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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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A mathematical model for secondary structure in proteins
13th IEEE International Conference on BioInformatics and BioEngineering, 2013We propose a new mathematical model for secondary structure in proteins. Our model is inspired by percolation theory on binary strings. What sets us apart from similar work on the subject is our attempt to deviate from a data mining approach (which is mostly the trend is science these days). Therefore, in predicting secondary structures, we make it our
Alexey Nikolaev, Saad Mneimneh
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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 ...
W R, Taylor, J M, Thornton
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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 ...
Stefania, Perticaroli +5 more
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Protein Secondary Structure Analysis in the Cloud
Proceedings of the 6th International Workshop on Parallelism in Bioinformatics, 2018Many biological problems, such finding recurring geometrical patterns in the secondary structures of protein pairs, are often solved by using parallel applications running on HPC systems that, thanks to their powerful architecture and high number of CPUs, can yield good performance.
Marco Ferretti, Luigi Santangelo
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Searching on the Secondary Structure of Protein Sequences
2002In spite of the many decades of progress in database research, surprisingly scientists in the life sciences community still struggle with inefficient and awkward tools for querying biological data sets. This work highlights a specific problem involving searching large volumes of protein data sets based on their secondary structure.
Laurie Hammel, Jignesh M. Patel
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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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A model of morphogenesis for protein secondary structures
Journal of Theoretical Biology, 1991This paper proposes a model for the expected probability distribution for a certain class of biological structures. In particular, a model is derived for the distribution of lengths of helices, sheets, turns, and coils as a function of the length of the structure divided by the length of the protein it is contained in.
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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, 2005Motivated 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.
Uros Midic +2 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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