An extension of secondary structure prediction towards the prediction of tertiary structure

Secondary structure prediction parameters and optimised decision constants for use with the method of Garnier et al. [(1978) J. Mol. Biol. 120, 97-120] have been derived for two new and distinct substates of beta-structure. These we term internal and external on the basis of their hydrogen bonding patterns. The profiles of the amino acids for several of the parameters are considerably different in the two substates. Predictions using the new parameters attempt to distinguish the strands at the core of the beta-sheet from those at its edges and so restrict the possible topologies in tertiary structure prediction. The potential application of these parameters is illustrated for the class of beta/alpha proteins.     

Revolutions in RNA secondary structure prediction

RNA structure formation is hierarchical and, therefore, secondary structure, the sum of canonical base-pairs, can generally be predicted without knowledge of the three-dimensional structure. Secondary structure prediction algorithms evolved from predicting a single, lowest free energy structure to their current state where statistics can be determined from the thermodynamic ensemble. This article reviews the free energy minimization technique and the salient revolutions in the dynamic programming algorithm methods for secondary structure prediction. Emphasis is placed on highlighting the recently developed method, which statistically samples structures from the complete Boltzmann ensemble.     

An efficient method for the prediction of deleterious multiple-point mutations in the secondary structure of RNAs using suboptimal folding solutions

Background  

RNAmute is an interactive Java application which, given an RNA sequence, calculates the secondary structure of all single point mutations and organizes them into categories according to their similarity to the predicted structure of the wild type. The secondary structure predictions are performed using the Vienna RNA package. A more efficient implementation of RNAmute is needed, however, to extend from the case of single point mutations to the general case of multiple point mutations, which may often be desired for computational predictions alongside mutagenesis experiments. But analyzing multiple point mutations, a process that requires traversing all possible mutations, becomes highly expensive since the running time is O(n ^m ) for a sequence of length n with m-point mutations. Using Vienna's RNAsubopt, we present a method that selects only those mutations, based on stability considerations, which are likely to be conformational rearranging. The approach is best examined using the dot plot representation for RNA secondary structure.     

Evolution of secondary structure in the family of 7SL-like RNAs

Primate and rodent genomes are populated with hundreds of thousands copies of Alu and B1 elements dispersed by retroposition, i.e., by genomic reintegration of their reverse transcribed RNAs. These, as well as primate BC200 and rodent 4.5S RNAs, are ancestrally related to the terminal portions of 7SL RNA sequence. The secondary structure of 7SL RNA (an integral component of the signal recognition particle) is conserved from prokaryotes to distant eukaryotic species. Yet only in primates and rodents did this molecule give rise to retroposing Alu and B1 RNAs and to apparently functional BC200 and 4.5S RNAs. To understand this transition and the underlying molecular events, we examined, by comparative analysis, the evolution of RNA structure in this family of molecules derived from 7SL RNA.RNA sequences of different simian (mostly human) and prosimian Alu subfamilies as well as rodent B1 repeats were derived from their genomic consensus sequences taken from the literature and our unpublished results (prosimian and New World Monkey). RNA secondary structures were determined by enzymatic studies (new data on 4.5S RNA are presented) and/or energy minimization analyses followed by phylogenetic comparison. Although, with the exception of 4.5S RNA, all 7SL-derived RNA species maintain the cruciform structure of their progenitor, the details of 7SL RNA folding domains are modified to a different extent in various RNA groups. Novel motifs found in retropositionally active RNAs are conserved among Alu and B1 subfamilies in different genomes. In RNAs that do not proliferate by retroposition these motifs are modified further. This indicates structural adaptation of 7SL-like RNA molecules to novel functions, presumably mediated by specific interactions with proteins; these functions were either useful for the host or served the selfish propagation of RNA templates within the host genome.Abbreviations FAM fossil Alu element - FLAM free left Alu monomer - FRAM free right Alu monomer - L-Alu left Alu subunit - R-Alu right Alu subunit Correspondence to: D. LabudaDedicated to Dr. Robert Cedergren on the occasion of his 25th anniversary at the University of Montreal     

Serum albumin domain secondary structure prediction

A new method has been used to predict probability profiles for helix, beta-sheet and bend structures along the entire sequence and derive an averaged profile for the three homologous domains. The results are correlated with the disulphide bridge pattern, the distribution of hydrophobic sites and points where albumin is cleaved by enzymes.     

