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At least 127 records · Page 7Linked to original sources
PubMed2001

Protein anatomy: structure and function of peptide fragments corresponding to the secondary structure units of barnase.

Globular proteins can be decomposed into several modules or secondary structure units. It is useful to investigate the functions of such structural units in order to understand the folding units of proteins. In our previous work, barnase was divided into six peptide fragments corresponding to modules, and some of them were shown to have RNA-binding and RNase activity [Yanagawa, et al. (1993) J. Biol. Chem. 268, 5861-5865]. Barnase mutant proteins obtained by permutation of the structural units also had RNase activity [Tsuji, T. et al. (1999) J. Mol. Biol. 286, 1581-1596]. Here we investigated the structure and function of peptide fragments corresponding to secondary structure units of barnase. The results of circular dichroism spectroscopy indicated that some of the peptide fragments form helical structures in aqueous solutions containing over 30% 2,2,2-trifluoroethanol, and the S6 (94-110) peptide fragment is induced to form a beta-sheet structure in the presence of RNA. The S6 peptide fragment forms aggregate complexes with RNA. Electron microscopic analysis showed that the aggregate complexes were comprised of filaments. These results indicate that not only modules but also secondary structure units dissected from a globular protein have functional and structure-forming capabilities.

Amino Acid Sequence↗
PubMed2007

Secondary structure length as a determinant of folding rate of proteins with two- and three-state kinetics.

We present a simple method for determining the folding rates of two- and three-state proteins from the number of residues in their secondary structures (secondary structure length). The method is based on the hypothesis that two- and three-state foldings share a common pattern. Three-state proteins first condense into metastable intermediates, subsequent forming of alpha-helices, turns, and beta-sheets at slow rate-limiting step. The folding rate of such proteins anticorrelate with the length of these beta-secondary structures. It is also assumed that in two-state folding, rapidly folded alpha-helices and turns may facilitate formation of fleeting unobservable intermediates and thus show two-state behavior. There is an inverse relationship between the folding rate and the length of beta-sheets and loops. Our study achieves 94.0 and 88.1% correlations with folding rates determined experimentally for 21 three- and 38 two-state proteins, respectively, suggesting that protein-folding rates are determined by the secondary structure length. The kinetic kinds are selected on the basis of a competitive formation of hydrophobic collapse and alpha-structure in early intermediates.

Kinetics↗
PubMed1996

Protein fold recognition by mapping predicted secondary structures.

A strategy is presented for protein fold recognition from secondary structure assignments (alpha-helix and beta-strand). The method can detect similarities between protein folds in the absence of sequence similarity. Secondary structure mapping first identifies all possible matches (maps) between a query string of secondary structures and the secondary structures of protein domains of known three-dimensional structure. The maps are then passed through a series of structural filters to remove those that do not obey simple rules of protein structure. The surviving maps are ranked by scores from the alignment of predicted and experimental accessibilities. Searches made with secondary structure assignments for a test set of 11 fold-families put the correct sequence-dissimilar fold in the first rank 8/11 times. With cross-validated predictions of secondary structure this drops to 4/11 which compares favourably with the widely used THREADER program (1/11). The structural class is correctly predicted 10/11 times by the method in contrast to 5/11 for THREADER. The new technique obtains comparable accuracy in the alignment of amino acid residues and secondary structure elements. Searches are also performed with published secondary structure predictions for the von-Willebrand factor type A domain, the proteasome 20 S alpha subunit and the phosphotyrosine interaction domain. These searches demonstrate how the method can find the correct fold for a protein from a carefully constructed secondary structure prediction, multiple sequence alignment and distant restraints. Scans with experimentally determined secondary structures and accessibility, recognise the correct fold with high alignment accuracy (86% on secondary structures). This suggests that the accuracy of mapping will improve alongside any improvements in the prediction of secondary structure or accessibility. Application to NMR structure determination is also discussed.

