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PubMed1986

An algorithm for secondary structure determination in proteins based on sequence similarity.

A secondary structure prediction algorithm is proposed on the hypothesis that short homologous sequences of amino acids have the same secondary structure tendencies. Comparisons are made with the secondary structure assignments of Kabsch and Sander from X-ray data [(1983) Biopolymers 22, 2577-2637] and an empirically determined similarity matrix which assigns a sequence similarity score between any two sequences of 7 residues in length. This similarity matrix differs in many respects from that of the Dayhoff substitution matrix [(1978) in: Atlas of Protein Sequence and Structure, (Dayhoff, M.O. ed). vol. 5. suppl. 3, pp. 353-358, National Biochemical Research Foundation, Washington, DC]. This homologue method had a prediction accuracy of 62.2% over 3 states for 61 proteins and 63.6% for a new set of 7 proteins not in the original data base.

Amino Acid Sequence↗
PubMed1986

The predicted secondary structure of enolase.

The results of several secondary-structure prediction programs were combined to produce an estimate of the regions of alpha-helix, beta-sheet and reverse turn for both chicken skeletal-muscle and yeast enolase sequences. The predicted secondary-structure content of the chicken enzyme is 27% alpha-helix and less than 10% beta-sheet, whereas in the yeast enolase a similar helix content but virtually no sheet are predicted. These results are in fair agreement with published experimental estimates of the amount of secondary structure in the yeast enzyme. The enzyme appears to be formed from three domains.

Animals↗
PubMed2005

A method for aligning RNA secondary structures and its application to RNA motif detection.

BACKGROUND: Alignment of RNA secondary structures is important in studying functional RNA motifs. In recent years, much progress has been made in RNA motif finding and structure alignment. However, existing tools either require a large number of prealigned structures or suffer from high time complexities. This makes it difficult for the tools to process RNAs whose prealigned structures are unavailable or process very large RNA structure databases. RESULTS: We present here an efficient tool called RSmatch for aligning RNA secondary structures and for motif detection. Motivated by widely used algorithms for RNA folding, we decompose an RNA secondary structure into a set of atomic structure components that are further organized by a tree model to capture the structural particularities. RSmatch can find the optimal global or local alignment between two RNA secondary structures using two scoring matrices, one for single-stranded regions and the other for double-stranded regions. The time complexity of RSmatch is O(mn) where m is the size of the query structure and n that of the subject structure. When applied to searching a structure database, RSmatch can find similar RNA substructures, and is capable of conducting multiple structure alignment and iterative database search. Therefore it can be used to identify functional RNA motifs. The accuracy of RSmatch is tested by experiments using a number of known RNA structures, including simple stem-loops and complex structures containing junctions. CONCLUSION: With respect to computing efficiency and accuracy, RSmatch compares favorably with other tools for RNA structure alignment and motif detection. This tool shall be useful to researchers interested in comparing RNA structures obtained from wet lab experiments or RNA folding programs, particularly when the size of the structure dataset is large.

Algorithms↗
PubMed1992

First determination of the secondary structure of purified factor VIII light chain.

The first analysis of the secondary structure of human factor VIII light chain was performed by c.d. spectroscopy. The purification process described in this paper allowed us to obtain the large amounts of purified factor VIII light chains required for c.d. experiments. Since this 80 kDa protein is non-covalently associated with a heavy chain to form the active molecule, isolated factor VIII light chains were obtained after immunoadsorption and dissociation of the immobilized active complexes by EDTA. Furthermore, factor VIII light chains were discriminated from the residual active complexes and the free heavy chains by a final ion-exchange-chromatography step. This f.p.l.c. analysis showed that factor VIII light chains were less electronegative than the active complexes. The results of conformational analysis by c.d. show that the protein possesses a high degree of regular secondary structure (58%) with approx. 22% of alpha-helix and 36% of beta-strand structures. The protein was completely unfolded by 3 M-guanidine hydrochloride. The results obtained from the analysis of c.d. spectra were compared with those predicted from three different statistical methods based on amino-acid sequence. The secondary structure information obtained from these methods was in good agreement with the c.d. results. These results were comparable with the secondary structure prediction of ceruloplasmin, a protein known to show sequence identity to factor VIII.

