Preditor híbrido de estruturas terciárias de proteínas
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Data
2023-08-10
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Universidade Federal de Goiás
Resumo
Proteins are organic molecules composed of chains of amino acids and play a
variety of essential biological functions in the body. The native structure of a protein is
the result of the folding process of its amino acids, with their spatial orientation primarily
determined by two dihedral angles (φ, ψ). This work proposes a new hybrid method for
predicting the tertiary structures of proteins called hyPROT, combining techniques of
Multi-objective Evolutionary Algorithm optimization (MOEA), Molecular Dynamics, and
Recurrent Neural Networks (RNNs). The proposed approach investigates the evolutionary
profile of dihedral angles (φ, ψ) obtained by different MOEAs during the minimization
process of the objective function by dominance and energy minimization by molecular
dynamics. This proposal is unprecedented in the protein prediction literature. The premise
under investigation is that the evolutionary profile of dihedrals may be concealing relevant
patterns about folding mechanisms. To analyze the evolutionary profile of angles (φ, ψ),
RNNs were used to abstract and generalize the specific biases of each MOEA. The selected
MOEAs were NSGAII, BRKGA, and GDE3, and the objective function investigated
combines the potential energy from non-covalent interactions and the solvation energy.
The results obtained show that the hyPROT was able to reduce the RMSD value of
the best prediction generated by the MOEAs individually by at least 33%. Predicting
new series for dihedral angles allowed for the formation of histograms, indicating the
formation of a possible statistical ensemble responsible for the distribution of dihedrals
(φ, ψ) during the folding process
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Citação
ALMEIDA, A. B. Preditor híbrido de estruturas terciárias de proteínas. 2023. 148 f. Tese (Doutorado em Ciência da Computação) - Instituto de Informática, Universidade Federal de Goiás, Goiânia, 2023.