Robust multivariate statistics and the prediction of protein secondary structure content
نویسندگان
چکیده
منابع مشابه
Robust multivariate statistics and the prediction of protein secondary structure content.
measures the fraction of the response variance explained by The prediction of a protein structure solely from knowledge the model. However, over the last decade the need for more of its sequence remains one of the more elusive goals of rigorous validation regression models has become clear (Wold, molecular biology. Although recent success in using multiple 1991). This is because it is difficult...
متن کاملPrediction of protein secondary structure content.
All existing algorithms for predicting the content of protein secondary structure elements have been based on the conventional amino-acid-composition, where no sequence coupling effects are taken into account. In this article, an algorithm was developed for predicting the content of protein secondary structure elements that was based on a new amino-acid-composition, in which the sequence coupli...
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15 صفحه اولPrediction of protein secondary structure content by artificial neural network
The neural network method was applied to the prediction of the content of protein secondary structure elements, including alpha-helix, beta-strand, beta-bridge, 3(10)-helix, pi-helix, H-bonded turn, bend, and random coil. The "pair-coupled amino acid composition" originally proposed by K. C. Chou [J Protein Chem 1999, 18, 473] was adopted as the input. Self-consistency and independent-dataset t...
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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. New techniques that apply machine learning and discriminant analysis show promise as alternatives to neural networks.
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ژورنال
عنوان ژورنال: Protein Engineering Design and Selection
سال: 1997
ISSN: 1741-0126,1741-0134
DOI: 10.1093/protein/10.7.747