Results 281 to 290 of about 418,398 (323)

Protein secondary structure prediction

Current Opinion in Structural Biology, 1995
The past year has seen a consolidation of protein secondary structure prediction methods. The advantages of prediction from an aligned family of proteins have been highlighted by several accurate predictions made 'blind', before any X-ray or NMR structure was known for the family.
S R, Krystek, W J, Metzler, J, Novotny
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Protein Secondary Structure Prediction with SPARROW

Journal of Chemical Information and Modeling, 2012
A first step toward predicting the structure of a protein is to determine its secondary structure. The secondary structure information is generally used as starting point to solve protein crystal structures. In the present study, a machine learning approach based on a complete set of two-class scoring functions was used.
Francesco, Bettella, Dawid, Rasinski, Ernst Walter, Knapp   +2 more
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Protein Secondary Structure Prediction

2009
While 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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Parallelized protein secondary structure prediction

Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826), 2005
Functional characterization of a protein sequence is one of the most frequent problems in biology. Secondary structure prediction is a useful first step in understanding how the amino acid sequence of a protein determines the native state. There are many previous famous prediction methods available now, although most of them allow users to submit their
null Yu-Tao Qi, null Feng Lin
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Predicting secondary structures of proteins

IEEE Engineering in Medicine and Biology Magazine, 2005
The 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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Improving Protein Secondary-Structure Prediction by Predicting Ends of Secondary-Structure Segments

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2005
Motivated by known preferences for certain amino acids in positions around a-helices, we developed neural network-based predictors of both N and C a-helix ends, which achieved about 88% accuracy. We applied a similar approach for predicting the ends of three types of secondary structure segments.
U. Midic, A.K. Dunker, Z. Obradovic
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Protein Secondary Structure Prediction Approaches

2020
The prediction of protein secondary structure from a protein sequence provides useful information for predicting the three-dimensional structure and function of the protein. In recent decades, protein secondary structure prediction systems have been improved benefiting from the advances in computational techniques as well as the growth and increased ...
Fawaz H. H. Mahyoub, Rosni Abdullah
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Protein secondary structure prediction using local alignments

Journal of Molecular Biology, 1997
The accuracy of secondary structure prediction methods has been improved significantly by the use of aligned protein sequences. The PHD method and the NNSSP method reach 71 to 72% of sustained overall three-state accuracy when multiple sequence alignments are with neural networks and nearest-neighbor algorithms, respectively.
A A, Salamov, V V, Solovyev
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Protein secondary structure prediction with dihedral angles

Proteins: Structure, Function, and Bioinformatics, 2005
AbstractWe 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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Evaluation of secondary structure predictions in proteins

Biochimica et Biophysica Acta (BBA) - Protein Structure, 1977
Data 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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