title: A hybrid genetic-neural system for predicting protein secondary structure.
abstract: BACKGROUND
  Due to the strict relation between protein function and structure, the prediction of protein 3D-structure has become one of the most important tasks in bioinformatics and proteomics. In fact, notwithstanding the increase of experimental data on protein structures available in public databases, the gap between known sequences and known tertiary structures is constantly increasing. The need for automatic methods has brought the development of several prediction and modelling tools, but a general methodology able to solve the problem has not yet been devised, and most methodologies concentrate on the simplified task of predicting secondary structure.
  
  
  RESULTS
  In this paper we concentrate on the problem of predicting secondary structures by adopting a technology based on multiple experts. The system performs an overall processing based on two main steps: first, a "sequence-to-structure" prediction is enforced by resorting to a population of hybrid (genetic-neural) experts, and then a "structure-to-structure" prediction is performed by resorting to an artificial neural network. Experiments, performed on sequences taken from well-known protein databases, allowed to reach an accuracy of about 76%, which is comparable to those obtained by state-of-the-art predictors.
  
  
  CONCLUSIONS
  The adoption of a hybrid technique, which encompasses genetic and neural technologies, has demonstrated to be a promising approach in the task of protein secondary structure prediction.
authors.affiliation: Dept. of Electrical and Electronic Engineering, University of Cagliari Piazza d'Armi, I-09123 Cagliari, Italy. armano@diee.unica.it
authors.forename: Giuliano
authors.initials: G
authors.lastname: Armano
authors.forename: Gianmaria
authors.initials: G
authors.lastname: Mancosu
authors.forename: Luciano
authors.initials: L
authors.lastname: Milanesi
authors.forename: Alessandro
authors.initials: A
authors.lastname: Orro
authors.forename: Massimiliano
authors.initials: M
authors.lastname: Saba
authors.forename: Eloisa
authors.initials: E
authors.lastname: Vargiu
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030562:Databases, Protein; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D011487:Protein Conformation; D017510:Protein Folding; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D011506:Proteins