Algorithms↗
PubMed1996

Sequence and structure determinants of Drosophila Hsp70 mRNA translation: 5'UTR secondary structure specifically inhibits heat shock protein mRNA translation.

Preferential translation of Drosophila heat shock protein 70 (Hsp70) mRNA requires only the 5'-untranslated region (5'-UTR). The sequence of this region suggests that it has relatively little secondary structure, which may facilitate efficient protein synthesis initiation. To determine whether minimal 5'-UTR secondary structure is required for preferential translation during heat shock, the effect of introducing stem-loops into the Hsp70 mRNA 5'-UTR was measured. Stem-loops of -11 kcal/mol abolished translation during heat shock, but did not reduce translation in non-heat shocked cells. A -22 kcal/mol stem-loop was required to comparably inhibit translation during growth at normal temperatures. To investigate whether specific sequence elements are also required for efficient preferential translation, deletion and mutation analyses were conducted in a truncated Hsp70 5'-UTR containing only the cap-proximal and AUG-proximal segments. Linker-scanner mutations in the cap-proximal segment (+1 to +37) did not impair translation. Re-ordering the segments reduced mRNA translational efficiency by 50%. Deleting the AUG-proximal segment severely inhibited translation. A 5-extension of the full-length leader specifically impaired heat shock translation. These results indicate that heat shock reduces the capacity to unwind 5-UTR secondary structure, allowing only mRNAs with minimal 5'-UTR secondary structure to be efficiently translated. A function for specific sequences is also suggested.

Animals↗
PubMed1981

A unique secondary folding pattern for 5S RNA corresponds to the lowest energy homologous secondary structure in 17 different prokaryotes.

A general secondary structure is proposed for the 5S RNA of prokaryotic ribosomes, based on helical energy filtering calculations. We have considered all secondary structures that are common to 17 different prokaryotic 5S RNAs and for each 5S sequence calculated the (global) minimum energy secondary structure (300,000 common structures are possible for each sequence). The 17 different minimum energy secondary structures all correspond, with minor differences, to a single, secondary structure model. This is strong evidence that this general 5S folding pattern corresponds to the secondary structure of the functional 5S rRNA. The general 5S secondary structure is forked and in analogy with the cloverleaf of tRNA is named the "wishbone" model. It constant 8 double helical regions; one in the stem, four in the first, or constant arm, and three in the second arm. Four of these double helical regions are present in a model earlier proposed (1) and four additional regions not proposed by them are presented here. In the minimum energy general structure, the four helices in the constant arm are exactly 15 nucleotide pairs long. These helices are stacked in the sequences from gram-positive bacteria and probably stacked in gram-negative sequences as well. In sequences from gram-positive bacteria the length of the constant arm is maintained at 15 stacked pairs by an unusual minimum energy interaction involving a C26-G57 base pair intercalated between two adjacent helical regions.

Bacteria↗
PubMed1991

Kringle-2 domain of the tissue-type plasminogen activator. 1H-NMR assignments and secondary structure.

A recombinant 90-residue polypeptide fragment containing the three-loop kringle-2 domain of human tissue-type plasminogen activator (t-PA) has been studied by two-dimensional 1H-NMR spectroscopy at 500 MHz. Complete sequence-specific resonance assignments were derived. Overall, the kringle exhibits a compact, folded conformation with more than 50% of the residues in irregular structures. Elements of secondary structure were identified from sequential, medium- and long-range dipolar (Overhauser) interproton interactions. These identifications were corroborated by analysis of spin-spin scalar 3J alpha N splittings and identification of backbone amide NH protons exhibiting retarded 1H/2H exchange in 2H2O. Three antiparallel beta-sheets and six tight turns were located. In addition, one short alpha-helical region was found in the Ser43-Ala44-Gln44a-Ala44b-Leu44c-Gly45+ ++ segment; this region contains three-residue insertions unique to the t-PA and urokinase kringles. Although the secondary structure of the t-PA kringle 2 in solution is in overall agreement with that observed in the crystallographic structure of the prothrombin kringle 1 [Tulinsky, A., Park, C.H. & Skrzypczak-Jankun, E. (1988) J. Mol. Biol. 202, 885-901], the alpha-helical segment and other details of the secondary structure differ somewhat from the prothrombin homolog.

