@article{open246,
          volume = {12},
          number = {5},
           month = {May},
          author = {Harpreet Kaur and G.P.S. Raghava},
            note = {Copyright of this article belongs to Wiley.},
           title = {A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment.},
       publisher = {Wiley},
            year = {2003},
         journal = {Protein science : a publication of the Protein Society},
           pages = {923--9},
        keywords = {?-Turns; prediction; neural networks; Weka classifiers; statistical; multiple alignment; secondary
structure; Web server},
             url = {http://crdd.osdd.net/open/246/},
        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) {\ensuremath{<}}/= 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.}
}