Phylogenetic analyses of Fringillidae (Aves : Passeriformes) using mitochondrial tRNA gene sequences and secondary structure

Fringillidae is a large and diverse family of Passeriformes. So far, however, Fringillidae relationships deduced from morphological features and by a number of molecular approaches have remained unproven. Recently, much attention has been attracted to mitochondrial tRNA genes, whose sequence and secondary structural characteristics have shown to be useful for Acrodont Lizards and deep-branch phylogenetic studies. In order to identify useful phylogenetic markers and test Fringillidae relationships, we have sequenced three major clusters of mitochondrial tRNA genes from 15 Fringillidae, taxa. A coincident tree, with coturnix as outgroup, was obtained through Maximum-likelihood method using combined dataset of 11 mitochondrial tRNA gene sequences. The result was similar to that through Neighbor-joining but different from Maximum-parsimony methods. Phylogenetic trees constructed with stem-region sequences of 11 genes had many different topologies and lower confidence than with total sequences. On the other hand, some secondary structural characteristics may provide phylogenetic information on relatively short internal branches at under-genus level. In summary, our data indicate that mitochondrial tRNA genes can achieve high confidence on Fringillidae phylogeny at subfamily level, and stem-region sequences may be suitable only at above-family level. Secondary structural characteristics may also be useful to resolve phylogenetic relationship between different genera of Fringillidae with good performance.

Secondary structure of yeast genomic downstream region and polyadenylation signals

Polyadenylation of 3 ' -forming in eukaryote concerns three elements located in precursor mRNA downstream region: efficiency element (EE), position element (PE) and the actual site for cleavage and polyadenylation. Several base sequences of EE and PE have

Phylogenetic analysis of the family Thermaceae with an emphasis on signature position and secondary structure of 16S rRNA

The sequences of the 16S rRNA genes from 38 strains of the family Thermaceae were compared by alignment analysis. The genus-specific and species-specific base substitutions or base deletions (signature positions) were found in three hypervariable regions (in the helices 6, 10 and 17). The differentiation of secondary structures of the high variable regions in the 5' end (38-497) containing several signature positions further supported the concept. Based on the comparisons of the secondary structures in the segments of 16S rRNAs, a key to the species of the family Thermaceae was proposed. (C) 2003 Published by Elsevier Science B.V. on behalf of the Federation of European Microbiological Societies.

The relation between translation speed and protein secondary structure

Based on the statistical analysis of 119 human and 92 E. coli proteins it was found that for both human and E. coli, the mRNA sequences consisting of tri-codon and tetra-codon with high translation speed preferably code for alpha helices more than for coils. For beta strand, the preference/ avoidance oscillates with the translation speed. Moreover, the non-homogeneous usages of tri-codon and tetra-codon with different translation speeds in a given secondary structure have also been found. These results cannot be simply explained by the effect of stochastic fluctuation.

Determination of the secondary structure of the king cobra neurotoxin CM-11

The king cobra neuotoxin CM-11 is a small protein with 72 amino acid residues. After its complete assignments of H-1-NMR resonance's were obtained using various 2D-NMR technologies, including of DQF-COSY, clean-TOCSY AND NOESY, the secondary structure was analysed by studying the various NOEs extracted from the NOESY spectra and the distribution of chemical shifts. The secondary structure was finally determined by MCD as follows: a triple-strand antiparallel beta sheet with I20-W36, R37-A43 and V53--S59 as its beta strands, a short alpha helix formed by W30-G35 and four turns formed by P7-K10, C14-G17, K50-V53 and D61-N64.

The rapid assignment of ruminal fungi to presumptive genera using ITS1 and ITS2 RNA secondary structures to produce group-specific fingerprints

Danny S. Tuckwell, Matthew J. Nicholson, Christopher S. McSweeney, Michael K. Theodorou and Jayne L. Brookman (2005). The rapid assignment of ruminal fungi to presumptive genera using ITS1 and ITS2 RNA secondary structures to produce group-specific fingerprints. Microbiology, 151 (5) pp.1557-1567 Sponsorship: BBSRC / Stapledon Memorial Trust RAE2008

Secondary structure effects on DNA hybridization kinetics: a solution versus surface comparison

The hybridization kinetics for a series of designed 25mer probe�target pairs having varying degrees of secondary structure have been measured by UV absorbance and surface plasmon resonance (SPR) spectroscopy in solution and on the surface, respectively. Kinetic rate constants derived from the resultant data decrease with increasing probe and target secondary structure similarly in both solution and surface environments. Specifically, addition of three intramolecular base pairs in the probe and target structure slow hybridization by a factor of two. For individual strands containing four or more intramolecular base pairs, hybridization cannot be described by a traditional two-state model in solution-phase nor on the surface. Surface hybridization rates are also 20- to 40-fold slower than solution-phase rates for identical sequences and conditions. These quantitative findings may have implications for the design of better biosensors, particularly those using probes with deliberate secondary structure.

