PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Protein Structure, Secondary”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

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

At least 217 records · Page 12Linked to original sources
PubMed2002

Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles.

Secondary structure predictions are increasingly becoming the workhorse for several methods aiming at predicting protein structure and function. Here we use ensembles of bidirectional recurrent neural network architectures, PSI-BLAST-derived profiles, and a large nonredundant training set to derive two new predictors: (a) the second version of the SSpro program for secondary structure classification into three categories and (b) the first version of the SSpro8 program for secondary structure classification into the eight classes produced by the DSSP program. We describe the results of three different test sets on which SSpro achieved a sustained performance of about 78% correct prediction. We report confusion matrices, compare PSI-BLAST to BLAST-derived profiles, and assess the corresponding performance improvements. SSpro and SSpro8 are implemented as web servers, available together with other structural feature predictors at: http://promoter.ics.uci.edu/BRNN-PRED/.

Algorithms↗
PubMed2006

Binding of glucose to the D-galactose/D-glucose-binding protein from Escherichia coli restores the native protein secondary structure and thermostability that are lost upon calcium depletion.

The effect of the depletion of calcium on the structure and thermal stability of the D-galactose/D-glucose-binding protein (GGBP) from Escherichia coli was studied by fluorescence spectroscopy and Fourier-transform infrared spectroscopy. The calcium-depleted protein (GGBP-Ca) was also studied in the presence of glucose (GGBP-Ca/Glc). The results show that calcium depletion has a small effect on the secondary structure of GGBP, and, in particular it affects a population of alpha-helices with a low exposure to solvent. Alternatively, glucose-binding to GGBP-Ca eliminates the effect induced by calcium depletion by restoring a secondary structure similar to that of the native protein. In addition, the infrared and fluorescence data obtained reveal that calcium depletion markedly reduces the thermal stability of GGBP. In particular, the spectroscopic experiments show that the depletion of calcium mainly affects the stability of the C-terminal domain of the protein. However, the binding of glucose restores the thermal stability of GGBP-Ca. The thermostability of GGBP and GGBP-Ca was also studied by molecular dynamics simulations. The simulation data support the spectroscopic results. New insights into the role of calcium in the thermal stability of GGBP contribute to a better understanding of the protein function and constitute important information for the development of biotechnological applications of this protein. Mutations and/or labelling of amino acid residues located in the protein C-terminal domain may affect the stability of the whole protein structure.

Binding Sites↗
PubMed2006

Measures for the assessment of fuzzy predictions of protein secondary structure.

Many of the recent secondary structure prediction methods incorporate the idea of fuzzy set theory, where instead of assigning a definite secondary structure to a query residue, probability for the residue being in each of the conformational states is estimated. Moreover, continuous assignment of conformational states to the experimentally observed protein structures can be performed in order to reflect inherent flexibility. Although various measures have been developed for evaluating performances of secondary structure prediction methods, they depend only on the most probable secondary structures. They do not assess the accuracy of the probabilities produced by fuzzy prediction methods, and they cannot incorporate information contained in continuous assignments of conformational states to observed structures. Three important measures for evaluating performance of a secondary structure prediction algorithm, Q score, Segment OVerlap (SOV) measure, and the k-state correlation coefficient (Corr), are deformed into fuzzy measures F score, Fuzzy OVerlap (FOV) measure, and the fuzzy correlation coefficient (Forr), so that the new measures not only assess probabilistic outputs of fuzzy prediction methods, but also incorporate information from continuous assignments of secondary structure. As an example of application, prediction results of four fuzzy secondary structure prediction methods, PSIPRED, PROFking, SABLE, and PREDICT, are assessed using the new fuzzy measures.

Computer Simulation↗
PubMed1993

Prediction of protein secondary structure at better than 70% accuracy.

