Comput Biol Chem - Protein folding simulations of 2D HP model by the genetic algorithm based on optimal secondary structures.

Tópicos

{ sequenc(1873) structur(1644) protein(1328) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
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Resumo

In this paper, based on the evolutionary Monte Carlo (EMC) algorithm, we have made four points of ameliorations and propose a so-called genetic algorithm based on optimal secondary structure (GAOSS) method to predict efficiently the protein folding conformations in the two-dimensional hydrophobic-hydrophilic (2D HP) model. Nine benchmarks are tested to verify the effectiveness of the proposed approach and the results show that for the listed benchmarks GAOSS can find the best solutions so far. It means that reasonable, effective and compact secondary structures (SSs) can avoid blind searches and can reduce time consuming significantly. On the other hand, as examples, we discuss the diversity of protein GSC for the 24-mer and 85-mer sequences. Several GSCs have been found by GAOSS and some of the conformations are quite different from each other. It would be useful for the designing of protein molecules. GAOSS would be an efficient tool for the protein structure predictions (PSP).

Resumo Limpo

paper base evolutionari mont carlo emc algorithm made four point amelior propos socal genet algorithm base optim secondari structur gaoss method predict effici protein fold conform twodimension hydrophobichydrophil d hp model nine benchmark test verifi effect propos approach result show list benchmark gaoss can find best solut far mean reason effect compact secondari structur sss can avoid blind search can reduc time consum signific hand exampl discuss divers protein gsc mer mer sequenc sever gscs found gaoss conform quit differ use design protein molecul gaoss effici tool protein structur predict psp

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