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PubMedDate not supplied

U1 snRNA: the evolution of its primary and secondary structure.

In this paper we first show that the primary structure of U1 snRNA is homologous to that of tandem repeated pre-tRNA. Two sets of polymerase III promoter sites (the a and b boxes) are clearly recognisable at the appropriate positions in U1, although neither is functional; these sites occur in a degenerate form and their transcription is initiated by polymerase II. Moreover, several of the conserved subsequences of tRNAs that are not associated with transcription initiation (and supposedly are conserved because of their role in translation) are conserved in U1 as well, one of them being the pattern Py-Py-anticodon-Pu-Pu (for both anticodons of tandem tRNA). Second, we show that the secondary structure of U1 is apparently formed after fixation of the "B-hairpin loop' by one of the associated proteins. If and only if this hairpin loop is fixed, a consensus secondary structure is produced by the minimisation-of-free-energy technique. Moreover, we show that this B-hairpin loop has been destabilised relatively recently in evolutionary time by deletions (e.g., in the polymerase III box). If we reinsert the deleted bases, the so constructed hypothetical "ancestral" molecule folds into the consensus secondary structure by unconstrained energy minimisation (i.e., without fixation of the B-loop). Some features of the secondary structure of tandem repeated pre-tRNA are conserved in U1, but the overall structure has changed dramatically. Like tRNA, U1 has a cloverleaf-like structure, but its overall size has doubled. By comparing their secondary structures and by alignment of the sequences, we trace the local events associated with the global change in secondary structure (and apparently in the function of the molecule). Finally, we discuss our results from the perspective of informatic prerequisites for heterarchical multilevel evolution.

Base Sequence↗
PubMed1991

Use of electrophoretic mobility to determine the secondary structure of a small antisense RNA.

Natural antisense RNAs have stem-loop (hairpin) secondary structures that are important for their function. The sar antisense RNA of phage P22 is unusual: the 3' half of the molecule forms an extensive stem-loop, but potential structures for the 5' half are not predicted to be thermodynamically stable. We devised a novel method to determine the secondary structure of sar RNA by examining the electrophoretic mobility on non-denaturing gels of numerous sar mutants. The results show that the wild-type RNA forms a 5' stem-loop that enhances electrophoretic mobility. All mutations that disrupt the stem of this hairpin decrease mobility of the RNA. In contrast, mutations that change the sequence of the stem without disrupting it (e.g. change G.U to A.U) do not affect mobility. Nearly all mutations in single-stranded regions of the structure also have no effect on mobility. Confirmation of the proposed 5' stem-loop was obtained by constructing and analyzing compensatory double mutants. Combinations of mutations that restore a base-pair of the stem also restore mobility. The genetic phenotypes of sar mutants confirm that the proposed secondary structure is correct and is essential for optimal activity of the antisense RNA in vivo.

Base Sequence↗
PubMed2006

A new representation for protein secondary structure prediction based on frequent patterns.

MOTIVATION: A new representation for protein secondary structure prediction based on frequent amino acid patterns is described and evaluated. We discuss in detail how to identify frequent patterns in a protein sequence database using a level-wise search technique, how to define a set of features from those patterns and how to use those features in the prediction of the secondary structure of a protein sequence using support vector machines (SVMs). RESULTS: Three different sets of features based on frequent patterns are evaluated in a blind testing setup using 150 targets from the EVA contest and compared to predictions of PSI-PRED, PHD and PROFsec. Despite being trained on only 940 proteins, a simple SVM classifier based on this new representation yields results comparable to PSI-PRED and PROFsec. Finally, we show that the method contributes significant information to consensus predictions. AVAILABILITY: The method is available from the authors upon request.

Algorithms↗
PubMed1988

The predicted secondary structures of class I fructose-bisphosphate aldolases.

The results of several secondary-structure prediction programs were combined to produce an estimate of the regions of alpha-helix, beta-sheet and reverse turns for fructose-bisphosphate aldolases from human and rat muscle and liver, from Trypanosoma brucei and from Drosophila melanogaster. All the aldolase sequences gave essentially the same pattern of secondary-structure predictions despite having sequences up to 50% different. One exception to this pattern was an additional strongly predicted helix in the rat liver and Drosophila enzymes. Regions of relatively high sequence variation generally were predicted as reverse turns, and probably occur as surface loops. Most of the positions corresponding to exon boundaries are located between regions predicted to have secondary-structural elements consistent with a compact structure. The predominantly alternating alpha/beta structure predicted is consistent with the alpha/beta-barrel structure indicated by preliminary high-resolution X-ray diffraction studies on rabbit muscle aldolase [Sygusch, Beaudry & Allaire (1986) Biophys. J. 49, 287a].

