TY  - JOUR
ID  - open246
UR  - http://onlinelibrary.wiley.com/doi/10.1110/ps.0241703/pdf
IS  - 5
A1  - Kaur, Harpreet
A1  - Raghava, G.P.S.
N2  - 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.
VL  - 12
TI  - A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment.
AV  - restricted
EP  - 9
N1  - Copyright of this article belongs to Wiley.
Y1  - 2003/05//
PB  - Wiley
JF  - Protein science : a publication of the Protein Society
KW  - ?-Turns; prediction; neural networks; Weka classifiers; statistical; multiple alignment; secondary
structure; Web server
SN  - 0961-8368
SP  - 923
ER  -