured with the structural annotation information.

LinAliFold and CentroidLinAliFold: fast RNA consensus secondary structure prediction for aligned sequences using beam search methods.

RNA consensus secondary structure prediction from aligned sequences is a powerful approach for improving the secondary structure prediction accuracy. However, because the computational complexities of conventional prediction tools scale with the cube of the alignment lengths, their application to long RNA sequences, such as viral RNAs or long non-coding RNAs, requires significant computational time. In this study, we developed LinAliFold and CentroidLinAliFold, fast RNA consensus secondary structure prediction tools based on minimum free energy and maximum expected accuracy principles, respectively. We achieved software acceleration using beam search methods that were successfully used for fast secondary structure prediction from a single RNA sequence. Benchmark analyses showed that LinAliFold and CentroidLinAliFold were much faster than the existing methods while preserving the prediction accuracy. As an empirical application, we predicted the consensus secondary structure of coronaviruses with approximately 30000 nt in 5 and 79 min by LinAliFold and CentroidLinAliFold, respectively. We confirmed that the predicted consensus secondary structure of coronaviruses was consistent with the experimental results. The source codes of LinAliFold and CentroidLinAliFold are freely available at https://github.com/fukunagatsu/LinAliFold-CentroidLinAliFold. Supplementary data are available at Bioinformatics Advances online.

NcRFP: A Novel end-to-end Method for Non-Coding RNAs Family Prediction Based on Deep Learning.

Evidence has accumulated enough to prove non-coding RNAs (ncRNAs) play important roles in cellular biological processes and disease pathogenesis. High throughput techniques have produced a large number of ncRNAs whose function remains unknown. Since the accurate identification of ncRNAs family is helpful to the research of their function, it is of necessity and urgency to predict the family of each ncRNAs. Although several traditional excellent methods are applicable to predict the family of ncRNAs, their complex procedures or inaccurate performance remain major problems confronting us. The main idea of those methods is first to predict the secondary structure, and then identify ncRNAs family according to properties of the secondary structure. Unfortunately, the multi-step error superposition, especially the imperfection of RNA secondary structure prediction tools, maybe the cause of low accuracy. In this paper, a novel end-to-end method 'ncRFP' was proposed to complete the prediction task based on Deep Learning. Instead of predicting the secondary structure, ncRFP predicts the ncRNAs family by automatically extracting features from ncRNAs sequences. Compared with other methods, ncRFP not only simplifies the process but also improves accuracy. The source code of ncRFP can be available at https://github.com/linyuwangPHD/ncRFP.

PIP2 Binds Multiple Cationic Sites of ENaC

Phosphatidylinositol 4,5‐bisphosphate (PIP2) regulates the activity and gating of many different ion channels to include the epithelial Na+ channel (ENaC). The negatively charged phosphoryl groups of the PIP2 headgroup are expected to interact with cationic residues of the cytoplasmic tails of ENaC. To date, understanding of the biophysical interaction between ENaC and PIP2 has been limited to indirect functional assays. Consequently, the precise binding sites and their affinities have not been defined. We began this study using secondary structure prediction tools to show that helical conformation that may cluster the cationic residues of ENaC cytoplasmic tails. Such clustering would form an electropositive region attractive to the PIP2 headgroup. We used a CIBN/CRY2‐5‐ptase optogenetic dimerization system in HEK cells to show that the C5 phosphoryl group of PIP2 plays a role in regulating ENaC activity. To more precisely define the PIP2 binding sites of ENaC, we designed short peptides corresponding to the cationic clusters of ENaC. Steady state intrinsic fluorescence ligand binding data showed that PIP2 interacted with the extreme amino terminus of the β‐ENaC subunit (βN1 peptide) and the amino and carboxy termini of γ‐ENaC nearest the transmembrane domains (γN2 and γC peptides, respectively), but no significant changes were observed with the other peptides or mutant controls. Microscale thermophoresis data revealed that the βN1 and γN2 sites has the strongest interaction with the PIP2 headgroup with Kd ~5.2 and 15 μM, respectively, whereas the γC site interacts with much weaker affinity. Again, no interaction was observed between PIP2 and the other peptides and mutant controls. These results are consistent with PIP2 binding ENaC to stabilize the open conformation of channel via moderate and weak electrostatic interactions within the cytoplasmic termini.Support or Funding InformationThis work is supported by NIH/HHLBI T32 HL07446 to CRA and JDS, and NIH/NIDDK R01 DK987460 and DK117909 to JDS.