Animals↗
PubMedDate not supplied

[The effect of water and secondary structure on the thermal stability of macroradicals in proteins].

The thermostability of macroradicals located in the protein peptide chain segments with the different secondary structure was investigated. It has been shown at the dry proteins that just the secondary structure determines the thermostability of the macroradicals. In the different proteins the thermostability of macroradicals in peptide chain segments with identical secondary structure is the same. The thermostability of free radicals is the same in aqueous solutions of the different proteins.

Free Radicals↗
PubMed1996

The importance of larger data sets for protein secondary structure prediction with neural networks.

A neural network algorithm is applied to secondary structure and structural class prediction for a database of 318 nonhomologous protein chains. Significant improvement in accuracy is obtained as compared with performance on smaller databases. A systematic study of the effects of network topology shows that, for the larger database, better results are obtained with more units in the hidden layer. In a 32-fold cross validated test, secondary structure prediction accuracy is 67.0%, relative to 62.6% obtained previously, without any evolutionary information on the sequence. Introduction of sequence profiles increases this value to 72.9%, suggesting that the two types of information are essentially independent. Tertiary structural class is predicted with 80.2% accuracy, relative to 73.9% obtained previously. The use of a larger database is facilitated by the introduction of a scaled conjugate gradient algorithm for optimizing the neural network. This algorithm is about 10-20 times as fast as the standard steepest descent algorithm.

Algorithms↗
PubMed1997

Performance of a neural-network-based determination of amino acid class and secondary structure from 1H-15N NMR data.

A neural network which can determine both amino acid class and secondary structure using NMR data from 15N-labeled proteins is described. We have included nitrogen chemical shifts, 3JHNH alpha coupling constants, alpha-proton chemical shifts, and side-chain proton chemical shifts as input to a three-layer feed-forward network. The network was trained with 456 spin systems from several proteins containing various types of secondary structure, and tested on human ubiquitin, which has no sequence homology with any of the proteins in the training set. A very limited set of data, representative of those from a TOCSY-HSQC and HNHA experiment, was used. Nevertheless, in 60% of the spin systems the correct amino acid class was among the top two choices given by the network, while in 96% of the spin systems the secondary structure was correctly identified. The performance of this network clearly shows the potential of the neural network algorithm in the automation of NMR spectral analysis.

Amino Acids↗
PubMed1997

Spectroscopic study of secondary structure and thermal denaturation of recombinant human factor XIII in aqueous solution.

The secondary structure and thermal denaturation (in H2O vs D2O) of recombinant human factor XIII in aqueous solutions were investigated using infrared and circular dichroism (CD) spectroscopies. The infrared amide I spectrum of the protein in H2O solution at 25 degrees C exhibited an absorbance maximum near 1642 cm-1, indicating the presence of a predominantly beta-sheet structure. Quantitative analysis revealed that the native protein contains 13-16% alpha-helix, 41-49% beta-sheet, 29% beta-turn, and 10-14% extended strand structures. The presence of a strong low-wavenumber beta-sheet band at 1641 cm-1 and a weak high-wavenumber beta-sheet band at 1689 cm-1 indicated that the beta-sheet structure of the protein is predominantly antiparallel. Quantitative analysis of the CD spectrum using the SELCON method indicated a secondary structural content of 10% alpha-helix, 40-50% beta-sheet, 20-35% beta-turns, and 20-35% unordered elements, which matches that determined by X-ray crystallography. The apparent discrepancy with the contents of unordered element determined by infrared spectroscopy is reconciled by considering that CD spectroscopy and X-ray crystallography assign extended loops and strands to unordered elements, whereas infrared spectroscopy recognizes these as distinct structured elements. During heating above 60 degrees C, a pair of new infrared bands appeared at 1626 and 1693 cm-1 for the protein in H2O and 1619 and 1683 cm-1 in D2O, indicating a formation of intermolecular beta-sheet aggregates. The intensities of the new bands increased as a function of temperature, concomitant with an intensity decrease in bands for the native protein structural elements. As expected, there was an increase in thermal stability in D2O relative to that in H2O, which was manifested as an increase of about 5 degrees C in the temperature for initial loss of infrared bands assigned to native structural elements and for appearance of bands due to intermolecular beta-sheet. In addition, the midpoint of the thermally induced transitions in infrared spectra were about 2.5 degrees C higher in D2O than in H2O. Based on the infrared analysis, the thermally denatured state of the protein in both H2O and D2O contains predominantly intermolecular beta-sheet. The broad, poorly resolved absorbance that spans the region between the intermolecular beta-sheet bands was assigned to an ensemble of heterogeneous structural elements (including unordered), none of which is populated to a high enough degree to result in a distinct infrared band. Results from CD spectroscopy support these conclusions about the structure of the denatured, aggregated protein.

