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At least 523 records · Page 29Linked to original sources
PubMed2005

Rapid assessment of contact-dependent secondary structure propensity: relevance to amyloidogenic sequences.

We have previously demonstrated that calculation of contact-dependent secondary structure propensity (CSSP) is highly sensitive in detecting non-native beta-strand propensities in the core sequences of known amyloidogenic proteins. Here we describe a CSSP method based on an artificial neural network that rapidly and accurately quantifies the influence of tertiary contacts (TCs) on secondary structure propensity in local regions of protein sequences. The present method exhibited 72% accuracy in predicting the alternate secondary structure adopted by chameleon sequences located in highly disparate TC regions. Analysis of 1930 nonhomologous protein domains reveals that the alpha-helix and the beta-strand largely share the same sequence context, and that tertiary context is a major determinant of the native conformation. Conversely, it appears that the propensity of random coils for either the alpha-helix or the beta-strand is largely invariant to tertiary effects. The present CSSP method successfully reproduced the amyloidogenic character observed in local regions of the human islet amyloid polypeptide (hIAPP). Furthermore, CSSP profiles were strongly correlated (r = 0.76) with the observed mutational effects on the aggregation rate of acylphosphatase. Taken together, these results provide compelling evidence in support of the present CSSP approach as a sensitive probe useful for analysis of full-length proteins and for detection of core sequences that may trigger amyloid fibril formation. The combined speed and simplicity of the CSSP method lends itself to proteome-wide analysis of the amyloidogenic nature of common proteins.

Algorithms↗
PubMed1997

Predicting the conformational class of short and medium size loops connecting regular secondary structures: application to comparative modelling.

Loops are regions of non-repetitive conformation connecting regular secondary structures. They are both the most difficult and error prone regions of a protein to solve by X-ray crystallography and the hardest regions to model using comparative procedures. Although a loop can sometimes be modelled from a homologue, very often it must be selected from outside the family. The loop prediction procedure, SLoop, attempts to identify the conformational class of the loop rather than to select a specific loop from a set of fragments extracted from known structures or generated ab initio. Templates are constructed for each of the 161 loop conformational classes that have been identified from the clustering of the structures of some 2024 loops of one to eight residues in length. A class template describes both sequence preferences and relative disposition of bounding secondary structures. During comparative modelling, the conformation of a loop can be predicted by identifying a loop class with which its sequence and disposition of bounding secondary structures are compatible. The procedure is tested on an unrelated non-redundant set of 1785 loops under stringent and lax evaluation schemes. Optimal sequence score cut-offs are identified such that the prediction rate is equal to the percentage of loops assigned to acceptable classes. Under the stringent evaluation, at the optimal sequence score cut-off, a conformation is predicted for 50% of loops of which 47% are correct, while under the lax evaluation a conformation is predicted for 63% of loops of which 54% are correct. Sequence score is shown to be a good indicator of the probability of a prediction being correct. Loop length also has a strong affect on prediction outcomes. Considering only loops of two to five residues in length, under the stringent evaluation 62% of loops are predicted with 52% of these predictions being correct while under the lax evaluation predictions are provided for 75% of loops of which 57% are correct.

Computer Simulation↗
PubMed1982

Pattern recognition in nucleic acid sequences. II. An efficient method for finding locally stable secondary structures.

We present a method for calculating all possible single hairpin loop secondary structures in a nucleic acid sequence by the order of N2 operations where N is the total number of bases. Each structure may contain any number of bulges and internal loops. Most natural sequences are found to be indistinguishable from random sequences in the potential of forming secondary structures, which is defined by the frequency of possible secondary structures calculated by the method. There is a strong correlation between the higher G+C content and the higher structure forming potential. Interestingly, the removal of intervening sequences in mRNAs is almost always accompanied by an increase in the G+C content, which may suggest an involvement of structural stabilization in the mRNA maturation.

Base Composition↗
PubMed2006

Chemometric tools for classification and elucidation of protein secondary structure from infrared and circular dichroism spectroscopic measurements.