We have trained a two-layered feed-forward neural network on a non-redundant data base of 130 protein chains to predict the secondary structure of water-soluble proteins. A new key aspect is the use of evolutionary information in the form of multiple sequence alignments that are used as input in place of single sequences. The inclusion of protein family information in this form increases the prediction accuracy by six to eight percentage points. A combination of three levels of networks results in an overall three-state accuracy of 70.8% for globular proteins (sustained performance). If four membrane protein chains are included in the evaluation, the overall accuracy drops to 70.2%. The prediction is well balanced between alpha-helix, beta-strand and loop: 65% of the observed strand residues are predicted correctly. The accuracy in predicting the content of three secondary structure types is comparable to that of circular dichroism spectroscopy. The performance accuracy is verified by a sevenfold cross-validation test, and an additional test on 26 recently solved proteins. Of particular practical importance is the definition of a position-specific reliability index. For half of the residues predicted with a high level of reliability the overall accuracy increases to better than 82%. A further strength of the method is the more realistic prediction of segment length. The protein family prediction method is available for testing by academic researchers via an electronic mail server.

Mathematical Computing↗
PubMed2005

Protein secondary structure assignment revisited: a detailed analysis of different assignment methods.

BACKGROUND: A number of methods are now available to perform automatic assignment of periodic secondary structures from atomic coordinates, based on different characteristics of the secondary structures. In general these methods exhibit a broad consensus as to the location of most helix and strand core segments in protein structures. However the termini of the segments are often ill-defined and it is difficult to decide unambiguously which residues at the edge of the segments have to be included. In addition, there is a "twilight zone" where secondary structure segments depart significantly from the idealized models of Pauling and Corey. For these segments, one has to decide whether the observed structural variations are merely distorsions or whether they constitute a break in the secondary structure. METHODS: To address these problems, we have developed a method for secondary structure assignment, called KAKSI. Assignments made by KAKSI are compared with assignments given by DSSP, STRIDE, XTLSSTR, PSEA and SECSTR, as well as secondary structures found in PDB files, on 4 datasets (X-ray structures with different resolution range, NMR structures). RESULTS: A detailed comparison of KAKSI assignments with those of STRIDE and PSEA reveals that KAKSI assigns slightly longer helices and strands than STRIDE in case of one-to-one correspondence between the segments. However, KAKSI tends also to favor the assignment of several short helices when STRIDE and PSEA assign longer, kinked, helices. Helices assigned by KAKSI have geometrical characteristics close to those described in the PDB. They are more linear than helices assigned by other methods. The same tendency to split long segments is observed for strands, although less systematically. We present a number of cases of secondary structure assignments that illustrate this behavior. CONCLUSION: Our method provides valuable assignments which favor the regularity of secondary structure segments.

Algorithms↗
PubMed2000

Enhanced prediction accuracy of protein secondary structure using hydrogen exchange Fourier transform infrared spectroscopy.

A novel equilibrium hydrogen exchange Fourier transform IR (HX-FTIR) spectroscopy method for predicting secondary structure content was employed using spectra obtained for a training set of 23 globular proteins. The IR bandshape and frequency changes resulting from controlled levels of H-D exchange were observed to be protein-dependent. Their analysis revealed these variations to be partly correlated to secondary structure. For each protein, a set of 6 spectra was measured with a systematic variation of the solvent H-D ratio and was subjected to factor analysis. The most significant component spectra for each protein, representing independent aspects of the spectral response to deuteration, were each subjected to a second factor analysis over the entire training set. Restricted multiple regression (RMR) analysis using the loadings of the principal components from 19 of these H-D analyses revealed an improvement in prediction accuracy compared with conventional bandshape-based analyses of FTIR data. Nearly a factor of 2 reduction in error for prediction of helix fractions was found using s1, the average spectral response for the H-D set. In some cases, significant error reduction for prediction of minor components was found using higher factors. Using the same analytical methods, prediction errors with this new deuteration-response-FTIR method were shown to be even better than those obtained by use of electronic circular dichroism (ECD) data for helix predictions and to be significantly lower for ECD-based sheet prediction, making these the best secondary structure predictions obtained with the RMR method. Tests of a limited variable selection scheme showed further improvements, consistent with previous results of this approach using ECD data.

