Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Secondary structure”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10Linked to original sources
PubMed2003

Global secondary structure packing angle bias in proteins.

While studies of secondary structure interactions have focused on local interacting features, there is a need for a more global characterization of packing-induced aligned packing of secondary structures. This study presents an analysis of the distribution of globally sampled secondary structures within selected subunits of a selected set of multimeric proteins. Comparisons are made between the distribution of the cosines of angles between triplets of linear segments associated to secondary structures and a theoretically obtained distribution for triplets of random uniformly distributed unit vectors. We show that, among all pairs of helix or strand segments, planar configurations appear more frequently than expected for uniformly distributed vectors, and alignment is strongly preferred compared to that expected for uniformly distributed vector triplets. Among all secondary structure triplets, pairs of angle cosines between helix strand segments deviate from uniformity corresponding to alignment and anti-alignment. Furthermore, among all helix or strand segments, including non-interacting secondary structures, the distribution of a single angle cosine indicates a strong preference for alignment and anti-alignment. Selection for interactive triplets shows results consistent with prior studies. Lastly, angle pairs are not statistically independent, indicating that alignment between two helix or strand segments is more likely if another helix or strand is aligned with either of the first two helices or strands. Selection for interactive segment triplets shows results consistent with prior studies.

Models, Molecular↗
PubMed1999

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.

Algorithms↗
PubMed1988

Primary and secondary structure of the 18S ribosomal RNA of the bird spider Eurypelma californica and evolutionary relationships among eukaryotic phyla.

The primary structure of the 18S rRNA of the bird spider Eurypelma californica has been determined in the framework of a study of metazoan phylogeny on the basis of ribosomal RNA structure. A secondary-structure model was derived by comparison of the sequence with that of 43 other eukaryotic small-ribosomal-subunit RNA sequences presently available. This comparison allows a rather detailed secondary-structure pattern to be postulated for a eukaryote-specific area of highly variable sequence and length for which no consensus model has hitherto been attained. A dendrogram, reflecting evolutionary relationships among the 40 eukaryotic species of known 18S rRNA structure, was constructed by a matrix method selecting the best-fitting tree on the basis of a least-squares criterion. The tree shows an early divergence of a microsporidium, an euglenoid, kinetoplastids and a slime mold. Among the remaining species, two main clusters are distinguishable, one comprising the Ciliata, the other comprising Metazoa, green plants, fungi and several protists. Among the Metazoa, the three phyla presently investigated, viz. Chordata, Arthropoda and Nemathelminthes, are distinguishable as three separate lines of descent.

Animals↗
PubMed1997

The role of long-range interactions in defining the secondary structure of proteins is overestimated.

MOTIVATION: Secondary structure predictions based on the properties of individual residues, and sometimes on local interactions, usually fail to exceed 65% efficiency. Therefore, non-local, long-range interactions seem to be a significant cause of this limitation. RESULTS: In this paper, we apply approaches to localize highly interacting residues and clusters of residues involved in multiple non-local interactions, and test various secondary structure predictions on this separate subset to assess the effect of long-range interactions on the prediction efficiencies. It was found that only a marginal part of the failure of secondary structure predictions results from the presence of long-range interactions. Alternative possibilities are also discussed.

Algorithms↗
PubMed1999

Decision tree-based formation of consensus protein secondary structure prediction.

MOTIVATION: Prediction of protein secondary structure provides information that is useful for other prediction methods like fold recognition and ab initio 3D prediction. A consensus prediction constructed from the output of several methods should yield more reliable results than each of the individual methods. METHOD: We present an approach that reveals subtle but systematic differences in the output of different secondary structure prediction methods allowing the derivation of coherent consensus predictions. The method uses a machine learning technique that builds decision trees from existing data. RESULTS: The first results of our analysis show that consensus prediction of protein secondary structure may be improved both quantitatively and qualitatively.

Algorithms↗
PubMed1985

Comparative sequence analysis as a tool for studying the secondary structure of mRNAs.

Analysis of phylogenetically conserved secondary structure has been important in the development of models for the secondary structure of structural RNAs. In this paper, we apply this type of analysis to several families of informational RNAs to evaluate its usefulness in developing secondary structure models for mRNAs and mRNA precursors. We observed many conserved helices in all mRNA groups analyzed. Three criteria were used to identify potential helices which were not conserved solely because of coding sequence constraints, and may therefore be important for the structure and function of the RNA. These results suggest that this approach will be useful in deriving secondary structure models for informational RNAs when used in conjunction with other complementary techniques, and in designing experiments to determine the functional significance of conserved base pairing interactions.

