Improved Chou-Fasman method for protein secondary structure prediction
نویسندگان
چکیده
منابع مشابه
Why the Chou-Fasman parameters are not reliable in protein secondary structure prediction Keywords: Chou-Fasman parameters / Protein prediction / Secondary structure
The Chou-Fasman method has been a widely used method for protein secondary structure prediction. This method is based on knowledge about amino acid residues potential to form αhelical regions or β-sheet regions in proteins. Our main interest in this study was to examine the reliability of these Chou-Fasman parameters. We have calculated the Chou-Fasman parameters, with 95% confidence limits, ou...
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A novel computerized program has been developed for predicting the secondary structure of proteins from their amino acid sequences. The scheme of the Chou and Fasman method (1978, Adv. Enzymol. Relat. Subj. Biochem. 47, 45-148) is closely followed. Some of their qualitative rules have been converted to numeric scales to obtain unambiguous predictions. This program has been tested on 21 proteins...
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Neural networks have conventionally been used to predict protein secondary structure. However, they have not been used to improve the predictions of existing methods. This paper presents the design and implementation of a neural network to refine secondary structure prediction using information obtained from the DSSP and Chou-Fasman algorithms. The network was trained with input patterns consis...
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We describe a method for using machine learning to refine algorithms represented as generalized finite-state automata. The knowledge in an automaton is translated into a corresponding artificial neural network, and then refined by applying backpropagation to a set of examples. Our technique for translating an automaton into a network extends the KBANN algorithm, a system that translates a set o...
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We present a new method for protein secondary structure prediction, based on the recognition of well-defined pentapeptides, in a large databank. Using a databank of 635 protein chains, we obtained a success rate of 68.6%. We show that progress is achieved when the databank is enlarged, when the 20 amino acids are adequately grouped in 10 sets and when more pentapeptides are attributed one of th...
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ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2006
ISSN: 1471-2105
DOI: 10.1186/1471-2105-7-s4-s14