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primary topic: A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment.

A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment.
type: AcademicArticle
type: Article
abstract: In the present study, an attempt has been made to develop a method for predicting gamma-turns in proteins. First, we have implemented the commonly used statistical and machine-learning techniques in the field of protein structure prediction, for the prediction of gamma-turns. All the methods have been trained and tested on a set of 320 nonhomologous protein chains by a fivefold cross-validation technique. It has been observed that the performance of all methods is very poor, having a Matthew's Correlation Coefficient (MCC) </= 0.06. Second, predicted secondary structure obtained from PSIPRED is used in gamma-turn prediction. It has been found that machine-learning methods outperform statistical methods and achieve an MCC of 0.11 when secondary structure information is used. The performance of gamma-turn prediction is further improved when multiple sequence alignment is used as the input instead of a single sequence. Based on this study, we have developed a method, GammaPred, for gamma-turn prediction (MCC = 0.17). The GammaPred is a neural-network-based method, which predicts gamma-turns in two steps. In the first step, a sequence-to-structure network is used to predict the gamma-turns from multiple alignment of protein sequence. In the second step, it uses a structure-to-structure network in which input consists of predicted gamma-turns obtained from the first step and predicted secondary structure obtained from PSIPRED.
date: 2003-05
volume: 12
issue: 5
is part of: Protein science : a publication of the Protein Society
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
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subject: QR Microbiology

A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment. (PDF)
type: Document

Wiley
type: Organization

Protein science : a publication of the Protein Society
type: Collection
issn: 09618368

G.P.S. Raghava
type: Person
given name: G.P.S.
family name: Raghava

Harpreet Kaur
type: Person
given name: Harpreet
family name: Kaur

A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment. (Image (PNG))
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A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment. (Image (PNG))
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A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment. (Image (PNG))
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A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment. (Image (PNG))
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A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment. (Indexer Terms)
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primary topic: A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment.

QR Microbiology
type: Concept