The predicted secondary structure of enolase.
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ABSTRACT:
SUBMITTER: Sawyer L
PROVIDER: S-EPMC1146795 | biostudies-other | 1986 May
REPOSITORIES: biostudies-other
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Project description:BACKGROUND: Residue depth allows determining how deeply a given residue is buried, in contrast to the solvent accessibility that differentiates between buried and solvent-exposed residues. When compared with the solvent accessibility, the depth allows studying deep-level structures and functional sites, and formation of the protein folding nucleus. Accurate prediction of residue depth would provide valuable information for fold recognition, prediction of functional sites, and protein design. RESULTS: A new method, RDPred, for the real-value depth prediction from protein sequence is proposed. RDPred combines information extracted from the sequence, PSI-BLAST scoring matrices, and secondary structure predicted with PSIPRED. Three-fold/ten-fold cross validation based tests performed on three independent, low-identity datasets show that the distance based depth (computed using MSMS) predicted by RDPred is characterized by 0.67/0.67, 0.66/0.67, and 0.64/0.65 correlation with the actual depth, by the mean absolute errors equal 0.56/0.56, 0.61/0.60, and 0.58/0.57, and by the mean relative errors equal 17.0%/16.9%, 18.2%/18.1%, and 17.7%/17.6%, respectively. The mean absolute and the mean relative errors are shown to be statistically significantly better when compared with a method recently proposed by Yuan and Wang [Proteins 2008; 70:509-516]. The results show that three-fold cross validation underestimates the variability of the prediction quality when compared with the results based on the ten-fold cross validation. We also show that the hydrophilic and flexible residues are predicted more accurately than hydrophobic and rigid residues. Similarly, the charged residues that include Lys, Glu, Asp, and Arg are the most accurately predicted. Our analysis reveals that evolutionary information encoded using PSSM is characterized by stronger correlation with the depth for hydrophilic amino acids (AAs) and aliphatic AAs when compared with hydrophobic AAs and aromatic AAs. Finally, we show that the secondary structure of coils and strands is useful in depth prediction, in contrast to helices that have relatively uniform distribution over the protein depth. Application of the predicted residue depth to prediction of buried/exposed residues shows consistent improvements in detection rates of both buried and exposed residues when compared with the competing method. Finally, we contrasted the prediction performance among distance based (MSMS and DPX) and volume based (SADIC) depth definitions. We found that the distance based indices are harder to predict due to the more complex nature of the corresponding depth profiles. CONCLUSION: The proposed method, RDPred, provides statistically significantly better predictions of residue depth when compared with the competing method. The predicted depth can be used to provide improved prediction of both buried and exposed residues. The prediction of exposed residues has implications in characterization/prediction of interactions with ligands and other proteins, while the prediction of buried residues could be used in the context of folding predictions and simulations.
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Project description:RNA structure is conformationally dynamic, and accurate all-atom tertiary (3D) structure modeling of RNA remains challenging with the prevailing tools. Secondary structure (2D) information is the standard prerequisite for most RNA 3D modeling. Despite several 2D and 3D structure prediction tools proposed in recent years, one of the challenges is to choose the best combination for accurate RNA 3D structure prediction. Here, we benchmarked seven small RNA PDB structures (40 to 90 nucleotides) with different topologies to understand the effects of different 2D structure predictions on the accuracy of 3D modeling. The current study explores the blind challenge of 2D to 3D conversions and highlights the performances of de novo RNA 3D modeling from their predicted 2D structure constraints. Our results show that conformational sampling-based methods such as SimRNA and IsRNA1 depend less on 2D accuracy, whereas motif-based methods account for 2D evidence. Our observations illustrate the disparities in available 3D and 2D prediction methods and may further offer insights into developing topology-specific or family-specific RNA structure prediction pipelines.
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Project description:We present a method for predicting folding rates of proteins from their amino acid sequences only, or rather, from their chain lengths and their helicity predicted from their sequences. The method achieves 82% correlation with experiment over all 64 "two-state" and "multistate" proteins (including two artificial peptides) studied up to now.