A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools.

Prediction of proteins' secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts. A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data. Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.

Biophysical analyses and functional implications of the interaction between Heat Shock Protein 27 and antibodies to HSP27

Heat Shock Protein 27 (HSP27) is a small molecular chaperone that reduces the development of atherosclerosis by lowering plasma cholesterol levels as well as inflammation. Human studies show an inverse correlation between atherosclerotic burden and HSP27 expression, and are supported by murine models in which augmenting HSP27 levels curbs experimental atherogenesis. Natural HSP27 auto-antibodies (AAb) are found in human plasma, however their role in modulating the athero-protective effects of HSP27 is unknown. The purpose of this study is to characterize the biophysical interaction between human recombinant HSP27 and AAb. A validated polyclonal anti-HSP27 IgG antibody (PAb) was used to mimic natural AAb. Homology modeling and secondary structure prediction tools facilitated the design of HSP27 truncation and phosphorylation mutants. Secondary structural changes were identified using Circular Dichroism (CD) and Dynamic Light Scattering (DLS). Similar to prior structural investigations of HSP27, there was a predominance of α-helical content in the N-terminal truncation and dephosphorylation (“AA”) mutants. The α-crystallin domain (ACD) predominantly consists of β-strands, with the addition of the N-terminal increasing helical content and the C-terminal maintaining β structure. With increasing ratios of PAb to HSP27 β structure abundance and particle size increased, with a similar trend observed with the N-terminus, C-terminus and ACD peptides but an opposite trend with the phosphorylation peptides. Taken together, these studies provide insights into the interaction of HSP27 and its AAb that ultimately may aid in optimizing the design of HSP27 peptidomimetics with anti-atherogenic potential.

RCPred: RNA complex prediction as a constrained maximum weight clique problem

BackgroundRNAs can interact and form complexes, which have various biological roles. The secondary structure prediction of those complexes is a first step towards the identification of their 3D structure. We propose an original approach that takes advantage of the high number of RNA secondary structure and RNA-RNA interaction prediction tools. We formulate the problem of RNA complex prediction as the determination of the best combination (according to the free energy) of predicted RNA secondary structures and RNA-RNA interactions.ResultsWe model those predicted structures and interactions as a graph in order to have a combinatorial optimization problem that is a constrained maximum weight clique problem. We propose an heuristic based on Breakout Local Search to solve this problem and a tool, called RCPred, that returns several solutions, including motifs like internal and external pseudoknots. On a large number of complexes, RCPred gives competitive results compared to the methods of the state of the art.ConclusionsWe propose in this paper a method called RCPred for the prediction of several secondary structures of RNA complexes, including internal and external pseudoknots. As further works we will propose an improved computation of the global energy and the insertion of 3D motifs in the RNA complexes.

CLUES TO THE DEVELOPMENT OF THE ANTI-ATHEROSCLEROTIC HEAT SHOCK PROTEIN 27 IMMUNOTHERAPY FROM THE BIOPHYSICAL CHARACTERIZATION OF THE HSP27 IMMUNE COMPLEX