Deuterium Oxide↗
PubMed2006

Finding motifs in protein secondary structure for use in function prediction.

This paper presents a novel algorithm for the discovery of biological sequence motifs. Our motivation is the prediction of gene function. We seek to discover motifs and combinations of motifs in the secondary structure of proteins for application to the understanding and prediction of functional classes. The motifs found by our algorithm allow both flexible length structural elements and flexible length gaps and can be of arbitrary length. The algorithm is based on neither top-down nor bottom-up search, but rather is dichotomic. It is also "anytime," so that fixed termination of the search is not necessary. We have applied our algorithm to yeast sequence data to discover rules predicting function classes from secondary structure. These resultant rules are informative, consistent with known biology, and a contribution to scientific knowledge. Surprisingly, the rules also demonstrate that secondary structure prediction algorithms are effective for membrane proteins and suggest that the association between secondary structure and function is stronger in membrane proteins than globular ones. We demonstrate that our algorithm can successfully predict gene function directly from predicted secondary structure; e.g., we correctly predict the gene YGL124c to be involved in the functional class "cytoplasmic and nuclear degradation." Datasets and detailed results (generated motifs, rules, evaluation on test dataset, and predictions on unknown dataset) are available at www.aber.ac.uk/compsci/Research/bio/dss/yeast.ss.mips/, and www.genepredictions.org.

Algorithms↗
PubMed1993

Context dependence of protein secondary structure formation: the three-dimensional structure and stability of a hybrid between chymotrypsin inhibitor 2 and helix E from subtilisin Carlsberg.

The loop region of chymotrypsin inhibitor 2 from barley has been employed as a scaffold for testing the intrinsic propensity of a peptide fragment to form a secondary structure. The helix formation of the nine amino acid residue segment Lys-Gln-Ala-Val-Asp-Asn-Ala-Tyr-Ala of helix E from subtilisin Carlsberg has been studied by the construction of a hybrid consisting of chymotrypsin inhibitor 2 (CI2) where part of the active loop has been replaced by the nonapeptide. An expression system for a truncated form of CI2 where the 19 structureless residues of the N-terminus have been removed and Leu20 replaced by methionyl was constructed from the entire 83-residue wild-type CI2 gene by polymerase chain reaction methodology. The gene encoding the hybrid was constructed from the truncated inhibitor gene. The stability of the truncated inhibitor and of the hybrid toward guanidinium chloride denaturation was examined. From these measurements, the energy of unfolding in pure water was extrapolated to 30.5 +/- 1.0 kJ/mol for the truncated inhibitor and 10.9 +/- 0.3 kJ/mol for the hybrid. These energies show that the stability of CI2 is unaffected by the N-terminal truncation but severely decreased by the loop mutations. The three-dimensional structure of the hybrid protein has been determined in solution by nuclear magnetic resonance spectroscopy using 893 distance restraints and 84 torsional angle restraints. The average root-mean-square deviation (rmsd) of 15 structures compared to their geometrical average was 0.8 +/- 0.2 A for heavy backbone atoms and 1.3 +/- 0.2 A for all heavy atoms.(ABSTRACT TRUNCATED AT 250 WORDS)

Amino Acid Sequence↗
PubMed1999

Palmitoylation of lung surfactant protein SP-C alters surface thermodynamics, but not protein secondary structure or orientation in 1,2-dipalmitoylphosphatidylcholine langmuir films.