Chromatography, High Pressure Liquid↗
PubMed2004

G-factor analysis of protein secondary structure in solutions and thin films.

The biological activity of proteins is structure dependent. In this discussion, we describe development of g-factor analysis for characterizing the secondary structure of proteins in solutions and films. In g-factor analysis, experimental circular dichroism (CD) and UV absorbance spectra are converted to dimensionless g-factor spectra by dividing the differential absorbance of circularly polarized light (AL - AR) by the UV absorbance (A) at each wavelength. The spectra can be deconvolved and the secondary structure estimated without information on the protein molecular weight, sample concentration, or sample path length. We have refined g-factor spectral acquisition and deconvolution parameters to improve the precision and accuracy of experimental g-factor spectra and the subsequent secondary structure estimations. In general, slower scan rates and longer response times are required to obtain high quality g-factor spectra than to obtain ordinary CD spectra, particularly for data at wavelengths longer than 230 nm. The spectral acquisition and deconvolution procedures have been validated for aqueous bovine serum albumin (BSA) and poly(L-proline). The secondary structures of fibronectin and laminin in buffer solutions are predicted. When BSA and poly(L-proline) form films on quartz, their secondary structures change significantly: 13% for BSA and 32% for poly(L-proline). By contrast, the secondary structure of fibronectin is the same in solution and films. The g-factor method is an easy, rapid, accurate and precise method for determining secondary structure and structural changes in protein solutions and films. Potential applications range from proteomics and structure-based drug discovery, to the design and fabrication of biosensors, biomaterials and biofluidic devices.

Algorithms↗
PubMed2004

Investigation of the Bernoulli model for RNA secondary structures.

Within this paper we investigate the Bernoulli model for random secondary structures of ribonucleic acid (RNA) molecules. Assuming that two random bases can form a hydrogen bond with probability p we prove asymptotic equivalents for the averaged number of hairpins and bulges, the averaged loop length, the expected order, the expected number of secondary structures of size n and order k and further parameters all depending on p. In this way we get an insight into the change of shape of a random structure during the process 1 p -->0. Afterwards we compare the computed parameters for random structures in the Bernoulli model to the corresponding quantities for real existing secondary structures of large subunit rRNA molecules found in the database of Wuyts et al. That is how it becomes possible to identify those parameters which behave (almost) randomly and those which do not and thus should be considered as interesting, e.g., with respect to the biological functions or the algorithmic prediction of RNA secondary structures.

Hydrogen Bonding↗
PubMed2005

Statistical evidence for conserved, local secondary structure in the coding regions of eukaryotic mRNAs and pre-mRNAs.

Owing to the degeneracy of the genetic code, protein-coding regions of mRNA sequences can harbour more than only amino acid information. We search the mRNA sequences of 11 human protein-coding genes for evolutionarily conserved secondary structure elements using RNA-Decoder, a comparative secondary structure prediction program that is capable of explicitly taking the known protein-coding context of the mRNA sequences into account. We detect well-defined, conserved RNA secondary structure elements in the coding regions of the mRNA sequences and show that base-paired codons strongly correlate with sparse codons. We also investigate the role of repetitive elements in the formation of secondary structure and explain the use of alternate start codons in the caveolin-1 gene by a conserved secondary structure element overlapping the nominal start codon. We discuss the functional roles of our novel findings in regulating the gene expression on mRNA level. We also investigate the role of secondary structure on the correct splicing of the human CFTR gene. We study the wild-type version of the pre-mRNA as well as 29 variants with synonymous mutations in exon 12. By comparing our predicted secondary structures to the experimentally determined splicing efficiencies, we find with weak statistical significance that pre-mRNAs with high-splicing efficiencies have different predicted secondary structures than pre-mRNAs with low-splicing efficiencies.