Protein classification and characterization often rely on the information contained in the protein secondary structure. Protein class assignment is usually based on X-ray diffraction measurements, which need the protein in a crystallized form, or on NMR spectra, to obtain the structure of a protein in solution. Simple spectroscopic techniques, such as circular dichroism (CD) and infrared (IR) spectroscopies, are also known to be related to protein secondary structure, but they have seldom been used for protein classification. To see the potential of CD, IR, and combined CD/IR measurements for protein classification, unsupervised pattern recognition methods, Principal Component Analysis (PCA) and cluster analysis, are proposed first to check for natural grouping tendencies of proteins according to their measured spectra. Partial Least Squares Discriminant Analysis (PLS-DA), a supervised pattern recognition method, is used afterwards to test the possibility to model explicitly each protein class and to test these models in class assignment of unknown proteins. Determination of the protein secondary structure, understood as the prediction of the abundance of the different secondary structure motifs in the biomolecule, was carried out with the local regression method interval Partial Least Squares (iPLS). CD, IR, and CD/IR measurements were correlated to the fraction of the motif to be predicted, determined from X-ray measurements. iPLS builds models extracting the spectral information most correlated to a specific secondary motif and avoids the use of irrelevant spectral regions. Spectral intervals chosen by iPLS models provide structural information which can be used to confirm previous biochemical assignments or identify new motif-related spectral features. The predictive ability of the models built with the selected spectral regions has a quality similar to previous classical approaches.

Circular Dichroism↗
PubMed2006

Mechanism of trinucleotide repeats instabilities: the necessities of repeat non-B secondary structure formation and the roles of cellular trans-acting factors.

The mechanism underlying CAG.CTG CGG.CCG and GAA.TTC trinucleotide repeats expansion and contraction instabilities has not been clearly understood. Investigations in vitro have demonstrated that the disease causing repeats are capable of adopting non-B secondary structures that mediate repeats expansion. However, in vivo, similar observations have not been easily made so far. Investigations on the non-B secondary structure formation using E.coli, yeast etc cannot simulate the suggested repeats expansion instability. These could leave a space to infer a disassociation of the suggested repeats non-B secondary structure formation and the repeats expansion in vivo. Although longer trinucleotide repeats may be theoretically easier to form non-B DNA secondary structures in replication or in post-replication, however such non-B secondary structures are likely to cause repeat fragility rather than repeat expansion. In fact, repeat expansion as seen in patients may not necessarily require trinucleotide repeats to form non-B secondary structures, instead the repeat expansions can be produced through a RNA transcription-stimulated local repeat DNA replication and a subsequent DNA rearrangement.

Fragile X Syndrome↗
PubMed1993

The limits of protein secondary structure prediction accuracy from multiple sequence alignment.

The expected best residue-by-residue accuracies for secondary structure prediction from multiple protein sequence alignment have been determined by an analysis of known protein structural families. The results show substantial variation is possible among homologous protein structures, and that 100% agreement is unlikely between a consensus prediction and one member of a protein structural family. The study provides the range of agreement to be expected between a perfect secondary structure prediction from a multiple alignment and each protein within the alignment. The results of this study overcome the difficulties inherent in the use of residue-by-residue accuracy for assessing the quality of consensus secondary structure predictions. The accuracies of recent consensus predictions for the annexins, SH2 domains and SH3 domains fall within the expected range for a perfect prediction.

Amino Acid Sequence↗
PubMed2003

An empirical correlation between secondary structure content and averaged chemical shifts in proteins.

