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Protein Secondary Structure Prediction with SPARROW
Journal of Chemical Information and Modeling, 2012A 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 +2 more
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2010
There are several methods available that can be used to gain information on secondary structure, apart from X-ray crystallography, which remains the ‘gold standard’.
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There are several methods available that can be used to gain information on secondary structure, apart from X-ray crystallography, which remains the ‘gold standard’.
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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.
William R. Taylor, Janet M. Thornton
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Determination of Protein Secondary Structure
1996Circular dichroism (CD) is one of the most sensitive physical techniques for determining structures and monitoring structural changes of biomolecules. It can directly interpret the changes of protein secondary structure, even though the method is empirical. The far-ultraviolet (far-UV) CD specra (below 250 nm) of proteins are extremely sensitive toward
Sergei Yu Venyaminov +2 more
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Chemogenomics with Protein Secondary-Structure Mimetics
2009During molecular recognition of proteins in biological systems, helices, reverse turns, and beta-sheets are dominant motifs. Often there are therapeutic reasons for blocking such recognition sites, and significant progress has been made by medicinal chemists in the design and synthesis of semirigid molecular scaffolds on which to display amino acid ...
Garland R. Marshall +2 more
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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, Steven A. Benner
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Recognition of super-secondary structure in proteins
Journal of Molecular Biology, 1984A procedure to recognize super-secondary structure in protein sequences is described. An idealized template, derived from known super-secondary structures, is used to locate probable sites by matching with secondary structure probability profiles. We applied the method to the identification of beta alpha beta units in beta/alpha type proteins with 75 ...
Janet M. Thornton, William R. Taylor
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The protein secondary structure flexibility
2008Modeling protein flexibility is a long standing challenge in computational biology with a special impact to protein docking. Relating problems are protein structures alignment and identification of flexible and rigid protein regions, as well as a general definition of a region degree of flexibility.
Šikić, Krešimir +2 more
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Secondary structure and rigidity in model proteins
Soft Matter, 2013There is tremendous interest in understanding the role that secondary structure plays in the rigidity and dynamics of proteins. In this work we analyze nanomechanical properties of proteins chosen to represent different secondary structures: α-helices (myoglobin and bovine serum albumin), β-barrels (green fluorescent protein), and α + β + loop ...
Jonathan D. Nickels +9 more
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A Grid-Enabled Protein Secondary Structure Predictor
IEEE Transactions on Nanobioscience, 2007We present an integrated Grid system for the prediction of protein secondary structures, based on the frequent automatic update of proteins in the training set. The predictor model is based on a feed-forward multilayer perceptron (MLP) neural network which is trained with the back-propagation algorithm; the design reuses existing legacy software and ...
M. MIRTO +4 more
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