NN-PRED: A novel consensus secondary structure prediction program using neural networks

In this paper, a new program for protein secondary structure prediction is proposed. The program, which is called NN-Pred, allows multiple sequences to be submitted and it returns predictions from fve secondary structure prediction algorithms. In addition, NN-Pred calculates a consensus prediction,...

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Bibliographic Details
Main Authors: Oscar Fernando Bedoya Leiva, Eduard Alberto Satizábal Tascón
Format: Article
Language:English
Published: Universidad Industrial de Santander 2013-03-01
Series:Revista UIS Ingenierías
Subjects:
Online Access:https://revistas.uis.edu.co/index.php/revistauisingenierias/article/view/3711
Description
Summary:In this paper, a new program for protein secondary structure prediction is proposed. The program, which is called NN-Pred, allows multiple sequences to be submitted and it returns predictions from fve secondary structure prediction algorithms. In addition, NN-Pred calculates a consensus prediction, which is based on a neural network strategy that is used in this paper to improve the prediction accuracy. NN-Pred was obtained by using a methodology called consensus strategy, which tries to make a better prediction by integrating some of the most remarkable existing techniques. The NN-Pred program provides a three-state (alpha-helix, beta-sheet, and other) prediction of secondary structure. According to the test sets, the prediction accuracy of NN-Pred is at least 70%, surpassing most of the existing methods. The experiments showed that neural networks can be used as a consensus strategy to producing accurate models for protein secondary prediction.
ISSN:1657-4583
2145-8456