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.

Protein Structure Prediction

“…GAs have a long and successful history in computational biology and recent work by Arunachalam et al (2006) and Armano et al (2005) has used GAs to good effect in structure prediction. In Arunachalam et al (2006), the prediction of several all α proteins is described using mutually orthogonal Latin squares and GAs.…”

“…Arunachalam's GA is of particular interest as a supplementary method that aims to aid convergence on local minimum is described. Armano et al (2005) introduces a hybrid ANN -GA approach to secondary structure prediction. While the paper does not highlight a ground breaking discovery, it does describe a method that is comparable to MUPRED (Bondugula and Xu, 2007), a method that is discussed in the next section.…”

Analysing protein–protein interaction networks of human liver cancer cell lines with diverse metastasis potential

“…Proteomic analysis is a failure without bioinformatic tools. At present common bioinformatic tools in proteomic analysis include sequence and structure analyzing software package, molecule biology software, network informatics resource, Protein-protein interaction network software, etc (Englbrecht et al 2005; Thorgeirsson et al 2006;Chagoyen et al 2006;Armano et al 2005;Fung et al 2005). Protein-protein interaction network can visually display interactions between proteins, easily making out hub proteins (Deeds et al 2006;Uetz et al 2006).…”

Automated QSPR through Competitive Workflow

“…The use of autonomous collaborating software agents to handle the complexity and dynamics required by some workflow management applications is an area of active research [6][7][8] and autonomous software agents have been applied to distributed learning [9], tool-integration [10], bioinformatics workflows [11] and systems biology modelling [12].…”