Animals↗
PubMed2004

Beyond average protein secondary structure content prediction using FTIR spectroscopy.

This paper demonstrates that secondary structure information beyond purely protein secondary structure content can be predicted from FTIR (Fourier transform infrared spectroscopy) spectra of proteins with a high degree of accuracy. Both neural networks and adaptive neuro-fuzzy inference systems (ANFISs) were employed to predict helix/sheet segment information. The best results were achieved using ANFISs with fuzzy subtractive clustering based on normalised, compressed amide I data with an average SEP (standard error of prediction, root mean of squared errors) of 1.51. Predictions for average helix/sheet length based merely on the amide I band maximum position in combination with the full-width at half-height resulted in a comparable average SEP of 1.62. This suggests the importance of information on the position and width of the amide I band maximum for the prediction of helix/sheet segment information. Finally, the most promising pattern recognition approaches found in this study were applied to a protein with an as yet unknown x-ray structure: native a1-antichymotrypsin (a1-ACT).

Algorithms↗
PubMed1981

Secondary structure of eukaryotic cytoplasmic 5S ribosomal RNA.

A five-helix secondary structural model is proposed for eukaryotic cytoplasmic 5S rRNA. All available sequence data are consistent with this model including those from Chlorella 5S rRNA whose sequence is revised by data included here. Various architectural features of eukaryotic 5S rRNA are summarized in terms of this secondary structural model. It is observed that previous failures to identify universal models for 5S rRNA secondary structure stem from significant differences in architecture between eukaryotic cytoplasmic and eubacterial 5S rRNAs. The usual four-helix model for eubacterial 5S rRNA secondary structure nevertheless does share several structural features with the five-helix model presented here for cytoplasmic 5S rRNA. It is thus likely that these two classes of 5S rRNA are the result of evolutionary divergence rather than convergence.

Animals↗
PubMed1994

Nativelike secondary structure in interleukin-1 beta inclusion bodies by attenuated total reflectance FTIR.

Attenuated total reflectance FTIR has been used to study the structure of human interleukin-1 beta in inclusion bodies (IBs) and other aggregated forms. The secondary structure composition of native wild-type IL-1 beta determined by FTIR is in excellent agreement with that previously determined by crystallography and NMR: 52% beta-sheet, 25% loop/irregular structure, and 23% turn. Remarkably, IL-1 beta inclusion bodies exhibit secondary structural composition very similar to that of the native protein. The results indicate that the IBs form from a folding intermediate that has nativelike secondary structure. The secondary structure content of aggregated IL-1 beta, formed either in refolding or by thermal denaturation, was identical within experimental error to that of the IB, indicating that these aggregates were formed from intermediates with structures similar to that of the inclusion body.

Aged↗
PubMed2003

DSSPcont: Continuous secondary structure assignments for proteins.

The DSSP program automatically assigns the secondary structure for each residue from the three-dimensional co-ordinates of a protein structure to one of eight states. However, discrete assignments are incomplete in that they cannot capture the continuum of thermal fluctuations. Therefore, DSSPcont (http://cubic.bioc.columbia.edu/services/DSSPcont) introduces a continuous assignment of secondary structure that replaces 'static' by 'dynamic' states. Technically, the continuum results from calculating weighted averages over 10 discrete DSSP assignments with different hydrogen bond thresholds. A DSSPcont assignment for a particular residue is a percentage likelihood of eight secondary structure states, derived from a weighted average of the ten DSSP assignments. The continuous assignments have two important features: (i) they reflect the structural variations due to thermal fluctuations as detected by NMR spectroscopy; and (ii) they reproduce the structural variation between many NMR models from one single model. Therefore, functionally important variation can be extracted from a single X-ray structure using the continuous assignment procedure.

Algorithms↗
PubMed1988

Secondary structure of interleukin-2(Ala125) in unfolded state.