Amino Acid Sequence↗
PubMed1999

Secondary structure forming propensity coupled with amphiphilicity is an optimal motif in a peptide or protein for association with chaperonin 60 (GroEL).

The interactions of GroEL with six dansyl peptides were investigated by means of our previously established fluorescence binding assay [Hutchinson, J. P., Oldham, T. C., El-Thaher, T. S. H., and Miller, A. D. (1997) J. Chem. Soc., Perkin Trans. 2, 279-288]. Three peptides (AMPH series) were constructed with a hierarchy of alpha-helix-forming propensities and amphiphilic characteristics. The remaining three peptides (NON-AMPH series) were prepared with a reordered amino acid sequence designed to form peptides of differing non-amphiphilic alpha-helix-forming propensity. Of these six peptides, two (AMPH(+) and NON-AMPH(+)) were N-capped with an S-form alpha-helix-inducing template (Ro 47-1615, Hoffmann-La Roche), two (AMPH(-) and NON-AMPH(-)) were N-capped with an R-form non-inducing template (Ro 47-1614, Hoffmann-La Roche), and two (AMPH(R) and NON-AMPH(R)) were without N-cap modification. This paper describes how the known strength of interaction of an unfolded protein substrate with the molecular chaperone GroEL (K(d) micromolar to nanomolar) may be emulated with a single peptide (AMPH(+)) (apparent K(d) 5 nM) which has a high propensity to form an amphiphilic alpha-helical structure in solution. Secondary structure forming propensity is not, in and of itself, an important contributor to the strength of interaction with GroEL. However, secondary structure forming propensity coupled with amphiphilicity may be sufficient to account for most, if not all, of the interaction strength between GroEL and an unfolded peptide or protein substrate.

Binding, Competitive↗
PubMed2003

Controlling nucleic acid secondary structure by intercalation: effects of DNA strand length on coralyne-driven duplex disproportionation.

Small molecules that intercalate in DNA and RNA are powerful agents for controlling nucleic acid structural transitions. We recently demonstrated that coralyne, a small crescent-shaped molecule, can cause the complete and irreversible disproportionation of duplex poly(dA)*poly(dT) into triplex poly(dA)*poly(dT)*poly(dT) and a poly(dA) self-structure. Both DNA secondary structures that result from duplex disproportionation are stabilized by coralyne intercalation. In the present study, we show that the kinetics and thermodynamics of coralyne-driven duplex disproportionation strongly depend on oligonucleotide length. For example, disproportionation of duplex (dA)16*(dT)16 by coralyne reverts over the course of hours if the sample is maintained at 4 degrees C. Coralyne-disproportioned (dA)32. (dT)(32), on the other hand, only partially reverts to the duplex state over the course of days at the same temperature. Furthermore, the equilibrium state of a (dA)16*(dT)16 sample in the presence of coralyne at room temperature contains three different secondary structures [i.e. duplex, triplex and the (dA)16 self-structure]. Even the well-studied process of triplex stabilization by coralyne binding is found to be a length-dependent phenomenon and more complicated than previously appreciated. Together these observations indicate that at least one secondary structure in our nucleic acid system [i.e. duplex, triplex or (dA)n self-structure] binds coralyne in a length-dependent manner.

Berberine Alkaloids↗
PubMed2004

Secondary structure of a dynamic type I'beta-hairpin peptide.