Pulmonary surfactant-specific protein, SP-C, isolated from porcine lung lavage, has been deacylated to investigate the role of the two thioester linked palmitoyl chains located near the N-terminus. Surface thermodynamic properties, secondary structure, and orientation of native and deacylated SP-C in 1, 2-dipalmitoylphosphatidylcholine (DPPC) monolayers has been characterized by combined surface pressure-molecular area (pi-A) isotherms and infrared reflection-absorption spectroscopy (IRRAS) measurements. The isotherms indicate that deacylation of SP-C produces more fluid monolayers at pressures less than 30 mN m-1. The helical secondary structure and tilt angle (70-80 degrees relative to the surface normal) of SP-C remained essentially unchanged upon deacylation in DPPC monolayers at a surface pressure approximately 30 mN m-1. The results are consistent with a model that acylation of SP-C may influence the rapid protein-aided spreading of a surface-associated surfactant reservoir, but not the structure of DPPC or SP-C in the monolayer at higher surface pressures.

1,2-Dipalmitoylphosphatidylcholine↗
PubMed2005

Effect of preparation conditions on protein secondary structure and biofilm formation of kafirin.

Various extraction and drying conditions for the isolation of kafirin from dry-milled, whole grain sorghum have been investigated, with a view to optimizing extraction of the protein for commercial food coatings and packaging films. The addition of sodium hydroxide to an aqueous ethanol extractant increased the yield and solubility of kafirin. Subsequent heat drying at 40 degrees C was shown to cause the kafirin to aggregate as indicated by an increase in intermolecular beta-sheets. Extraction of the flour using ethanol (70%, w/w) with 0.5% (w/w) sodium metabisulfite and 0.35% (w/w) sodium hydroxide at 70 degrees C followed by freeze-drying of the protein was found to produce a yield of 54% kafirin with good film-forming properties. The kafirin films were assessed for their sensory properties, tensile strength, strain, and water vapor permeability. Fourier transform infrared spectroscopy was used to study the secondary structure of the extracted kafirins. The best films were made with kafirin containing a large proportion of nativelike alpha-helical structures with little intermolecular beta-sheet content as indicated by the Fourier transform infrared reflectance peak intensity ratios associated with these secondary structures. The principal factor affecting the secondary structure of the protein appeared to be the temperature at which the protein was dried. Heat drying resulted in a greater proportion of intermolecular beta-sheets. Any industrial-scale extraction must therefore minimize protein aggregation and maximize native alpha-helical structures to achieve optimal film quality.

Flour↗
PubMed1993

Prediction of protein secondary structure by the hidden Markov model.

The purpose of this paper is to introduce a new method for analyzing the amino acid sequences of proteins using the hidden Markov model (HMM), which is a type of stochastic model. Secondary structures such as helix, sheet and turn are learned by HMMs, and these HMMs are applied to new sequences whose structures are unknown. The output probabilities from the HMMs are used to predict the secondary structures of the sequences. The authors tested this prediction system on approximately 100 sequences from a public database (Brookhaven PDB). Although the implementation is 'without grammar' (no rule for the appearance patterns of secondary structure) the result was reasonable.

Algorithms↗
PubMed1996

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

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

Algorithms↗
PubMed2001

"Soft"-cuticle protein secondary structure as revealed by FT-Raman, ATR FT-IR and CD spectroscopy.

The nature of the interaction of insect cuticular proteins and chitin is unknown even though about half of the cuticular proteins sequenced thus far share a consensus region that has been predicted to be the site of chitin binding. We previously predicted the preponderance of a beta-pleated sheet in the consensus region and proposed its responsibility for the formation of helicoidal cuticle (Iconomidou et al., Insect Biochem. Mol. Biol. 29 (1999) 285). In this study, we examined experimentally the secondary structure of intact and guanidine hydrochloride extracted cuticle and the cuticular protein extract. The studied cuticle came from the larval dorsal abdomen of the lepidopteran Hyalophora cecropia, a classical example of "soft" cuticle. Analysis with FT-Raman, ATR FT-IR and CD spectroscopy indicates that antiparallel beta-pleated sheet is the predominant molecular conformation of "soft-cuticle" proteins both in situ in the cuticle and following extraction. It seems that this conformation dictates the modes of chitin-protein interaction in cuticle, in agreement with earlier proposals (Atkins, J. Biosci. 8 (1985) 375).

Amino Acid Sequence↗
PubMedDate not supplied

[Information about the protein secondary structure improves quality of an alignment of protein sequences].