Heat Shock Protein 27 (HSP27) is a 205-amino acid protein with novel anti-atherosclerotic properties that attenuates atherosclerosis by reducing inflammation and lowering cholesterol levels in serum and arterial plaques. Diminished serum HSP27 levels are associated with increased cardiovascular risk. Recently, we demonstrated the presence of anti-HSP27 auto-antibodies (AAB), that potentiate atheroprotection by attenuating inflammatory signaling. For example, as opposed to HSP27 alone, the combination of HSP27 plus the AAB (i.e., the immune complex) appears to dock at the cell membrane, altering the interaction with extracellular inflammatory signaling pathways (e.g., TLR4 mediated activation of NF-kB). As we are currently focusing on the development of HSP27 immunotherapeutics, this study was performed to characterize the structural features of the HSP27-AAB interaction that may be of importance to peptidomimetic design. Homology modelling and secondary structure prediction tools were used to generate design assorted forms of the HSP27 in E.coli. Various biophysical techniques including circular dichroism (CD), dynamic light scattering (DLS), isothermal titration calorimetry (ITC) were used to assess structure and function of these recombinant protein in the presence and absence of an anti-HSP27 polyclonal antibody (PAb; generated in rabbits) that was used to mimic naturally occurring AABs. In vitro, this PAb showed a high degree of fidelity with AABs. The HSP27 C-terminus includes a key conserved alpha crystalline domain with a primary beta sheet structure. The N-terminus is responsible for the formation of larger oligomers. Using ITC we determined that the interaction of HSP27 with PAB was an exothermic reaction (△H = -3.0 x 10-4 cal/mol) with a Ka of 8.9 x10-4 M-1 with an average N valve of 0.5 suggesting 2:1 binding between the PAb and HSP27. Moreover, this interaction markedly increases the beta-sheet content and complex multi-site binding profile. Our study suggests that the formation of the HSP27 immune complex involves a moderately intensity interaction which results in conformational (secondary structure) changes. These structural changes may be of importance in determining the extracellular signalling (e.g., docking with TLR4), and highlight newly exposed HSP27 motifs that will be soon be explored using candidate peptidomimetics that may be of therapeutic value for the treatment of not only atherosclerosis, but inflammation in general.

Abstract 4548: Uncovering the membrane targeting mechanism of human unconventional class I myosins: a computational study

Abstract The class 1 myosins (Myo1) are a widely expressed family of single-headed, non-filament-forming, membrane-binding myosins that comprise several sub-classes and are characterized by their ability to cross-link the plasma membrane to the underlying actin cytoskeleton by the presence of a lipid-binding region in the tail domain and an actin-binding region in the motor domain. Consequently, these motor proteins are drivers of numerous cellular processes that link the membranes to the actin cytoskeleton, including but not limited to vesicle trafficking, phagocytosis, cell migration, and intracellular membrane trafficking. Their importance in regulating these processes is underscored by studies that link pathogenesis and cancer with this class of motor proteins. For example, it has been recently shown that aberrant expression of Myo1b is linked to the lymph node metastasis of human head and neck squamous cell carcinoma through enhanced cancer cell motility and therefore, an interesting target for new diagnostic and therapeutic strategies for patients with this type of cancer, the sixth most common malignancy worldwide. Our focus in this study is the little studied aspect of membrane targeting in the different sub-classes for Myo1, even though it is critical part of the functionality of these proteins. Biological membranes are an intricate system consisting of many structurally discrete proteins and lipids that serve as interaction sites with lipid binding domains of soluble proteins. The pleckstrin homology (PH) domain is one of the most common structural folds that serve as a perfect scaffold for cytosolic proteins to bind and target membranes. Although conventional sequence analysis doesn’t identify any membrane binding domains in the tails of Myo1 proteins, recent studies of Myo1c and Myo1g implicate the presence of a PH domain and attribute this domain to their membrane targeting function. A preliminary analysis based on a combination of sequence analysis and secondary structure prediction tools, suggested that Myo1b and several other sub-classes also contain a PH domain. We hypothesize that the membrane targeting function of all Myo1 sub-classes is driven by the presence of a PH domain within their tails. To address this hypothesis, we have carried out a comprehensive investigation of the Myo1 tails from all human unconventional class I myosin isoforms sub-classes to identify, model, and analyze the membrane binding mechanism of their PH domains. This study details the membrane binding mechanisms of unconventional class I myosins and lays the foundation for the role of membrane targeting of Myo1 proteins in cancer and disease. Citation Format: Daniel Gruffat, Shaneen Singh. Uncovering the membrane targeting mechanism of human unconventional class I myosins: a computational study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4548. doi:10.1158/1538-7445.AM2017-4548

Evaluation of in silico protein secondary structure prediction methods by employing statistical techniques

