MASSP3: a system for predicting protein secondary structure

Authors:
Giuliano Armano;Alessandro Orro;Eloisa Vargiu
Affiliations:
Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d'Armi, Cagliari, Italy;Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d'Armi, Cagliari, Italy;Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d'Armi, Cagliari, Italy
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2006

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Abstract

A system that resorts to multiple experts for dealing with the problem of predicting secondary structures is described, whose performances are comparable to those obtained by other state-of-the-art predictors. The system performs an overall processing based on two main steps: first, a "sequence-to-structure" prediction is performed, by resorting to a population of hybrid genetic-neural experts, and then a "structure-to-structure" prediction is performed, by resorting to a feedforward artificial neural networks. To investigate the performance of the proposed approach, the system has been tested on the RS126 set of proteins. Experimental results (about 76% of accuracy) point to the validity of the approach.