A spontaneously folding beta-hairpin peptide (Lys-Lys-Tyr-Thr-Val-Ser-Ile-Asn-Gly-Lys-Lys-Ile-Thr-Val-Ser-Ile) and related cyclic (cyclo-Gly-Lys-Tyr-Ile-Asn-Gly-Lys-Ile-Ile-Asn) and linear (Ser-Ile-Asn-Gly-Lys) controls were studied to determine the effects of various factors on secondary structure. Secondary structure was evaluated using circular dichroism (CD) and 1D and 2D (1)H nuclear magnetic resonance (NMR). The effects of chemical modifications in the peptide and various solution conditions were investigated to determine their impact on peptide structure. The beta-hairpin peptide displayed a CD minimum at 216 nm and a TOCSY i + 1 - i + 2 and i + 2 -i + 3 interaction, confirming the expected structure. Using NMR alpha-proton (H(alpha)) chemical shifts, the extents of folding of the beta-hairpin and linear control were estimated to be 51 and 25% of the cyclic control (pH 4, 37 degrees C), which was taken to be maximally folded. Substitution of iso-aspartic acid for Asn reduced the secondary structure dramatically; substitution of aspartic acid for Asn also disrupted the structure. This result suggests that deamidation in unconstrained beta-turns may have adverse effects on secondary structure. N-terminal acetylation and extreme pH conditions also reduced structure, while the addition of methanol increased structure.

Amino Acid Sequence↗
PubMed2003

A partition function algorithm for nucleic acid secondary structure including pseudoknots.

Nucleic acid secondary structure models usually exclude pseudoknots due to the difficulty of treating these nonnested structures efficiently in structure prediction and partition function algorithms. Here, the standard secondary structure energy model is extended to include the most physically relevant pseudoknots. We describe an O(N(5)) dynamic programming algorithm, where N is the length of the strand, for computing the partition function and minimum energy structure over this class of secondary structures. Hence, it is possible to determine the probability of sampling the lowest energy structure, or any other structure of particular interest. This capability motivates the use of the partition function for the design of DNA or RNA molecules for bioengineering applications.

Algorithms↗
PubMed2003

Comparative analysis of mt LSU rRNA secondary structures of Odonates: structural variability and phylogenetic signal.

Secondary structures of the most conserved part of the mt 16S rRNA gene, domains IV and V, have been recently analysed in a comparative study. However, full secondary structures of the mt LSU rRNA molecule are published for only a few insect species. The present study presents full secondary structures of domains I, II, IV and V of Odonates and one representative of mayflies, Ephemera sp. The reconstructions are based on a comparative approach and minimal consensus structures derived from sequence alignments. The inferred structures exhibit remarkable similarities to the published Drosophila melanogaster model, which increases confidence in these structures. Structural variance within Odonates is homoplastic, and neighbour-joining trees based on tree edit distances do not correspond to any of the phylogenetically expected patterns. However, despite homoplastic quantitative structural variation, many similarities between Odonates and Ephemera sp. suggest promising character sets for higher order insect systematics that merit further investigations.

Animals↗
PubMed2004

The influence of gapped positions in multiple sequence alignments on secondary structure prediction methods.

All currently leading protein secondary structure prediction methods use a multiple protein sequence alignment to predict the secondary structure of the top sequence. In most of these methods, prior to prediction, alignment positions showing a gap in the top sequence are deleted, consequently leading to shrinking of the alignment and loss of position-specific information. In this paper we investigate the effect of this removal of information on secondary structure prediction accuracy. To this end, we have designed SymSSP, an algorithm that post-processes the predicted secondary structure of all sequences in a multiple sequence alignment by (i) making use of the alignment's evolutionary information and (ii) re-introducing most of the information that would otherwise be lost. The post-processed information is then given to a new dynamic programming routine that produces an optimally segmented consensus secondary structure for each of the multiple alignment sequences. We have tested our method on the state-of-the-art secondary structure prediction methods PHD, PROFsec, SSPro2 and JNET using the HOMSTRAD database of reference alignments. Our consensus-deriving dynamic programming strategy is consistently better at improving the segmentation quality of the predictions compared to the commonly used majority voting technique. In addition, we have applied several weighting schemes from the literature to our novel consensus-deriving dynamic programming routine. Finally, we have investigated the level of noise introduced by prediction errors into the consensus and show that predictions of edges of helices and strands are half the time wrong for all the four tested prediction methods.