Algorithmic computation of knot polynomials of secondary structure elements of proteins

The classification of protein structures is an important and still outstanding problem. The purpose of this paper is threefold. First, we utilize a relation between the Tutte and homfly polynomial to show that the Alexander-Conway polynomial can be algorithmically computed for a given planar graph. Second, as special cases of planar graphs, we use polymer graphs of protein structures. More precisely, we use three building blocks of the three-dimensional protein structure-alpha-helix, antiparallel beta-sheet, and parallel beta-sheet-and calculate, for their corresponding polymer graphs, the Tutte polynomials analytically by providing recurrence equations for all three secondary structure elements. Third, we present numerical results comparing the results from our analytical calculations with the numerical results of our algorithm-not only to test consistency, but also to demonstrate that all assigned polynomials are unique labels of the secondary structure elements. This paves the way for an automatic classification of protein structures.

Secondary structure prediction with support vector machines

Motivation: A new method that uses support vector machines (SVMs) to predict protein secondary structure is described and evaluated. The study is designed to develop a reliable prediction method using an alternative technique and to investigate the applicability of SVMs to this type of bioinformatics problem. Methods: Binary SVMs are trained to discriminate between two structural classes. The binary classifiers are combined in several ways to predict multi-class secondary structure. Results: The average three-state prediction accuracy per protein (Q3) is estimated by cross-validation to be 77.07 ± 0.26% with a segment overlap (Sov) score of 73.32 ± 0.39%. The SVM performs similarly to the 'state-of-the-art' PSIPRED prediction method on a non-homologous test set of 121 proteins despite being trained on substantially fewer examples. A simple consensus of the SVM, PSIPRED and PROFsec achieves significantly higher prediction accuracy than the individual methods. Availability: The SVM classifier is available from the authors. Work is in progress to make the method available on-line and to integrate the SVM predictions into the PSIPRED server.

Benchmarking secondary structure prediction for fold recognition

If secondary structure predictions are to be incorporated into fold recognition methods, an assessment of the effect of specific types of errors in predicted secondary structures on the sensitivity of fold recognition should be carried out. Here, we present a systematic comparison of different secondary structure prediction methods by measuring frequencies of specific types of error. We carry out an evaluation of the effect of specific types of error on secondary structure element alignment (SSEA), a baseline fold recognition method. The results of this evaluation indicate that missing out whole helix or strand elements, or predicting the wrong type of element, is more detrimental than predicting the wrong lengths of elements or overpredicting helix or strand. We also suggest that SSEA scoring is an effective method for assessing accuracy of secondary structure prediction and perhaps may also provide a more appropriate assessment of the “usefulness” and quality of predicted secondary structure, if secondary structure alignments are to be used in fold recognition.

Rapid protein domain assignment from amino acid sequence using predicted secondary structure

The elucidation of the domain content of a given protein sequence in the absence of determined structure or significant sequence homology to known domains is an important problem in structural biology. Here we address how successfully the delineation of continuous domains can be accomplished in the absence of sequence homology using simple baseline methods, an existing prediction algorithm (Domain Guess by Size), and a newly developed method (DomSSEA). The study was undertaken with a view to measuring the usefulness of these prediction methods in terms of their application to fully automatic domain assignment. Thus, the sensitivity of each domain assignment method was measured by calculating the number of correctly assigned top scoring predictions. We have implemented a new continuous domain identification method using the alignment of predicted secondary structures of target sequences against observed secondary structures of chains with known domain boundaries as assigned by Class Architecture Topology Homology (CATH). Taking top predictions only, the success rate of the method in correctly assigning domain number to the representative chain set is 73.3%. The top prediction for domain number and location of domain boundaries was correct for 24% of the multidomain set (±20 residues). These results have been put into context in relation to the results obtained from the other prediction methods assessed