Background: With the advent of many new advanced techniques, sequences of a number of proteins have been made available. But the relative paucity of the experimentally determined three-dimensional structures of these proteins has paved way for the development of computational structure prediction methods. Protein secondary structure prediction is an essential step in modeling the tertiary structure. Among the various secondary structure prediction methods available, three different methods with unique working principles, namely, GOR, HNN, and SOPMA were evaluated for their efficiency to predict secondary structures. Methods: A set of 90 different proteins with known secondary structures from three major classes namely, mainly alpha, mainly beta, and mainly alpha beta was used as reference. Secondary structure data of these proteins obtained through experimental methods were compared with that of predictions made by GOR, HNN, and SOPMA respectively by employing various statistical analyses, namely paired sample test, correlation coefficient, standard deviation, standard error mean and scatter plots. Results: The secondary structure prediction tools namely, GOR and HNN were found to predict helical structures more accurately than the sheets. SOPMA was observed to predict sheets more accurately than helices. Conclusion: Based on the observed results, it could be concluded that there is no single tool that consistently predicts all the secondary structures accurately. It could also be anticipated that a combined use of these secondary prediction tools could further enhance the efficacy of in silico protein secondary structure prediction methods.

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.

MiRPlant: an integrated tool for identification of plant miRNA from RNA sequencing data.

BackgroundSmall RNA sequencing is commonly used to identify novel miRNAs and to determine their expression levels in plants. There are several miRNA identification tools for animals such as miRDeep, miRDeep2 and miRDeep*. miRDeep-P was developed to identify plant miRNA using miRDeep’s probabilistic model of miRNA biogenesis, but it depends on several third party tools and lacks a user-friendly interface. The objective of our miRPlant program is to predict novel plant miRNA, while providing a user-friendly interface with improved accuracy of prediction.ResultWe have developed a user-friendly plant miRNA prediction tool called miRPlant. We show using 16 plant miRNA datasets from four different plant species that miRPlant has at least a 10% improvement in accuracy compared to miRDeep-P, which is the most popular plant miRNA prediction tool. Furthermore, miRPlant uses a Graphical User Interface for data input and output, and identified miRNA are shown with all RNAseq reads in a hairpin diagram.ConclusionsWe have developed miRPlant which extends miRDeep* to various plant species by adopting suitable strategies to identify hairpin excision regions and hairpin structure filtering for plants. miRPlant does not require any third party tools such as mapping or RNA secondary structure prediction tools. miRPlant is also the first plant miRNA prediction tool that dynamically plots miRNA hairpin structure with small reads for identified novel miRNAs. This feature will enable biologists to visualize novel pre-miRNA structure and the location of small RNA reads relative to the hairpin. Moreover, miRPlant can be easily used by biologists with limited bioinformatics skills.miRPlant and its manual are freely available at http://www.australianprostatecentre.org/research/software/mirplant or http://sourceforge.net/projects/mirplant/.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-275) contains supplementary material, which is available to authorized users.

RNA-SSPT: RNA Secondary Structure Prediction Tools.

The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes.

A Tool Preference Choice Method for RNA Secondary Structure Prediction by SVM with Statistical Tests

The Prediction of RNA secondary structures has drawn much attention from both biologists and computer scientists. Many useful tools have been developed for this purpose. These tools have their individual strengths and weaknesses. As a result, based on support vector machines (SVM), we propose a tool choice method which integrates three prediction tools: pknotsRG, RNAStructure, and NUPACK. Our method first extracts features from the target RNA sequence, and adopts two information-theoretic feature selection methods for feature ranking. We propose a method to combine feature selection and classifier fusion in an incremental manner. Our test data set contains 720 RNA sequences, where 225 pseudoknotted RNA sequences are obtained from PseudoBase, and 495 nested RNA sequences are obtained from RNA SSTRAND. The method serves as a preprocessing way in analyzing RNA sequences before the RNA secondary structure prediction tools are employed. In addition, the performance of various configurations is subject to statistical tests to examine their significance. The best base-pair accuracy achieved is 75.5%, which is obtained by the proposed incremental method, and is significantly higher than 68.8%, which is associated with the best predictor, pknotsRG.