It is shown that the averaged chemical shift (ACS) of a particular nucleus in the protein backbone empirically correlates well to its secondary structure content (SSC). Chemical shift values of more than 200 proteins obtained from the Biological Magnetic Resonance Bank are used to calculate ACS values, and the SSC is estimated from the corresponding three-dimensional coordinates obtained from the Protein Data Bank. ACS values of (1)H(alpha) show the highest correlation to helical and sheet structure content (correlation coefficient of 0.80 and 0.75, respectively); (1)H(N) exhibits less reliability (0.65 for both sheet and helix), whereas such correlations are poor for the heteronuclei. SSC estimated using this correlation shows a good agreement with the conventional chemical shift index-based approach for a set of proteins that only have chemical shift information but no NMR or x-ray determined three-dimensional structure. These results suggest that even chemical shifts averaged over the entire protein retain significant information about the secondary structure. Thus, the correlation between ACS and SSC can be used to estimate secondary structure content and to monitor large-scale secondary structural changes in protein, as in folding studies.

Amino Acid Sequence↗
PubMed1977

Secondary-structure predictions of calcium-binding proteins.

The known tertiary structure of carp muscle parvalbumin is consistent with an "EF-hand" architecture (helix-loop-helix) for each calcium-ion binding site. Primary-sequence alignments have indicated four EF hands in rabbit skeletal muscle troponin C and in rabbit myosin alkali light chains. Five secondary-structure prediction methods, based on amino acid sequence only, have been fully computerized and used to calculate joint prediction histograms for several calcium-binding proteins. The joint histogram can suggest directly the extent and sequence of the helical- and loop-structural elements, as well as any secondary structural distortions or evolutionary developments. Since the histogram predicted well the length and sequence of secondary structural elements in carp muscle parvalbumin, it seemed reasonable to calculate the joint distribution for other proteins that might bind calcium through the EF-hand configuration. The histograms indicated the four EF-hand regions speculated fro rabbit skeletal muscle troponin C but suggested only three such hands in bovine cardiac muscle troponin C and with a distorted fourth hand. Considerable secondary structural distortion is postulated for the alkali light chains. Possible EF configurations consistent with the histogram results are speculated for Escherichia coli acyl-carrier protein and bovine prothrombin fragment 1, which have been shown to bind calcium. The secondary-structure-prediction algorithms appear to be a useful adjunct to sequence-alignment techniques, especially in cases where the primary sequence homology is weak or the evolutionary distance is large.

Amino Acid Sequence↗
PubMed1999

Evaluation and improvement of multiple sequence methods for protein secondary structure prediction.

A new dataset of 396 protein domains is developed and used to evaluate the performance of the protein secondary structure prediction algorithms DSC, PHD, NNSSP, and PREDATOR. The maximum theoretical Q3 accuracy for combination of these methods is shown to be 78%. A simple consensus prediction on the 396 domains, with automatically generated multiple sequence alignments gives an average Q3 prediction accuracy of 72.9%. This is a 1% improvement over PHD, which was the best single method evaluated. Segment Overlap Accuracy (SOV) is 75.4% for the consensus method on the 396-protein set. The secondary structure definition method DSSP defines 8 states, but these are reduced by most authors to 3 for prediction. Application of the different published 8- to 3-state reduction methods shows variation of over 3% on apparent prediction accuracy. This suggests that care should be taken to compare methods by the same reduction method. Two new sequence datasets (CB513 and CB251) are derived which are suitable for cross-validation of secondary structure prediction methods without artifacts due to internal homology. A fully automatic World Wide Web service that predicts protein secondary structure by a combination of methods is available via http://barton.ebi.ac.uk/.

Algorithms↗
PubMed1991

Protein secondary structure from Fourier transform infrared spectroscopy: a data base analysis.

An infrared (ir) method to determine the secondary structure of proteins in solution using the amide I region of the spectrum has been devised. The method is based on the circular dichroism (CD) matrix method for secondary structure analysis given by Compton and Johnson (L. A. Compton and W. C. Johnson, 1986, Anal. Biochem. 155, 155-167). The infrared data matrix was constructed from the normalized Fourier transform infrared spectra from 1700 to 1600 cm-1 of 17 commercially available proteins. The secondary structure matrix was constructed from the X-ray data of the seventeen proteins with secondary structure elements of helix, beta-sheet, beta-turn, and other (random). The CD and ir methods were compared by analyzing the proteins of the CD and ir databases as unknowns. Both methods produce similar results compared to structures obtained by X-ray crystallographic means with the CD slightly better for helix conformation, and the ir slightly better for beta-sheet. The relatively good ir analysis for concanavalin A and alpha-chymotrypsin indicate that the ir method is less affected by the presence of aromatic groups. The concentration of the protein and the cell path length need not be known for the ir analysis since the spectra can be normalized to the total ir intensity in the amide I region. The ir spectra for helix, beta-sheet, beta-turn, and other, as extracted from the data-base, agree with the literature band assignments. The ir data matrix and the inverse matrix necessary to analyze unknown proteins are presented.