Algorithms↗
PubMed2006

Combinatorics of saturated secondary structures of RNA.

Following Zuker (1986), a saturated secondary structure for a given RNA sequence is a secondary structure such that no base pair can be added without violating the definition of secondary structure, e.g., without introducing a pseudoknot. In the Nussinov-Jacobson energy model (Nussinov and Jacobson, 1980), where the energy of a secondary structure is -1 times the number of base pairs, saturated secondary structures are local minima in the energy landscape, hence form kinetic traps during the folding process. Here we present recurrence relations and closed form asymptotic limits for combinatorial problems related to the number of saturated secondary structures. In addition, Python source code to compute the number of saturated secondary structures having k base pairs can be found at the web servers link of bioinformatics.bc.edu/clotelab/.

Base Pairing↗
PubMed1989

Prediction of protein secondary structure based on physical theory. Histones.

Secondary structures of histones H1, H2A, H2B, H3, H4 and H5 have been calculated by the computer program ALB based on a molecular theory of protein secondary structure. The predicted secondary structures of all histones are predominantly alpha-helical. The calculated secondary structure of linker histones H1 and H5 is close to that previously obtained from two-dimensional NMR data. For each of the core histones (H2A, H2B, H3, H4) one long alpha-helix and several short ones have been predicted. These long helices can be identified with rods in the low-resolution electron density map.

Computer Simulation↗
PubMed2006

Novel methods for secondary structure determination using low wavelength (VUV) circular dichroism spectroscopic data.

BACKGROUND: Circular Dichroism (CD) spectroscopy is a widely used method for studying protein structures in solution. Modern synchrotron radiation CD (SRCD) instruments have considerably higher photon fluxes than do conventional lab-based CD instruments, and hence have the ability to routinely measure CD data to much lower wavelengths. Recently a new reference dataset of SRCD spectra of proteins of known structure, designed to cover secondary structure and fold space, has been produced which includes low wavelength (vacuum ultraviolet - VUV) data. However, the existing algorithms used to calculate protein secondary structures from CD data have not been designed to take optimal advantage of the additional information in these low wavelength data. RESULTS: In this study, we have optimised secondary structure calculation methods based on the low wavelength CD data by examining existing algorithms and secondary structure assignment schemes, and then developing new methods which have produced clear improvements in prediction accuracy, especially for beta-sheet components. We have further shown that if precise measurements of protein concentrations, and therefore spectral magnitudes, are not available, the inclusion of the low wavelength data will significantly improve the analyses. However, we have also demonstrated that the new reference dataset, methods, and assignments can also improve the analyses of conventional circular dichroism data, even if the low wavelength data is not available. CONCLUSION: VUV CD data include important information on protein structure which can be exploited with the algorithms and methodologies described.

Algorithms↗
PubMed2005

Secondary structure assignment that accurately reflects physical and evolutionary characteristics.

