creators_name: Kaur, Harpreet
creators_name: Raghava, G.P.S.
type: article
datestamp: 2012-01-06 14:59:25
lastmod: 2012-01-06 14:59:26
metadata_visibility: show
title: A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment.
ispublished: pub
subjects: QR
full_text_status: restricted
keywords: �-Turns; prediction; neural networks; Weka classifiers; statistical; multiple alignment; secondary
structure; Web server
note: Copyright of this article belongs to Wiley.
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
date_type: published
publication: Protein science : a publication of the Protein Society
volume: 12
number: 5
publisher: Wiley
pagerange: 923-9
refereed: TRUE
issn: 0961-8368
official_url: http://onlinelibrary.wiley.com/doi/10.1110/ps.0241703/pdf
related_url_url: http://onlinelibrary.wiley.com/doi/10.1110/ps.0241703/pdf
related_url_type: pub
citation:   Kaur, Harpreet and Raghava, G.P.S.  (2003) A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment.  Protein science : a publication of the Protein Society, 12 (5).  pp. 923-9.  ISSN 0961-8368     
document_url: http://crdd.osdd.net/open/246/1/raghava2003.1.pdf