Circular Dichroism↗
PubMed1998

Specific correlations between relative synonymous codon usage and protein secondary structure.

We found significant species-specific correlations between the use of two synonymous codons and protein secondary structure units by comparing the three-dimensional structures of human and Escherichia coli proteins with their mRNA sequences. The correlations are not explained by codon-context, expression level, GC/AU content, or positional effects. The E. coli correlation is between Asn AAC and the C-terminal regions of beta-sheet segments; it may result from selection for translational accuracy, suggesting the hypothesis that downstream Asn residues are important for beta-sheet formation. The correlation in human proteins is between Asp GAU and the N termini of alpha-helices; it may be important for eukaryote-specific sequential, cotranslational folding. The kingdom-specific correlations may reflect kingdom-specific differences in translational mechanisms. The correlations may help identify residues that are important for secondary structure formation, be useful in secondary structure prediction algorithms, and have implications for recombinant gene expression.

Bacterial Proteins↗
PubMed1990

Prediction of RNA secondary structure, including pseudoknotting, by computer simulation.

A computer program is presented which determines the secondary structure of linear RNA molecules by simulating a hypothetical process of folding. This process implies the concept of 'nucleation centres', regions in RNA which locally trigger the folding. During the simulation, the RNA is allowed to fold into pseudoknotted structures, unlike all other programs predicting RNA secondary structure. The simulation uses published, experimentally determined free energy values for nearest neighbour base pair stackings and loop regions, except for new extrapolated values for loops larger than seven nucleotides. The free energy value for a loop arising from pseudoknot formation is set to a single, estimated value of 4.2 kcal/mole. Especially in the case of long RNA sequences, our program appears superior to other secondary structure predicting programs described so far, as tests on tRNAs, the LSU intron of Tetrahymena thermophila and a number of plant viral RNAs show. In addition, pseudoknotted structures are often predicted successfully. The program is written in mainframe APL and is adapted to run on IBM compatible PCs, Atari ST and Macintosh personal computers. On an 8 MHz 8088 standard PC without coprocessor, using STSC APL, it folds a sequence of 700 nucleotides in one and a half hour.

Algorithms↗
PubMed1980

Relationships between alpha and beta secondary structures and amino-acid pseudosymmetrical arrangements.

A total of 51 polypeptides of known amino acid sequence and secondary structure have been screened for the presence of symmetrical arrangements of amino acids. Similarity between amino acids was derived by using a genetic test (minimum mutation distance) or a structural test (relative frequencies of amino acids substitutions in families of related proteins). It is shown that the frequency of proteins displaying symmetrical arrangements of amino acids is slightly higher than predicted by chance. In contrast, when the analysis is restricted to protein subregions displaying identical types of secondary structure, the frequency of proteins in which the alpha and beta subregions exhibit symmetrical arrangements of amino acids is significantly higher than predicted by chance. On the other hand, it is observed that more discriminatory results are always obtained when the structural test is used as a criterion for amino acid similarity. These data suggest that symmetrical arrangements of amino acids could result from structural constraints imposed either by the alpha or beta secondary structures. It is postulated that the regular alternation in hydrophobicity which is generally observed in the amino acid sub-sequences displaying alpha or beta secondary structures may be responsible for the occurrence of symmetrical arrangements of amino acids.

Amino Acid Sequence↗
PubMed1977

Secondary structures of proteins from the 30S subunit of the Escherichia coli ribosome.