BACKGROUND: Secondary structure is used in hierarchical classification of protein structures, identification of protein features, such as helix caps and loops, for fold recognition, and as a precursor to ab initio structure prediction. There are several methods available for assigning secondary structure if the three-dimensional structure of the protein is known. Unfortunately they differ in their definitions, particularly in the exact positions of the termini. Additionally, most existing methods rely on hydrogen bonding, which means that important secondary structural classes, such as isolated beta-strands and poly-proline helices cannot be identified as they do not have characteristic hydrogen-bonding patterns. For this reason we have developed a more accurate method for assigning secondary structure based on main chain geometry, which also allows a more comprehensive assignment of secondary structure. RESULTS: We define secondary structure based on a number of geometric parameters. Helices are defined based on whether they fit inside an imaginary cylinder: residues must be within the correct radius of a central axis. Different types of helices (alpha, 3(10) or pi) are assigned on the basis of the angle between successive peptide bonds. beta-strands are assigned based on backbone dihedrals and with alternating peptide bonds. Thus hydrogen bonding is not required and beta-strands can be within a parallel sheet, antiparallel sheet, or can be isolated. Poly-proline helices are defined similarly, although with three-fold symmetry. CONCLUSION: We find that our method better assigns secondary structure than existing methods. Specifically, we find that comparing our methods with those of others, amino-acid trends at helix caps are stronger, secondary structural elements less likely to be concatenated together and secondary structure guided sequence alignment is improved. We conclude, therefore, that secondary structure assignments using our method better reflects physical and evolutionary characteristics of proteins. The program is available from http://www.bioinf.man.ac.uk/~lovell/segno.shtml.

Algorithms↗
PubMed1991

Tandemly repeating peptide motifs and their secondary structure in Ceratitis capitata eggshell proteins Ccs36 and Ccs38.

Evidence from amino acid composition, Fourier transform analysis of primary structure and secondary structure prediction suggests a tripartite structure for Ceratitis capitata eggshell proteins Ccs36 and Ccs38, which consists of a central domain and two flanking 'arms'. The proteins, apparently, contain tandemly repeating peptide motifs specific for each domain of the tripartite structure. The central domain of both proteins, which exhibits extensive sequence homology with the corresponding domains of Drosophila melanogaster proteins s36 and s38, is formed by tandem repeats of an octapeptide-X-X-X-Z-Z-Z-Z-Z- (where X = large hydrophobic residue and Z = beta-turn former residue) and its variants. It is predicted to adopt a compact, most probably twisted, antiparallel beta-pleated sheet structure of beta-sheet strands regularly alternating with beta-turns or loops. The central domains of Ccs36 and Ccs38 share structural similarities, but they are recognizably different. The 'arms' of the proteins presumably serving for protein and species-specific functions differ substantially from those of Drosophila melanogaster. In Ccs36, the C-terminal 'arm' is formed by, almost precise, tandem repeats of an octapeptide-Y-X-A-A-P-A-A-S- (X = G or S), whereas the N-terminal 'arm' contains repeats of the octapeptide -Z-Z-Z-A-X-A-A-Z- (X = Q, N or E and Z a beta-turn former). In both 'arms' alpha-helices are predicted, alternating with beta-turns.(ABSTRACT TRUNCATED AT 250 WORDS)

Amino Acid Sequence↗
PubMed1993

Statistics of RNA secondary structures.

A statistical reference for RNA secondary structures with minimum free energies is computed by folding large ensembles of random RNA sequences. Four nucleotide alphabets are used: two binary alphabets, AU and GC, the biophysical AUGC and the synthetic GCXK alphabet. RNA secondary structures are made of structural elements, such as stacks, loops, joints, and free ends. Statistical properties of these elements are computed for small RNA molecules of chain lengths up to 100. The results of RNA structure statistics depend strongly on the particular alphabet chosen. The statistical reference is compared with the data derived from natural RNA molecules with similar base frequencies. Secondary structures are represented as trees. Tree editing provides a quantitative measure for the distance dt, between two structures. We compute a structure density surface as the conditional probability of two structures having distance t given that their sequences have distance h. This surface indicates that the vast majority of possible minimum free energy secondary structures occur within a fairly small neighborhood of any typical (random) sequence. Correlation lengths for secondary structures in their tree representations are computed from probability densities. They are appropriate measures for the complexity of the sequence-structure relation. The correlation length also provides a quantitative estimate for the mean sensitivity of structures to point mutations.

Base Sequence↗