A Simple Comparison between Specific Protein Secondary Structure Prediction Tools

A comparative evaluation of five widely used protein secondary structure prediction programs available in World Wide Web was carried out. Secondary structure data of ten proteins containing 190 secondary structure motifs were collected from Protein Data Bank (PDB). The amino acid sequences of the proteins were then evaluated using GOR, PSIPRED, HNN, PROF, and YASPIN secondary structure prediction tools and the results were compared with the structural information obtained from PDB. The study reveals considerable differences between results obtained from each program. Within the limit of this comparative study, PSIPRED showed the highest prediction accuracy with 77 % accuracy in α helix prediction and 70 % accuracy in b strand prediction. Furthermore, the level of accuracy varied with the length of the secondary structure motifs. Highest accuracies were obtained for α helices of 16-20 amino acids and β strands of 7-9 amino acids in length. The results suggest that, among the most frequently used software programs available in World Wide Web, PSIPRED is the tool that gives the best results for secondary structure prediction. <em>Tropical Agricultural Research Vol. 23 (1): 91-98 (2011)</em> DOI: <a href="http://dx.doi.org/10.4038/tar.v23i1.4636">http://dx.doi.org/10.4038/tar.v23i1.4636</a>

RNA Secondary Structure Thermodynamics.

Several different ways to predict RNA secondary structures have been suggested in the literature. Statistical methods, such as those that utilize stochastic context-free grammars (SCFGs), or approaches based on machine learning aim to predict the best representative structure for the underlying ensemble of possible conformations. Their parameters have therefore been trained on larger subsets of well-curated, known secondary structures. Physics-based methods, on the other hand, usually refrain from using optimized parameters. They model secondary structures from loops as individual building blocks which have been assigned a physical property instead: the free energy of the respective loop. Such free energies are either derived from experiments or from mathematical modeling. This rigorous use of physical properties then allows for the application of statistical mechanics to describe the entire state space of RNA secondary structures in terms of equilibrium probabilities. On that basis, and by using efficient algorithms, many more descriptors of the conformational state space of RNA molecules can be derived to investigate and explain the many functions of RNA molecules. Moreover, compared to other methods, physics-based models allow for a much easier extension with other properties that can be measured experimentally. For instance, small molecules or proteins can bind to an RNA and their binding affinity can be assessed experimentally. Under certain conditions, existing RNA secondary structure prediction tools can be used to model this RNA-ligand binding and to eventually shed light on its impact on structure formation and function.

Structure–function analysis of HsiF, a gp25-like component of the type VI secretion system, in Pseudomonas aeruginosa

Bacterial pathogens use a range of protein secretion systems to colonize their host. One recent addition to this arsenal is the type VI secretion system (T6SS), which is found in many Gram-negative bacteria. The T6SS involves 12–15 components, including a ClpV-like AAA+ ATPase. Moreover, the VgrG and Hcp components have been proposed to form a puncturing device, based on structural similarity to the tail spike components gp5/gp27 and the tail tube component gp19 of the T4 bacteriophage, respectively. Another T6SS component shows similarity to a T4 phage protein, namely gp25. The gp25 protein has been proposed to have lysozyme activity. Other T6SS components do not exhibit obvious similarity to characterized T4 phage components. The genome of Pseudomonas aeruginosa contains three T6SS gene clusters. In each cluster a gene encoding a putative member of the gp25-like protein family was identified, which we called HsiF. We confirmed this similarity by analysing the structure of the P. aeruginosa HsiF proteins using secondary and tertiary structure prediction tools. We demonstrated that HsiF1 is crucial for the T6SS-dependent secretion of Hcp and VgrG. Importantly, lysozyme activity of HsiF proteins was not detectable, and we related this observation to the demonstration that HsiF1 localizes to the cytoplasm of P. aeruginosa. Finally, our data showed that a conserved glutamate, predicted to be required for proper HsiF folding, is essential for its function. In conclusion, our data confirm the central role of HsiF in the T6SS mechanism, provide information on the predicted HsiF structure, and call for reconsideration of the function of gp25-like proteins.