Circular Dichroism↗
PubMed2003

Effects of Ce(3+), Cd(2+), and Hg(2+) on activities and secondary structure of trypsin.

The different effects of Ce(3+), Cd(2+), and Hg(2+) on the activities and secondary structure of trypsin were studied. The results showed that trypsin activity was increased substantially by Ce(3+) in 0.5-5 micromol/L concentration, but the activity was decreased significantly by Cd(2+) or Hg(2+) in 0.5-5 micromol/L concentration. The ultraviolet-visible spectrum of trypsin with 4 micromol/L Ce3+ treatment was the same as that of the control, but the 232-nm characteristic peak of trypsin with 4 micromol/L Cd(2+) or Hg(2+) treatment was blue-shifted and the peak intensity weakened. The circular dichroism (CD) spectrum of trypsin with 4 micromol/L Ce(3+) treatment was similar to that of the control. The secondary structure of trypsin did not change with Ce(3+) treatment. However, the CD spectrum of trypsin with 4 micromol/L Cd(2+) or Hg(2+) treatment was different from that of the control and Ce(3+) treatment. The secondary structure of trypsin with Cd(2+) or Hg(2+) treatment changed greatly; for example, the alpha-helix and beta-sheet contents were reduced significantly, the beta-turn was enhanced greatly, and the random coil contents increased or decreased.

Animals↗
PubMed1995

Assignment of the backbone 1H,15N,13C NMR resonances and secondary structure of a double-stranded RNA binding domain from the Drosophila protein staufen.

NMR spectroscopy has been used to determine the secondary structure of one of the double-stranded RNA binding domains from the Drosophila protein staufen. The domain has an alpha beta beta beta alpha arrangement of secondary structure, with the beta strands forming an antiparallel beta sheet. The secondary structure differs from that found in the RNP RNA binding domain.

Amino Acid Sequence↗
PubMed1988

Crossover regions in foot-and-mouth disease virus (FMDV) recombinants correspond to regions of high local secondary structure.

The RNA genome of foot-and-mouth disease virus (FMDV) was analysed for the degree of inverted complementarity and thus potential secondary structure using the procedure of Pustell and Kafatos [Nucleic Acids Res (1982) 10: 4765-4782]. Regions of crossover in 42 FMDV recombinants [King et al. (1985) Virus Res 3: 373-384; Saunders et al. (1985) J Virol 56: 921-929] and regions lacking crossovers were assigned an average secondary structure score against which the number of observed recombinants was plotted. In general it was found that the mean value of potential secondary structure is significantly higher in crossover zones than in recombination-free zones. Recombination increased much more steeply with increasing secondary structure in the part of the genome coding for non-structural proteins than in the 5' third of the genome coding for structural proteins.

Aphthovirus↗
PubMed1995

Omega loops: nonregular secondary structures significant in protein function and stability.

Omega (omega-) loops, a nonregular secondary structure found in globular proteins, are characterized by a polypeptide chain that follows a loop-shaped course in three-dimensional space. They do not contain repeating backbone dihedral angles or regular patterns of hydrogen bonding; however, many omega-loops contain a large number of hydrogen bonds, therefore it is not correct to think of omega-loops as structures lacking in hydrogen bonds. omega-Loops are found almost exclusively at the protein surface and exhibit amino acid preferences consistent with this observation. Since the first description of omega-loops in 1986, experiments have been conducted to probe the role of these structures in protein function, stability, and folding. It has become clear that omega-loops are often involved in protein function and molecular recognition. One motif, an omega-loop lid, that is flexible and mobile until substrate or inhibitor is bound and which probably plays a role in one or more steps of enzymatic catalysis, has been described in a variety of enzymes. Because they lack the periodic hydrogen bonding patterns of the regular secondary structures, some omega-loops are well suited for such functional roles in proteins. However, loops with a higher-than-average number of hydrogen bonds or hydrophobic contacts may play roles in protein stability or folding. Rather than determining further geometric definitions of loops, it may be instructional to group them according to their roles in protein structure, i.e., as categories of functional omega-loops, stability omega-loops, and folding omega-loops.