All popular algorithms of pair-wise alignment of protein primary structures (e.g. Smith-Waterman (SW), FASTA, BLAST, et al.) utilize only amino acid sequences. The SW-algorithm is the most accurate among them, i.e. it produces alignments that are most similar to the alignments obtained by superposition of protein 3D-structures. But even the SW-algorithm is unable to restore the 3D-based alignment if similarity of amino acid sequences (%id) is below 30%. We have proposed a novel alignment method that explicitly takes into account the secondary structure of the compared proteins. We have shown that it creates significantly more accurate alignments compared to SW-algorithm. In particular, for sequences with %id < 30% the average accuracy of the new method is 58% compared to 35% for SW-algorithm (the accuracy of an algorithmic sequence alignment is the part of restored position of a "golden standard" alignment obtained by superposition of corresponding 3D-structures). The accuracy of the proposed method is approximately identical both for experimental, and for theoretically predicted secondary structures. Thus the method can be applied for alignment of protein sequences even if protein 3D-structure is unknown. The program is available at ftp://194.149.64.196/STRUSWER/.

Algorithms↗
PubMed1992

Molecular cloning of cDNA and analysis of protein secondary structure of Candida albicans enolase, an abundant, immunodominant glycolytic enzyme.

We isolated and sequenced a clone for Candida albicans enolase from a C. albicans cDNA library by using molecular genetic techniques. The 1.4-kbp cDNA encoded one long open reading frame of 440 amino acids which was 87 and 75% similar to predicted enolases of Saccharomyces cerevisiae and enolases from other organisms, respectively. The cDNA included the entire coding region and predicted a protein of molecular weight 47,178. The codon usage was highly biased and similar to that found for the highly expressed EF-1 alpha proteins of C. albicans. Northern (RNA) blot analysis showed that the enolase cDNA hybridized to an abundant C. albicans mRNA of 1.5 kb present in both yeast and hyphal growth forms. The polypeptide product of the cloned cDNA, which was purified as a recombinant protein fused to glutathione S-transferase, had enolase enzymatic activity and inhibited radioimmunoprecipitation of a single C. albicans protein of molecular weight 47,000. Analysis of the predicted C. albicans enolase showed strong conservation in regions of alpha helices, beta sheets, and beta turns, as determined by comparison with the crystal structure of apo-enolase A of S. cerevisiae. The lack of cysteine residues and a two-amino-acid insertion in the main domain differentiated C. albicans enolase from S. cerevisiae enolase. Immunofluorescence of whole C. albicans cells by using a mouse antiserum generated against the purified fusion protein showed that enolase is not located on the surface of C. albicans. Recombinant C. albicans enolase will be useful in understanding the pathogenesis and host immune response in disseminated candidiasis, since enolase is an immunodominant antigen which circulates during disseminated infections.

Amino Acid Sequence↗
PubMed2000

Application of multiple sequence alignment profiles to improve protein secondary structure prediction.

The effect of training a neural network secondary structure prediction algorithm with different types of multiple sequence alignment profiles derived from the same sequences, is shown to provide a range of accuracy from 70.5% to 76.4%. The best accuracy of 76.4% (standard deviation 8.4%), is 3.1% (Q(3)) and 4.4% (SOV2) better than the PHD algorithm run on the same set of 406 sequence non-redundant proteins that were not used to train either method. Residues predicted by the new method with a confidence value of 5 or greater, have an average Q(3) accuracy of 84%, and cover 68% of the residues. Relative solvent accessibility based on a two state model, for 25, 5, and 0% accessibility are predicted at 76.2, 79.8, and 86. 6% accuracy respectively. The source of the improvements obtained from training with different representations of the same alignment data are described in detail. The new Jnet prediction method resulting from this study is available in the Jpred secondary structure prediction server, and as a stand-alone computer program from: http://barton.ebi.ac.uk/. Proteins 2000;40:502-511.

Algorithms↗