Actins↗
PubMed2001

Elucidating protein secondary structures using alpha-carbon recurrence quantifications.

Secondary structures of proteins were studied by recurrence quantification analysis (RQA). High-resolution, 3-dimensional coordinates of alpha-carbon atoms comprising a set of 68 proteins were downloaded from the Protein Data Bank. By fine-tuning four recurrence parameters (radius, line, residue, separation), it was possible to establish excellent agreement between percent contribution of alpha-helix and beta-sheet structures determined independently by RQA and that of the DSSP algorithm (Define Secondary Structure of Proteins). These results indicate that there is an equivalency between these two techniques, which are based upon totally different pattern recognition strategies. RQA enhances qualitative contact maps by quantifying the arrangements of recurrent points of alpha carbons close in 3-dimensional space. For example, the radius was systematically increased, moving the analysis beyond local alpha-carbon neighborhoods in order to capture super-secondary and tertiary structures. However, differences between proteins could only be detected within distances up to about 6-11 A, but not higher. This result underscores the complexity of alpha-carbon spacing when super-secondary structures appear at larger distances. Finally, RQA-defined secondary structures were found to be robust against random displacement of alpha carbons upwards of 1 A. This finding has potential import for the dynamic functions of proteins in motion.

Bacterial Proteins↗
PubMed1993

Secondary structure of proteins associated in thin films.

The solid state secondary structure of myoglobin, RNase A, concanavalin A (Con A), poly(L-lysine), and two linear heterooligomeric peptides were examined by both far-uv CD spectroscopy1 and by ir spectroscopy. The proteins associated from water solution on glass and mica surfaces into noncrystalline, amorphous films, as judged by transmission electron microscopy of carbon-platinum replicas of surface and cross-fractured layer. The association into the solid state induced insignificant changes in the amide CD spectra of all alpha-helical myoglobin, decreased the molar ellipticity of the alpha/beta RNase A, and increased the molar ellipticity of all-beta Con A with no change in the positions of the bands' maxima. High-temperature exposure of the films induced permanent changes in the conformation of all proteins, resulting in less alpha-helix and more beta-sheet structure. The results suggest that the protein alpha-helices are less stable in films and that the secondary structure may rearrange into beta-sheets at high temperature. Two heterooligomeric peptides and poly(L-lysine), all in solution at neutral pH with "random coil" conformation, formed films with variable degrees of their secondary structure in beta-sheets or beta-turns. The result corresponded to the protein-derived Chou-Fasman amino acid propensities, and depended on both temperature and solvent used. The ir and CD spectra correlations of the peptides in the solid state indicate that the CD spectrum of a "random" structure in films differs from random coil in solution. Formic acid treatment transformed the secondary structure of the protein and peptide films into a stable alpha-helix or beta-sheet conformations. The results indicate that the proteins aggregate into a noncrystalline, glass-like state with preserved secondary structure. The solid state secondary structure may undergo further irreversible transformations induced by heat or solvent.

Amino Acid Sequence↗
PubMed1994

Secondary structure of the nicotinic acetylcholine receptor: implications for structural models of a ligand-gated ion channel.

The secondary structure and effects of two ligands, carbamylcholine and tetracaine, on the secondary structure of affinity-purified nicotinic acetylcholine receptor (nAChR) from Torpedo has been studied using Fourier transform infrared spectroscopy (FTIR). FTIR spectra of the nAChR were acquired in both 1H2O and 2H2O buffer and exhibit spectral features indicative of a substantial alpha-helical content with lesser amounts of beta-sheet and random coil structures. The resolution enhancement techniques of Fourier self-deconvolution and Fourier derivation reveal seven component bands contributing to both the amide I band and amide I' band contours in 1H2O and 2H2O, respectively. Curve-fitting estimates of the nAChR secondary structure are consistent with the qualitative analysis of the FTIR spectra as follows: 39% alpha-helix, 35% beta-sheet, 6% turn, and 20% random coil. Of particular interest is the estimated alpha-helical content as this value places restrictions on models of the nAChR transmembrane topology and on the types of secondary structures that may contribute to functional domains, such as the ligand-binding site. The estimated alpha-helical content is sufficient to account for four transmembrane alpha-helices in each nAChR subunit as well as a substantial portion of the extracellular and/or the cytoplasmic domains. FTIR spectra were also acquired in the presence and absence of 1 mM carbamylcholine and 5 mM tetracaine to examine the effects of ligand binding on the secondary structure of the nAChR. The similarity of the spectra, even after spectral deconvolution, indicates that the secondary structure of the nAChR is essentially unaffected by desensitization.(ABSTRACT TRUNCATED AT 250 WORDS)

