MASSP3: A System for Predicting Protein Secondary Structure
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MASSP3: A System for Predicting Protein Secondary Structure Giuliano Armano, Alessandro Orro, and Eloisa Vargiu Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123 Cagliari, Italy Received 15 May 2005; Revised 22 September 2005; Accepted 1 December 2005 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. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
1.
INTRODUCTION
Due to the strict relation between protein function and structure, the prediction of protein 3D structure has during recent years become one of the most important tasks in bioinformatics. In fact, notwithstanding the increase of experimental data on protein structures available in public databases, the gap between known sequences (165,000 entries in Swiss-Prot [1] in December 2004) and known tertiary structures (28,000 entries in PDB [2] in December 2004) is constantly increasing. The need for automatic methods has brought the development of several prediction and modeling tools, but despite the increase of accuracy a general methodology to solve the problem has not been yet devised. Building complete protein tertiary structure is still not a tractable task, and most methodologies concentrate on the simplified task of predicting their secondary structure. In fact, the knowledge of secondary structure is a useful starting point for further investigating the problem of finding protein tertiary structures and functionalities. In this paper, we concentrate on the problem of predicting secondary structures using a system that 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 network (ANN). Multiple experts are the underlying technology of the former subsystem, also rooted in two powerful soft-computing techniques, that is, genetic and neural. It is worth pointing out that here the term “expert” denotes a software module entrusted with the task of predicting protein secondary
structure in combination with other experts of the same kind. The remainder of this paper is organized as follows. In Section 2, some relevant work is briefly recalled. Section 3 introduces the architecture of the system that has been d
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