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Protein structure comparison via contact map alignment

Fonte: UNIVERSIDADE FEDERAL DE LAVRAS; PPBV - Programa de Pós-graduação em Biotecnologia Vegetal; UFLA; BRASIL Publicador: UNIVERSIDADE FEDERAL DE LAVRAS; PPBV - Programa de Pós-graduação em Biotecnologia Vegetal; UFLA; BRASIL
Tipo: Dissertação
PT_BR
Relevância na Pesquisa
26.16%
Dissertação apresentada à Universidade Federal de Lavras, como parte das exigências do Programa de Pós-Graduação em Biotecnologia Vegetal, área de concentração em Biotecnologia Vegetal, para a obtenção do título de Mestre.

Protein structure comparison via contact map alignment

Fonte: UNIVERSIDADE FEDERAL DE LAVRAS; PPBV - Programa de Pós-graduação em Biotecnologia Vegetal; UFLA; BRASIL Publicador: UNIVERSIDADE FEDERAL DE LAVRAS; PPBV - Programa de Pós-graduação em Biotecnologia Vegetal; UFLA; BRASIL
Tipo: Tese de Doutorado
PT_BR
Relevância na Pesquisa
26.16%

The Local Searcher as a Supplier of Building Blocks in Self-generating Memetic Algorithms

Krasnogor, Natalio; Gustafson, Steven
Fonte: Universidade de Tubinga Publicador: Universidade de Tubinga
Tipo: Teil einer Konferenzveröffentlichung
EN
Relevância na Pesquisa
26.16%
In this paper we implement a Self-Generating Memetic Algorithm for the Maximum Contact Overlap Problem (MAX-CMO). We demonstrate how the optimization of solutions can be done simultaneously with the discovering of useful local search strategies. In turn, the evolved local searchers act as suppliers of building blocks for the evolutionary algorithm.

A simple and fast heuristic for protein structure comparison

Pelta, David Alejandro; Gonz??lez Gonz??lez, Juan Ram??n; Moreno Vega, Marcos
Fonte: Biomed Central Publicador: Biomed Central
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
26.96%
Background Protein structure comparison is a key problem in bioinformatics. There exist several methods for doing protein comparison, being the solution of the Maximum Contact Map Overlap problem (MAX-CMO) one of the alternatives available. Although this problem may be solved using exact algorithms, researchers require approximate algorithms that obtain good quality solutions using less computational resources than the formers. Results We propose a variable neighborhood search metaheuristic for solving MAX-CMO. We analyze this strategy in two aspects: 1) from an optimization point of view the strategy is tested on two different datasets, obtaining an error of 3.5%(over 2702 pairs) and 1.7% (over 161 pairs) with respect to optimal values; thus leading to high accurate solutions in a simpler and less expensive way than exact algorithms; 2) in terms of protein structure classification, we conduct experiments on three datasets and show that is feasible to detect structural similarities at SCOP's family and CATH's architecture levels using normalized overlap values. Some limitations and the role of normalization are outlined for doing classification at SCOP's fold level. Conclusion We designed, implemented and tested.a new tool for solving MAX-CMO...