Seventy-five percent accuracy in protein secondary structure prediction

In this study we present an accurate secondary structure prediction procedure by using a query and related sequences. The most novel aspect of our approach is its reliance on local pairwise alignment of the sequence to be predicted with each related sequence rather than utilization of a multiple alignment. The residue-by-residue accuracy of the method is 75% in three structural states after jack-knife tests. The gain in prediction accuracy compared with the existing techniques, which are at best 72%, is achieved by secondary structure propensities based on both local and long-range effects, utilization of similar sequence information in the form of carefully selected pairwise alignment fragments, and reliance on a large collection of known protein primary structures. The method is especially appropriate for large-scale sequence analysis efforts such as genome characterization, where precise and significant multiple sequence alignments are not available or achievable. Proteins 27:329–335, 1997. © 1997 Wiley-Liss, Inc.     

Statistical evaluation of improvement in RNA secondary structure prediction

With discovery of diverse roles for RNA, its centrality in cellular functions has become increasingly apparent. A number of algorithms have been developed to predict RNA secondary structure. Their performance has been benchmarked by comparing structure predictions to reference secondary structures. Generally, algorithms are compared against each other and one is selected as best without statistical testing to determine whether the improvement is significant. In this work, it is demonstrated that the prediction accuracies of methods correlate with each other over sets of sequences. One possible reason for this correlation is that many algorithms use the same underlying principles. A set of benchmarks published previously for programs that predict a structure common to three or more sequences is statistically analyzed as an example to show that it can be rigorously evaluated using paired two-sample t-tests. Finally, a pipeline of statistical analyses is proposed to guide the choice of data set size and performance assessment for benchmarks of structure prediction. The pipeline is applied using 5S rRNA sequences as an example.     

基于SVM的蛋白质二级结构预测

蛋白质结构预测是现代计算生物领域最重要的问题之一,而蛋白质二级结构预测是蛋白质高级结构预测的基础。目前蛋白质二级结构的预测方法较多,其中SVM方法取得了较高的预测精度。重在阐述使用SVM用于蛋白质二级结构预测的步骤,以及与其他方法进行比较时应该注意的事项,为下一步的研究提供参考及启发。     

Protein secondary structure prediction using dynamic programming

In the present paper, we describe how a directed graph was constructed and then searched for the optimum path using a dynamic programming approach, based on the secondary structure propensity of the protein short sequence derived from a training data set. The protein secondary structure was thus predicted in this way. The average three-state accuracy of the algorithm used was 76.70%.     

Pairwise covariance adds little to secondary structure prediction but improves the prediction of non-canonical local structure

Background  

Amino acid sequence probability distributions, or profiles, have been used successfully to predict secondary structure and local structure in proteins. Profile models assume the statistical independence of each position in the sequence, but the energetics of protein folding is better captured in a scoring function that is based on pairwise interactions, like a force field.     

Deleterious mutation accumulation in asexual Timema stick insects

Sexual reproduction is extremely widespread in spite of its presumed costs relative to asexual reproduction, indicating that it must provide significant advantages. One postulated benefit of sex and recombination is that they facilitate the purging of mildly deleterious mutations, which would accumulate in asexual lineages and contribute to their short evolutionary life span. To test this prediction, we estimated the accumulation rate of coding (nonsynonymous) mutations, which are expected to be deleterious, in parts of one mitochondrial (COI) and two nuclear (Actin and Hsp70) genes in six independently derived asexual lineages and related sexual species of Timema stick insects. We found signatures of increased coding mutation accumulation in all six asexual Timema and for each of the three analyzed genes, with 3.6- to 13.4-fold higher rates in the asexuals as compared with the sexuals. In addition, because coding mutations in the asexuals often resulted in considerable hydrophobicity changes at the concerned amino acid positions, coding mutations in the asexuals are likely associated with more strongly deleterious effects than in the sexuals. Our results demonstrate that deleterious mutation accumulation can differentially affect sexual and asexual lineages and support the idea that deleterious mutation accumulation plays an important role in limiting the long-term persistence of all-female lineages.     

From structure prediction to genomic screens for novel non-coding RNAs

Non-coding RNAs (ncRNAs) are receiving more and more attention not only as an abundant class of genes, but also as regulatory structural elements (some located in mRNAs). A key feature of RNA function is its structure. Computational methods were developed early for folding and prediction of RNA structure with the aim of assisting in functional analysis. With the discovery of more and more ncRNAs, it has become clear that a large fraction of these are highly structured. Interestingly, a large part of the structure is comprised of regular Watson-Crick and GU wobble base pairs. This and the increased amount of available genomes have made it possible to employ structure-based methods for genomic screens. The field has moved from folding prediction of single sequences to computational screens for ncRNAs in genomic sequence using the RNA structure as the main characteristic feature. Whereas early methods focused on energy-directed folding of single sequences, comparative analysis based on structure preserving changes of base pairs has been efficient in improving accuracy, and today this constitutes a key component in genomic screens. Here, we cover the basic principles of RNA folding and touch upon some of the concepts in current methods that have been applied in genomic screens for de novo RNA structures in searches for novel ncRNA genes and regulatory RNA structure on mRNAs. We discuss the strengths and weaknesses of the different strategies and how they can complement each other.     