Secondary structure of interleukin-2(Ala125) in unfolded state was examined by circular dichroism (CD). Unfolding of tertiary structure of the protein, as determined by CD, was observed when the solvent pH was decreased below 3.0 or the disulfide bond was reduced. Consistent with the CD results, a stronger fluorescence enhancement of 1-anilinonaphthalene-8-sulfonic acid was observed on acidification or reduction of interleukin-2(Ala125) relative to that of the native protein, indicating a larger hydrophobic surface exposed to solvent. However, the secondary structure was fully retained in 5% acetic acid or aqueous HCl, pH 3.0. It seemed that alpha-helical content of the protein is even greater at pH 2.0. Reduced protein showed a far u.v. CD spectrum indistinguishable from the oxidized one at pH 4.0. These results suggest that the secondary structure of interleukin-2(Ala125) does not require tertiary structure.

Circular Dichroism↗
PubMed1988

Evolution of the secondary structures and compensatory mutations of the ribosomal RNAs of Drosophila melanogaster.

This paper examines the effects of DNA sequence evolution on RNA secondary structures and compensatory mutations. Models of the secondary structures of Drosophila melanogaster 18S ribosomal RNA (rRNA) and of the complex between 2S, 5.8S, and 28S rRNAs have been drawn on the basis of comparative and energetic criteria. The overall AU richness of the D. melanogaster rRNAs allows the resolution of some ambiguities in the structures of both large rRNAs. Comparison of the sequence of expansion segment V2 in D. melanogaster 18S rRNA with the same region in three other Drosophila species and the tsetse fly (Glossina morsitans morsitans) allows us to distinguish between two models for the secondary structure of this region. The secondary structures of the expansion segments of D. melanogaster 28S rRNA conform to a general pattern for all eukaryotes, despite having highly divergent sequences between D. melanogaster and vertebrates. The 70 novel compensatory mutations identified in the 28S rRNA show a strong (70%) bias toward A-U base pairs, suggesting that a process of biased mutation and/or biased fixation of A and T point mutations or AT-rich slippage-generated motifs has occurred during the evolution of D. melanogaster rDNA. This process has not occurred throughout the D. melanogaster genome. The processes by which compensatory pairs of mutations are generated and spread are discussed, and a model is suggested by which a second mutation is more likely to occur in a unit with a first mutation as such a unit begins to spread through the family and concomitantly through the population. Alternatively, mechanisms of proofreading in stem-loop structures at the DNA level, or between RNA and DNA, might be involved. The apparent tolerance of noncompensatory mutations in some stems which are otherwise strongly supported by comparative criteria within D. melanogaster 28S rRNA must be borne in mind when compensatory mutations are used as a criterion in secondary-structure modeling. Noncompensatory mutation may extend to the production of unstable structures where a stem is stabilized by RNA-protein or additional RNA-RNA interactions in the mature ribosome. Of motifs suggested to be involved in rRNA processing, one (CGAAAG) is strongly overrepresented in the 28S rRNA sequence. The data are discussed both in the context of the forces involved with the evolution of multigene families and in the context of molecular coevolution in the rDNA family in particular.

Animals↗
PubMed2005

The effect of long-range interactions on the secondary structure formation of proteins.

The influence of long-range residue interactions on defining secondary structure in a protein has long been discussed and is often cited as the current limitation to accurate secondary structure prediction. There are several experimental examples where a local sequence alone is not sufficient to determine its secondary structure, but a comprehensive survey on a large data set has not yet been done. Interestingly, some earlier studies denied the negative effect of long-range interactions on secondary structure prediction accuracy. Here, we have introduced the residue contact order (RCO), which directly indicates the separation of contacting residues in terms of the position in the sequence, and examined the relationship between the RCO and the prediction accuracy. A large data set of 2777 nonhomologous proteins was used in our analysis. Unlike previous studies, we do find that prediction accuracy drops as residues have contacts with more distant residues. Moreover, this negative correlation between the RCO and the prediction accuracy was found not only for beta-strands, but also for alpha-helices. The prediction accuracy of beta-strands is lower if residues have a high RCO or a low RCO, which corresponds to the situation that a beta-sheet is formed by beta-strands from different chains in a protein complex. The reason why the current study draws the opposite conclusion from the previous studies is examined. The implication for protein folding is also discussed.

