Prediction of α-turns in proteins using PSI-BLAST profiles and secondary structure information

Kaur, Harpreet ; Raghava, G. P. S. (2004) Prediction of α-turns in proteins using PSI-BLAST profiles and secondary structure information Proteins: Structure, Function, and Genetics, 55 (1). pp. 83-90. ISSN 0887-3585

[img]
Preview
PDF - Publisher Version
250kB

Official URL: http://onlinelibrary.wiley.com/doi/10.1002/prot.10...

Related URL: http://dx.doi.org/10.1002/prot.10569

Abstract

In this paper a systematic attempt has been made to develop a better method for predicting α-turns in proteins. Most of the commonly used approaches in the field of protein structure prediction have been tried in this study, which includes statistical approach "Sequence Coupled Model" and machine learning approaches; i) artificial neural network (ANN); ii) Weka (Waikato Environment for Knowledge Analysis) Classifiers and iii) Parallel Exemplar Based Learning (PEBLS). We have also used multiple sequence alignment obtained from PSIBLAST and secondary structure information predicted by PSIPRED. The training and testing of all methods has been performed on a data set of 193 non-homologous protein X-ray structures using five-fold cross-validation. It has been observed that ANN with multiple sequence alignment and predicted secondary structure information outperforms other methods. Based on our observations we have developed an ANN-based method for predicting α-turns in proteins. The main components of the method are two feed-forward back-propagation networks with a single hidden layer. The first sequence-structure network is trained with the multiple sequence alignment in the form of PSI-BLAST-generated position specific scoring matrices. The initial predictions obtained from the first network and PSIPRED predicted secondary structure are used as input to the second structure-structure network to refine the predictions obtained from the first net. The final network yields an overall prediction accuracy of 78.0% and MCC of 0.16. A web server AlphaPred (http://www.imtech.res.in/raghava/alphapred/) has been developed based on this approach.

Item Type:Article
Source:Copyright of this article belongs to John Wiley and Sons.
Keywords:Neural Networks; Multiple Alignment; Tight Turns; Web Server; Weka; PEBLS
ID Code:43052
Deposited On:09 Jun 2011 11:13
Last Modified:18 May 2016 00:09

Repository Staff Only: item control page