Animals↗
PubMed2006

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. RESULTS: Selecting a category in each one of the processed tables lists all single point mutations belonging to that category. Selecting a mutation displays a graphical drawing of the single point mutation and the wild type, and includes basic information such as associated energies, representations and distances. RNAMute can be used successfully with very little previous experience and without choosing any parameter value alongside the initial RNA sequence. The package runs under LINUX operating system. CONCLUSION: RNAMute is a user friendly tool that can be used to predict single point mutations leading to conformational rearrangements in the secondary structure of RNAs. In several cases of substantial interest, notably in virology, a point mutation may lead to a loss of important functionality such as the RNA virus replication and translation initiation because of a conformational rearrangement in the secondary structure.

Animals↗
PubMed1996

Structure prediction of the EcoRV DNA methyltransferase based on mutant profiling, secondary structure analysis, comparison with known structures of methyltransferases and isolation of catalytically inactive single mutants.

The EcoRV DNA methyltransferase (M.EcoRV) is an alpha-adenine methyltransferase. We have used two different programs to predict the secondary structure of M.EcoRV. The resulting consensus prediction was tested by a mutant profiling analysis. 29 neutral mutations of M.EcoRV were generated by five cycles of random mutagenesis and selection for active variants to increase the reliability of the prediction and to get a secondary structure prediction for some ambiguously predicted regions. The predicted consensus secondary structure elements could be aligned to the common topology of the structures of the catalytic domains of M.HhaI and M.TaqI. In a complementary approach we have isolated nine catalytically inactive single mutants. Five of these mutants contain an amino acid exchange within the catalytic domain of M.EcoRV (Val2-Ala, Lys81Arg, Cys192Arg, Asp193Gly, Trp231Arg). The Trp231Arg mutant binds DNA similarly to wild-type M.EcoRV, but is catalytically inactive. Hence this mutant behaves like a bona fide active site mutant. According to the structure prediction, Trp231 is located in a loop at the putative active site of M.EcoRV. The other inactive mutants were insoluble. They contain amino acid exchanges within the conserved amino acid motifs X, III or IV in M.EcoRV confirming the importance of these regions.

Amino Acid Sequence↗
PubMed1986

A dynamic programming algorithm for finding alternative RNA secondary structures.

Dynamic programming algorithms that predict RNA secondary structure by minimizing the free energy have had one important limitation. They were able to predict only one optimal structure. Given the uncertainties of the thermodynamic data and the effects of proteins and other environmental factors on structure, the optimal structure predicted by these methods may not have biological significance. We present a dynamic programming algorithm that can determine optimal and suboptimal secondary structures for an RNA. The power and utility of the method is demonstrated in the folding of the intervening sequence of the rRNA of Tetrahymena. By first identifying the major secondary structures corresponding to the lowest free energy minima, a secondary structure of possible biological significance is derived.

Animals↗
PubMed1984

Recent advances in structural analysis of gangliosides: primary and secondary structures.

High-field (500 MHz) proton NMR has been used to elucidate the primary and secondary structures of glycosphingolipids (GSLs). Using 2-D J-correlated spectroscopy (2-D SECSY) which establishes scalar couplings of protons, the monosaccharide composition, anomeric configuration and aglycon structures of a GSL can be established. 2-D nuclear Overhauser effect spectroscopy (2-D NOE) then establishes through-space intra- and inter-residue couplings of cross-relaxing protons. We have found that each anomeric proton is involved in NOE couplings with inter- and intra-residue protons. The inter-residue coupling, resulting from interaction of protons across the glycosidic linkage, establishes the n-1 sugar residue and specific glycosidation site to which the n-residue is linked. When such information is known for each residue and is combined, the sequence of the core oligosaccharide is obtained. The sialylation-induced glycosidation shift is then used to establish the site of sialic acid residue attachment in a ganglioside molecule. We have also observed that the anomeric proton inter-residue NOE couplings can be used to suggest the preferred conformation of an oligosaccharide. We have found that the oligosaccharide residue of globoside exists in a unique and rather rigid conformation which could be stabilized by hydrogen bonds and van der Waals interactions. Since GSLs are known to have a receptor role and are implicated in cell-cell recognition, enzyme-substrate interaction and antigen-antibody interaction, the determination of their conformation should be useful in understanding their biological functions.

Animals↗
PubMed2006

Prediction of RNA secondary structure by free energy minimization.