Amino Acids↗
PubMed2002

Prediction of protein secondary structures from conformational biases.

We use LINUS (the "Local Independently Nucleated Units of Structure"), a procedure developed by Srinivasan and Rose, to provide a physical interpretation of and predict the secondary structures of proteins. The secondary structure type at a given site is identified by the largest conformational bias during short simulations. We examine the rate of successful prediction as a function of temperature and the interaction window. At high temperatures, there is a large propensity for the establishment of beta-strands whereas alpha-helices appear only when the temperature is lower than a certain threshold value. It is found that there exists an optimal temperature at which the correct secondary structures are predicted most accurately. We find that this temperature is close to the peak temperature of the specific heat. Changing the interaction window or carrying out longer simulations approaching equilibrium lead to little change in the optimal success rate. Our findings are in accord with the observation by Srinivasan and Rose that the secondary structures are mainly determined by local interactions and appear in the early stage of folding.

Computer Simulation↗
PubMed1985

A kinetic approach to the prediction of RNA secondary structures.

A new approach to the prediction of secondary RNA structures based on the analysis of the kinetics of molecular self-organisation is proposed herein. The Markov process is used to describe structural reconstructions during secondary structure formation. This process is modelled by a Monte-Carlo method. Examples of the calculation by this method of the secondary structures kinetic ensemble are given. Distribution of time-dependent probabilities within the ensembles is obtained. An effective method for search for the equilibrium ensemble is also suggested. This method is based on the construction of a tree of all possible secondary structures of RNA. By ascribing a probability for each structure (according to its free energy) the Boltzmann equilibrium ensemble can be obtained.

Computers↗
PubMed2004

Evolutionary rate variation and RNA secondary structure prediction.

Predicting RNA secondary structure using evolutionary history can be carried out by using an alignment of related RNA sequences with conserved structure. Accurately determining evolutionary substitution rates for base pairs and single stranded nucleotides is a concern for methods based on this type of approach. Determining these rates can be hard to do reliably without a large and accurate initial alignment, which ideally also has structural annotation. Hence, one must often apply rates extracted from other RNA families with trusted alignments and structures. Here, we investigate this problem by applying rates derived from tRNA and rRNA to the prediction of the much more rapidly evolving 5'-region of HIV-1. We find that the HIV-1 prediction is in agreement with experimental data, even though the relative evolutionary rate between A and G is significantly increased, both in stem and loop regions. In addition we obtained an alignment of the 5' HIV-1 region that is more consistent with the structure than that currently in the database. We added randomized noise to the original values of the rates to investigate the stability of predictions to rate matrix deviations. We find that changes within a fairly large range still produce reliable predictions and conclude that using rates from a limited set of RNA sequences is valid over a broader range of sequences.

Algorithms↗
PubMed1998

Structural dynamics of the Streptomyces lividans K+ channel (SKC1): secondary structure characterization from FTIR spectroscopy.

Fourier transform infrared (FTIR) spectroscopy was used to probe the secondary structure, orientation, and the kinetics of amide hydrogen-deuterium exchange (HX) of the small K+ channel from Streptomyces lividans. Frequency component analysis of the amide I band showed that SKC1 is composed of 44-46% alpha-helix, 21-24% beta-sheet, 10-12% turns and 18-20% unordered structures. The order parameter S of the helical component of SKC1 was between 0.60 and 0.69. Close to 80% of SKC1 amide protons exchange within approximately 3 h of D2O exposure, suggesting that the channel is largely accessible to solvent exchange. These results are consistent with a model of SKC1 in which helices slightly tilted from the membrane normal line the water-filled vestibules that flank the K+ selectivity filter.

Amino Acid Sequence↗