Results 211 to 220 of about 214,637 (252)
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Prediction of super-secondary structure in proteins
Nature, 1983Various methods for the prediction of secondary structure from amino acid sequence can consistently achieve on average 60% accuracy when tested for several proteins. Improvement on this value has proved difficult, despite increasing the size of the data set and refining predictive techniques.
W R, Taylor, J M, Thornton
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Protein Secondary Structure Prediction
2009While the prediction of a native protein structure from sequence continues to remain a challenging problem, over the past decades computational methods have become quite successful in exploiting the mechanisms behind secondary structure formation. The great effort expended in this area has resulted in the development of a vast number of secondary ...
Pirovano, W.A., Heringa, J.
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Evaluating Predictions of Secondary Structure in Proteins
Biochemical and Biophysical Research Communications, 1994To learn how secondary structure assignments diverge during divergent evolution, pairs of proteins with solved crystal structures were aligned and their assignments compared as a function of evolutionary distance. Residues assigned in one structure to a helix or a strand are frequently paired with residues assigned in the other to a coil.
T F, Jenny, S A, Benner
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Improving Protein Secondary Structure Predictions by Prediction Fusion
Information Fusion, 2009Protein secondary structure prediction is still a challenging problem at today. Even if a number of prediction methods have been presented in the literature, the various prediction tools that are available on-line produce results whose quality is not always fully satisfactory.
Giuseppe Tradigo +2 more
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Evaluation of secondary structure predictions in proteins
Biochimica et Biophysica Acta (BBA) - Protein Structure, 1977Data of 33 proteins are used to compare four methods which predict secondary structure from the amino acid sequence. The prediction of alpha-helices according to the histogram method of Argos et al. (Argos, P., Schwarz, J. and Schwarz, J. (1976) Biochim. Biophys.
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Prediction of Protein Structural Classes Based on Predicted Secondary Structure
2015Prediction of protein structural classes is an important area in bioinformatics, it is beneficial to research protein function, regulation and interactions. In this paper, a 20-dimensional feature vector is extracted based on the predicted secondary structure sequence and the corresponding E-H sequence.
Fanliang Kong +3 more
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Predicting secondary structures of proteins
IEEE Engineering in Medicine and Biology Magazine, 2005The article presents the application of a new machine-learning algorithm for the prediction of secondary structures of proteins. The logical analysis of data (LAD) algorithm was applied to recognize which amino acids properties could be analyzed to deliver additional information, independent from protein homology, useful in determining the secondary ...
J. Blazewicz, P.L. Hammer, P. Lukasiak
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Secondary structure prediction and protein design.
Biochemical Society symposium, 1990For non-homologous proteins, and after cross-validation, the methods reviewed in this article exhibit a probability index (percentage of correctly predicted residues per predicted residues) of 59-65.5% according to the methods employed with a standard deviation of 7% for three conformational states: alpha-helix, beta-strand and coil.
Garnier, Jean +3 more
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Evolutionary computation for the prediction of secondary protein structures
Proceedings of the 2011 ACM Symposium on Applied Computing, 2011We have developed an evolutionary computation approach to predict secondary structure motifs using some main amino acid physical-chemical properties. The prediction model will consist of rules that predict both the beginning and the end of the regions corresponding to a secondary structure state conformation (α-helix or β-strand).
Alfonso E. Márquez Chamorro +2 more
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Protein secondary structure prediction with dihedral angles
Proteins: Structure, Function, and Bioinformatics, 2005AbstractWe present DESTRUCT, a new method of protein secondary structure prediction, which achieves a three‐state accuracy (Q3) of 79.4% in a cross‐validated trial on a nonredundant set of 513 proteins. An iterative set of cascade–correlation neural networks is used to predict both secondary structure and ψ dihedral angles, with predicted values ...
Matthew J, Wood, Jonathan D, Hirst
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