Neural network prediction of 3(10)-helices in proteins.
Indian J Biochem Biophys
;
2001 Feb-Apr; 38(1-2): 107-14
Artículo
en Inglés
| IMSEAR
| ID: sea-26355
ABSTRACT
Secondary structure prediction from the primary sequence of a protein is fundamental to understanding its structure and folding properties. Although several prediction methodologies are in vogue, their performances are far from being completely satisfactory. Among these, non-linear neural networks have been shown to be relatively effective, especially for predicting beta-turns, where dominant interactions are local, arising from four sequence-contiguous residues. Most 3(10)-helices in proteins are also short, comprising of three sequence-contiguous residues and two capping residues. In order to understand the extent of local interactions in these 3(10)-helices, we have applied a neural network model with varying window size to predict 3(10)-helices in proteins. We found the prediction accuracy of 3(10)-helices (approximately 14%), as judged by the Matthew's Correlation Coefficient, to be less than that of beta-turns (approximately 20%). The optimal window size for the prediction of 3(10)-helices was about 9 residues. The significance and implications of these results in understanding the occurrence of 3(10)-helices and preferences of amino acid residues in 3(10)-helices are discussed.
Texto completo:
Disponible
Índice:
IMSEAR (Asia Sudoriental)
Asunto principal:
Unión Proteica
/
Programas Informáticos
/
Proteínas
/
Reproducibilidad de los Resultados
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Modelos Estadísticos
/
Bases de Datos Factuales
/
Redes Neurales de la Computación
/
Estructura Secundaria de Proteína
/
Enlace de Hidrógeno
/
Modelos Químicos
Tipo de estudio:
Estudio pronóstico
/
Factores de riesgo
Idioma:
Inglés
Revista:
Indian J Biochem Biophys
Año:
2001
Tipo del documento:
Artículo
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