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Project description:BACKGROUND: Prediction of protein structural classes (alpha, beta, alpha + beta and alpha/beta) from amino acid sequences is of great importance, as it is beneficial to study protein function, regulation and interactions. Many methods have been developed for high-homology protein sequences, and the prediction accuracies can achieve up to 90%. However, for low-homology sequences whose average pairwise sequence identity lies between 20% and 40%, they perform relatively poorly, yielding the prediction accuracy often below 60%. RESULTS: We propose a new method to predict protein structural classes on the basis of features extracted from the predicted secondary structures of proteins rather than directly from their amino acid sequences. It first uses PSIPRED to predict the secondary structure for each protein sequence. Then, the chaos game representation is employed to represent the predicted secondary structure as two time series, from which we generate a comprehensive set of 24 features using recurrence quantification analysis, K-string based information entropy and segment-based analysis. The resulting feature vectors are finally fed into a simple yet powerful Fisher's discriminant algorithm for the prediction of protein structural classes. We tested the proposed method on three benchmark datasets in low homology and achieved the overall prediction accuracies of 82.9%, 83.1% and 81.3%, respectively. Comparisons with ten existing methods showed that our method consistently performs better for all the tested datasets and the overall accuracy improvements range from 2.3% to 27.5%. A web server that implements the proposed method is freely available at http://www1.spms.ntu.edu.sg/~chenxin/RKS_PPSC/. CONCLUSION: The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the predicted secondary structure sequences, which is capable of characterizing the sequence order information, local interactions of the secondary structural elements, and spacial arrangements of alpha helices and beta strands. Thus, it is a valuable method to predict protein structural classes particularly for low-homology amino acid sequences.
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Project description:Enolase is an important enzyme in glycolysis and various biological processes. Its dysfunction is closely associated with diseases. Here, the enolase from Drosophila melanogaster (DmENO) was purified and crystallized. A crystal of DmENO diffracted to 2.0 Å resolution and belonged to space group R32. The structure was solved by molecular replacement. Like most enolases, DmENO forms a homodimer with conserved residues in the dimer interface. DmENO possesses an open conformation in this structure and contains conserved elements for catalytic activity. This work provides a structural basis for further functional and evolutionary studies of enolase.
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Project description:Enolase is a dimeric metal-activated metalloenzyme which uses two magnesium ions per subunit: the strongly bound conformational ion and the catalytic ion that binds to the enzyme-substrate complex inducing catalysis. The crystal structure of the human neuronal enolase-Mg2F2P(i) complex (enolase fluoride/phosphate inhibitory complex, EFPIC) determined at 1.36 A resolution shows that the combination of anions effectively mimics an intermediate state in catalysis. The phosphate ion binds in the same site as the phosphate group of the substrate/product, 2-phospho-D-glycerate/phosphoenolpyruvate, and induces binding of the catalytic Mg2+ ion. One fluoride ion bridges the structural and catalytic magnesium ions while the other interacts with the structural magnesium ion and the ammonio groups of Lys 342 and Lys 393. These fluoride ion positions correspond closely to the positions of the oxygen atoms of the substrate's carboxylate moiety. To relate structural changes resulting from fluoride, phosphate, and magnesium ions binding to those that are induced by phosphate and magnesium ions alone, we also determined the structure of the human neuronal enolase-Mg2P(i) complex (enolase phosphate inhibitory complex, EPIC) at 1.92 A resolution. It shows the closed conformation in one subunit and a mixture of open and semiclosed conformations in the other. The EPFIC dimer is essentially symmetric while the EPIC dimer is asymmetric. Isothermal titration calorimetry data confirmed binding of four fluoride ions per dimer and yielded Kb values of 7.5 x 10(5) +/- 1.3 x 10(5), 1.2 x 10(5) +/- 0.2 x 10(5), 8.6 x 10(4) +/- 1.6 x 10(4), and 1.6 x 10(4) +/- 0.7 x 10(4) M(-1). The different binding constants indicate negative cooperativity between the subunits; the asymmetry of EPIC supports such an interpretation.
| S-EPMC2566932 | biostudies-literature
Project description: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.