Amino Acids↗
PubMed2006

Protein secondary structure prediction for a single-sequence using hidden semi-Markov models.

BACKGROUND: The accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algorithms: Single-sequence prediction algorithms imply that information about other (homologous) proteins is not available, while algorithms of the second type imply that information about homologous proteins is available, and use it intensively. The single-sequence algorithms could make an important contribution to studies of proteins with no detected homologs, however the accuracy of protein secondary structure prediction from a single-sequence is not as high as when the additional evolutionary information is present. RESULTS: In this paper, we further refine and extend the hidden semi-Markov model (HSMM) initially considered in the BSPSS algorithm. We introduce an improved residue dependency model by considering the patterns of statistically significant amino acid correlation at structural segment borders. We also derive models that specialize on different sections of the dependency structure and incorporate them into HSMM. In addition, we implement an iterative training method to refine estimates of HSMM parameters. The three-state-per-residue accuracy and other accuracy measures of the new method, IPSSP, are shown to be comparable or better than ones for BSPSS as well as for PSIPRED, tested under the single-sequence condition. CONCLUSIONS: We have shown that new dependency models and training methods bring further improvements to single-sequence protein secondary structure prediction. The results are obtained under cross-validation conditions using a dataset with no pair of sequences having significant sequence similarity. As new sequences are added to the database it is possible to augment the dependency structure and obtain even higher accuracy. Current and future advances should contribute to the improvement of function prediction for orphan proteins inscrutable to current similarity search methods.

Algorithms↗
PubMed1988

A simple method for predicting the secondary structure of globular proteins: implications and accuracy.

A method is presented for predicting the secondary structure of globular proteins from their amino acid sequence. It is based on a rigorous statistical exploitation of the well-known biological fact that the amino acid compositions of each secondary structure are different. We also propose an evaluation process that allows us to estimate the capacity of a method to predict the secondary structure of a new protein which does not have any homologous proteins whose structure is already known. This evaluation process shows that our method has a prediction accuracy of 58.7% over three states for the 62 proteins of the Kabsch and Sander (1983a) data bank. This result is better than that obtained by the most widely used methods--Lim (1974), Chou and Fasman (1978) and Garnier et al. (1978)--and also than that obtained by a recent method based on local homologies (Levin et al., 1986). Our prediction method is very simple and may be implemented on any microcomputer and even on programmable pocket calculators. A simple Pascal implementation of the method prediction algorithm is given. The interpretation of our results in terms of protein folding and directions for further work are discussed.

Algorithms↗
PubMed2002

Evolution of the secondary structure of the rRNA internal transcribed spacer 2 (ITS2) in hard ticks (Ixodidae, Arthropoda).

ITS2 sequences are used extensively in molecular taxonomy and population genetics of arthropods and other animals yet little is known about the molecular evolution of ITS2. We studied the secondary structure of ITS2 in species from each of the six main lineages of hard ticks (family Ixodidae). The ITS2 of these ticks varied in length from 679 bp in Ixodes scapularis to 1547 bp in Aponomma concolor. Nucleotide content varied also: the ITS2 of ticks from the Prostriata lineage (Ixodes spp.) had 46-49% GC whereas ITS2 sequences of ticks from the Metastriata lineage (all other hard ticks) had 61-62% GC. Despite variation in nucleotide sequence, the secondary structure of the ITS2 of all of these ticks apparently has five domains. Stems 1, 3, 4 and 5 of this secondary structure were obvious in all of the species studied. However, stem 2 was not always obvious despite the fact that it is flanked by highly conserved sequence motifs in the adjacent stems, stems 1 and 3. The ITS2 of hard ticks has apparently evolved mostly by increases and decreases in length of the nucleotide sequences, which caused increases, and decreases in the length of stems of the secondary structure. This is most obvious when stems of the secondary structures of the Prostriata (Ixodes spp.) are compared to those of the Metastriata (all other hard ticks). Increases in the size of the ITS2 may have been caused by replication slippage which generated large repeats, like those seen in Haemaphysalis humerosa and species from the Rhipicepalinae lineage, and the small repeats found in species from the other lineages of ticks.