The secondary structures of the proteins S4, S6, S8, S9, S12, S13, S15, S16, S18, S20 and S21 from the subunit of the E. coli ribosome were predicted according to four different methods. From the resultant diagrams indicating regions of helix, turn, extended structure and random coil, average values for the respective secondary structures could be calculated for each protein. Using the known relative distances for residues in the helical, turn and sheet or allowed random conformations, estimates are made of the maximum possible lengths of the proteins in order to correlate these with results obtained from antibody binding studies to the 30S subunit as determined by electron microscopy. The influence of amino acid changes on the predicted secondary structures of proteins from a few selected mutants was studied. The altered residues tend to be structurally conservative or to induce only minimal local changes.

Alleles↗
PubMed2004

A new algorithm for RNA secondary structure design.

The function of many RNAs depends crucially on their structure. Therefore, the design of RNA molecules with specific structural properties has many potential applications, e.g. in the context of investigating the function of biological RNAs, of creating new ribozymes, or of designing artificial RNA nanostructures. Here, we present a new algorithm for solving the following RNA secondary structure design problem: given a secondary structure, find an RNA sequence (if any) that is predicted to fold to that structure. Unlike the (pseudoknot-free) secondary structure prediction problem, this problem appears to be hard computationally. Our new algorithm, "RNA Secondary Structure Designer (RNA-SSD)", is based on stochastic local search, a prominent general approach for solving hard combinatorial problems. A thorough empirical evaluation on computationally predicted structures of biological sequences and artificially generated RNA structures as well as on empirically modelled structures from the biological literature shows that RNA-SSD substantially out-performs the best known algorithm for this problem, RNAinverse from the Vienna RNA Package. In particular, the new algorithm is able to solve structures, consistently, for which RNAinverse is unable to find solutions. The RNA-SSD software is publically available under the name of RNA Designer at the RNASoft website (www.rnasoft.ca).

Algorithms↗
PubMed1996

Alignment of possible secondary structures in multiple RNA sequences using simulated annealing.

Multiple sequence alignment has been a useful technique for identifying RNA secondary structures. In this paper, an algorithm for aligning multiple RNA sequences to identify possible secondary structure is presented. In this algorithm, dot matrices generated from intra-sequence comparisons are used to obtain possible common secondary structures. A hit probability for dot matrices is calculated and a score function based on this hit probability is defined. Simulated annealing is applied to optimize the score function. The solution set of multiple sequence alignment is introduced, and the effects on the solution set of increasing the number of alignment gaps and the alignment length are analyzed. Several additional strategies to reduce simulated annealing time are applied. A method is applied to reduce the computation time based on the solution set. Also, an optimized transition rule, double shuffle, which moves two positions in a sequence with each iteration, is applied to increase the rate of convergence. This algorithm was used to find possible common secondary structures in RNA sequences.

Algorithms↗
PubMed1997

Secondary structure induction in aqueous vs membrane-like environments.

The conformational propensity of the 20 naturally occurring amino acids was determined in aqueous 3-[N-morpholino]propane-sulfonic acid (MOPS) buffer, protein interior-like [nonmicellar sodium dodecylsulfate (SDS)] and membrane-like environments (micellar SDS and lysophosphatidylglycerol/lysophosphatidylcholine micelles) using a single "guest" position in a polyalanine-based model host peptide (Ac-KYA13K-NH2). This model system allows the intrinsic alpha-helical or beta-sheet propensity of the amino acids to be determined without intra- and interchain side chain interactions. The overall environment dependence observed for the conformational propensity for the amino acids studied confirms the importance of determining propensity in lipidic environments to better elucidate the biological functions of proteins. The hydrophobic interactions between peptide side chains and lipids appeared to be the primary forces driving the conformational induction in lipidic environments of the model peptides studied. Finally, when comparing the results of these studies with those reported in the literature, the local environment was found to highly influence 65% of the 20 naturally occurring amino acids.

Amino Acids↗