RNA secondary structure is often predicted from sequence by free energy minimization. Over the past two years, advances have been made in the estimation of folding free energy change, the mapping of secondary structure and the implementation of computer programs for structure prediction. The trends in computer program development are: efficient use of experimental mapping of structures to constrain structure prediction; use of statistical mechanics to improve the fidelity of structure prediction; inclusion of pseudoknots in secondary structure prediction; and use of two or more homologous sequences to find a common structure.

Base Sequence↗
PubMedDate not supplied

Review: protein secondary structure prediction continues to rise.

Methods predicting protein secondary structure improved substantially in the 1990s through the use of evolutionary information taken from the divergence of proteins in the same structural family. Recently, the evolutionary information resulting from improved searches and larger databases has again boosted prediction accuracy by more than four percentage points to its current height of around 76% of all residues predicted correctly in one of the three states, helix, strand, and other. The past year also brought successful new concepts to the field. These new methods may be particularly interesting in light of the improvements achieved through simple combining of existing methods. Divergent evolutionary profiles contain enough information not only to substantially improve prediction accuracy, but also to correctly predict long stretches of identical residues observed in alternative secondary structure states depending on nonlocal conditions. An example is a method automatically identifying structural switches and thus finding a remarkable connection between predicted secondary structure and aspects of function. Secondary structure predictions are increasingly becoming the work horse for numerous methods aimed at predicting protein structure and function. Is the recent increase in accuracy significant enough to make predictions even more useful? Because the recent improvement yields a better prediction of segments, and in particular of beta strands, I believe the answer is affirmative. What is the limit of prediction accuracy? We shall see.

Computational Biology↗
PubMed1996

Context-dependent secondary structure formation of a designed protein sequence.

Protein secondary structures have been viewed as fundamental building blocks for protein folding, structure and design. Previous studies indicate that the propensities of individual amino acids to form particular secondary structures are the result of a combination of local conformational preferences and non-local factors. To examine the extent to which non-local factors influence the formation of secondary structural elements, we have designed an 11-amino-acid sequence (dubbed the 'chameleon' sequence) that folds as an alpha-helix when in one position but as a beta-sheet when in another position of the primary sequence of the IgG-binding domain of protein G (GB1). Both proteins, chameleon-alpha and chameleon-beta, are folded into structures similar to native GB1, as judged by several biophysical criteria. Our results demonstrate that non-local interactions can determine the secondary structure of peptide sequences of substantial length. They also support views of protein folding that favour tertiary interactions as dominant determinants of structure.

Amino Acid Sequence↗
PubMed1994

Predicted secondary structure of the 20 S proteasome and model structure of the putative peptide channel.

Secondary structure prediction has made great progress in recent years due to the incorporation of evolutionary information, and may be close to a point where (in combination with biochemical and low-resolution structural data) it can guide the modelling of tertiary structure in cases where no model building is possible by homology. Towards this goal it is important to gather information on the performance of prediction methods in advance of the publication of new structures. In anticipation of the soon-to-be-released structure of the 20 S proteasome from Thermoplasma acidophilum, we have applied several widely used secondary structure prediction methods to proteasome sequences and have attempted to model the putative channel in the outer proteasome rings (alpha-rings) based on the obtained predictions.

Amino Acid Sequence↗
PubMed2002

A novel fold recognition method using composite predicted secondary structures.

In this work, we introduce a new method for fold recognition using composite secondary structures assembled from different secondary structure prediction servers for a given target sequence. An automatic, complete, and robust way of finding all possible combinations of predicted secondary structure segments (SSS) for the target sequence and clustering them into a few flexible clusters, each containing patterns with the same number of SSS, is developed. This program then takes two steps in choosing plausible homologues: (i) a SSS-based alignment excludes impossible templates whose SSS patterns are very different from any of those of the target; (ii) a residue-based alignment selects good structural templates based on sequence similarity and secondary structure similarity between the target and only those templates left in the first stage. The secondary structure of each residue in the target is selected from one of the predictions to find the best match with the template. Truncation is applied to a target where different predictions vary. In most cases, a target is also divided into N-terminal and C-terminal fragments, each of which is used as a separate subsequence. Our program was tested on the fold recognition targets from CASP3 with known PDB codes and some available targets from CASP4. The results are compared with a structural homologue list for each target produced by the CE program (Shindyalov and Bourne, Protein Eng 1998;11:739-747). The program successfully locates homologues with high Z-score and low root-mean-score deviation within the top 30-50 predictions in the overwhelming majority of cases.

Algorithms↗