Animals↗
PubMed2001

A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach.

We have introduced a new method of protein secondary structure prediction which is based on the theory of support vector machine (SVM). SVM represents a new approach to supervised pattern classification which has been successfully applied to a wide range of pattern recognition problems, including object recognition, speaker identification, gene function prediction with microarray expression profile, etc. In these cases, the performance of SVM either matches or is significantly better than that of traditional machine learning approaches, including neural networks.The first use of the SVM approach to predict protein secondary structure is described here. Unlike the previous studies, we first constructed several binary classifiers, then assembled a tertiary classifier for three secondary structure states (helix, sheet and coil) based on these binary classifiers. The SVM method achieved a good performance of segment overlap accuracy SOV=76.2 % through sevenfold cross validation on a database of 513 non-homologous protein chains with multiple sequence alignments, which out-performs existing methods. Meanwhile three-state overall per-residue accuracy Q(3) achieved 73.5 %, which is at least comparable to existing single prediction methods. Furthermore a useful "reliability index" for the predictions was developed. In addition, SVM has many attractive features, including effective avoidance of overfitting, the ability to handle large feature spaces, information condensing of the given data set, etc. The SVM method is conveniently applied to many other pattern classification tasks in biology.

Computer Simulation↗
PubMed2003

Effects of detergents on the secondary structures of prion protein peptides as studied by CD spectroscopy.

Pathogenic prion proteins (PrP(Sc)) are thought to be produced by alpha-helical to beta-sheet conformational changes in the normal cellular prion proteins (PrP(C)) located solely in the caveolar compartments. In order to inquire into the possible conformational changes due to the influences of hydrophobic environments within caveolae, the secondary structures of prion protein peptides were studied in various kinds of detergents by CD spectra. The peptides studied were PrP(129-154) and PrP(192-213); the former is supposed to assume beta-sheets and the latter alpha-helices, in PrP(Sc). The secondary structure analyses for the CD spectra revealed that in buffer solutions, both PrP(129-154) and PrP(192-213) mainly adopted random-coils (approximately 60%), followed by beta-sheets (30%-40%). PrP(129-154) showed no changes in the secondary structures even in various kinds of detergents such as octyl-beta-D-glucopyranoside (OG), octy-beta-D-maltopyranoside (OM). sodium dodecyl sulfate (SDS), Zwittergent 3-14 (ZW) and dodecylphosphocholine (DPC). In contrast, PrP(192-213) changed its secondary structure depending on the concentration of the detergents. SDS, ZW, OG and OM increased the alpha-helical content, and decreased the beta-sheet and random-coil contents. DPC also increased the alpha-helical content, but to a lesser extent than did SDS, ZW, OG or OM. These results indicate that PrP(129-154) has a propensity to adopt predominantly beta-sheets. On the other hand, PrP(192-213) has a rather fickle propensity and varies its secondary structure depending on the environmental conditions. It is considered that the hydrophobic environments provided by these detergents may mimic those provided by gangliosides in caveolae, the head groups of which consist of oligosaccharide chains containing sialic acids. It is concluded that PrP(C) could be converted into a nascent PrP(Sc) having a transient PrP(Sc) like structureunder the hydrophobic environments produced by gangliosides.

Animals↗
PubMedDate not supplied

Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction.

In this paper, we develop a segmental semi-Markov model (SSMM) for protein secondary structure prediction which incorporates multiple sequence alignment profiles with the purpose of improving the predictive performance. The segmental model is a generalization of the hidden Markov model where a hidden state generates segments of various length and secondary structure type. A novel parameterized model is proposed for the likelihood function that explicitly represents multiple sequence alignment profiles to capture the segmental conformation. Numerical results on benchmark data sets show that incorporating the profiles results in substantial improvements and the generalization performance is promising. By incorporating the information from long range interactions in beta-sheets, this model is also capable of carrying out inference on contact maps. This is an important advantage of probabilistic generative models over the traditional discriminative approach to protein secondary structure prediction. The Web server of our algorithm and supplementary materials are available at http://public.kgi.edu/-wild/bsm.html.

Algorithms↗