Exploiting the past and the future in protein secondary structure prediction

MOTIVATION: Predicting the secondary structure of a protein (alpha-helix, beta-sheet, coil) is an important step towards elucidating its three-dimensional structure, as well as its function. Presently, the best predictors are based on machine learning approaches, in particular neural network architectures with a fixed, and relatively short, input window of amino acids, centered at the prediction site. Although a fixed small window avoids overfitting problems, it does not permit capturing variable long-rang information. RESULTS: We introduce a family of novel architectures which can learn to make predictions based on variable ranges of dependencies. These architectures extend recurrent neural networks, introducing non-causal bidirectional dynamics to capture both upstream and downstream information. The prediction algorithm is completed by the use of mixtures of estimators that leverage evolutionary information, expressed in terms of multiple alignments, both at the input and output levels. While our system currently achieves an overall performance close to 76% correct prediction--at least comparable to the best existing systems--the main emphasis here is on the development of new algorithmic ideas. AVAILABILITY: The executable program for predicting protein secondary structure is available from the authors free of charge. CONTACT: pfbaldi@ics.uci.edu, gpollast@ics.uci.edu, brunak@cbs.dtu.dk, paolo@dsi.unifi.it.     

Improving the prediction of secondary structure of 'TIM-barrel' enzymes.

The information contained in aligned sets of homologous protein sequences should improve the score of secondary structure prediction. Seven different enzymes having the (beta/alpha)8 or TIM-barrel fold were used to optimize the prediction with regard to this class of enzymes. The alpha-helix, beta-strand and loop propensities of the Garnier-Osguthorpe-Robson method were averaged at aligned residue positions, leading to a significant improvement over the average score obtained from single sequences. The increased accuracy correlates with the average sequence variability of the aligned set. Further improvements were obtained by using the following averaged properties as weights for the averaged state propensities: amphipathic moment and alpha-helix; hydropathy and beta-strand; chain flexibility and loop. The clustering of conserved residues at the C-terminal ends of the beta-strands was used as an additional positive weight for beta-strand propensity and increased the prediction of otherwise unpredicted beta-strands decisively. The automatic weighted prediction method identifies greater than 95% of the secondary structure elements of the set of seven TIM-barrel enzymes.     

Machine learning approach for the prediction of protein secondary structure

PROMIS (protein machine induction system), a program for machine learning, was used to generalize rules that characterize the relationship between primary and secondary structure in globular proteins. These rules can be used to predict an unknown secondary structure from a known primary structure. The symbolic induction method used by PROMIS was specifically designed to produce rules that are meaningful in terms of chemical properties of the residues. The rules found were compared with existing knowledge of protein structure: some features of the rules were already recognized (e.g. amphipathic nature of alpha-helices). Other features are not understood, and are under investigation. The rules produced a prediction accuracy for three states (alpha-helix, beta-strand and coil) of 60% for all proteins, 73% for proteins of known alpha domain type, 62% for proteins of known beta domain type and 59% for proteins of known alpha/beta domain type. We conclude that machine learning is a useful tool in the examination of the large databases generated in molecular biology.     

An algorithm for protein secondary structure prediction based on class prediction

An algorithm has been developed to improve the success rate in the prediction of the secondary structure of proteins by taking into account the predicted class of the proteins. This method has been called the 'double prediction method' and consists of a first prediction of the secondary structure from a new algorithm which uses parameters of the type described by Chou and Fasman, and the prediction of the class of the proteins from their amino acid composition. These two independent predictions allow one to optimize the parameters calculated over the secondary structure database to provide the final prediction of secondary structure. This method has been tested on 59 proteins in the database (i.e. 10,322 residues) and yields 72% success in class prediction, 61.3% of residues correctly predicted for three states (helix, sheet and coil) and a good agreement between observed and predicted contents in secondary structure.     

RNAmute: RNA secondary structure mutation analysis tool

Background  

RNAMute is an interactive Java application that calculates the secondary structure of all single point mutations, given an RNA sequence, and organizes them into categories according to their similarity with respect to the wild type predicted structure. The secondary structure predictions are performed using the Vienna RNA package. Several alternatives are used for the categorization of single point mutations: Vienna's RNAdistance based on dot-bracket representation, as well as tree edit distance and second eigenvalue of the Laplacian matrix based on Shapiro's coarse grain tree graph representation.