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Project description:Influenza A virus (IAV) is a segmented negative-sense RNA virus that causes seasonal epidemics and periodic pandemics in humans. Two regions (nucleotide positions 82-148 and 497-564) in the positive-sense RNA of the NS segment fold into a multi-branch loop or hairpin structures.We studied 25,384 NS segment positive-sense RNA unique sequences of human and non-human IAVs in order to predict secondary RNA structures of the 82-148 and 497-564 regions using RNAfold software, and determined their host- and lineage-specific distributions. Hairpins prevailed in avian and avian-origin human IAVs, including H1N1pdm1918 and H5N1. In human and swine IAV hairpins distribution varied between evolutionary lineages.These results suggest a possible functional role for these RNA secondary structures and the need for experimental evaluation of these structures in the influenza life cycle.
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Project description:BackgroundNon-coding RNAs (ncRNAs) have a multitude of roles in the cell, many of which remain to be discovered. However, it is difficult to detect novel ncRNAs in biochemical screens. To advance biological knowledge, computational methods that can accurately detect ncRNAs in sequenced genomes are therefore desirable. The increasing number of genomic sequences provides a rich dataset for computational comparative sequence analysis and detection of novel ncRNAs.ResultsHere, Dynalign, a program for predicting secondary structures common to two RNA sequences on the basis of minimizing folding free energy change, is utilized as a computational ncRNA detection tool. The Dynalign-computed optimal total free energy change, which scores the structural alignment and the free energy change of folding into a common structure for two RNA sequences, is shown to be an effective measure for distinguishing ncRNA from randomized sequences. To make the classification as a ncRNA, the total free energy change of an input sequence pair can either be compared with the total free energy changes of a set of control sequence pairs, or be used in combination with sequence length and nucleotide frequencies as input to a classification support vector machine. The latter method is much faster, but slightly less sensitive at a given specificity. Additionally, the classification support vector machine method is shown to be sensitive and specific on genomic ncRNA screens of two different Escherichia coli and Salmonella typhi genome alignments, in which many ncRNAs are known. The Dynalign computational experiments are also compared with two other ncRNA detection programs, RNAz and QRNA.ConclusionThe Dynalign-based support vector machine method is more sensitive for known ncRNAs in the test genomic screens than RNAz and QRNA. Additionally, both Dynalign-based methods are more sensitive than RNAz and QRNA at low sequence pair identities. Dynalign can be used as a comparable or more accurate tool than RNAz or QRNA in genomic screens, especially for low-identity regions. Dynalign provides a method for discovering ncRNAs in sequenced genomes that other methods may not identify. Significant improvements in Dynalign runtime have also been achieved.
| S-EPMC1570369 | biostudies-literature
Project description:Predictions of protein secondary structure are used with amino acid sequence alignments to show that the N-terminal domains of cyclodextrin glucanotransferases and a yeast alpha-glucosidase may have the same super-secondary structure as alpha-amylases, i.e. an (alpha/beta)8-barrel fold. Sequence similarities provide evidence that glucanotransferases, and possibly the glucosidase, are, like alpha-amylases, Ca2+-containing enzymes. The relationship between substrate specificity and the nature of the amino acid residues proposed at the active site is discussed for the transferases and alpha-glucosidase. A set of three programs for an Apple IIe computer to carry out the calculations described by Garnier, Osguthorpe & Robson [(1978) J. Mol. Biol. 120, 97-120] and a set of four programs for an Apple IIe computer to carry out the calculations described by Levin, Robson & Garnier [(1986) FEBS Lett. 205, 303-308] have been deposited as Supplementary Publication SUP 50149 (25 pages) at the British Library Document Supply Centre, Boston Spa, Wetherby, West Yorkshire LS23 7BQ, U.K., from whom copies can be obtained on the terms indicated in Biochem. J. (1989) 257, 5.
| S-EPMC1138484 | biostudies-other