Cheng-Jian Lin

Chuan-Kang Ting

Shih-Chieh Su

Su, Shih-Chieh

English

PubMed

Abstract
          
            Background
            Proteins play fundamental and crucial roles in nearly all biological processes, such as, enzymatic catalysis, signaling transduction, DNA and RNA synthesis, and embryonic development. It has been a long-standing goal in molecular biology to predict the tertiary structure of a protein from its primary amino acid sequence. From visual comparison, it was found that a 2D triangular lattice model can give a better structure modeling and prediction for proteins with short primary amino acid sequences.
          
          
            Methods
            This paper proposes a hybrid of hill-climbing and genetic algorithm (HHGA) based on elite-based reproduction strategy for protein structure prediction on the 2D triangular lattice.
          
          
            Results
            The simulation results show that the proposed HHGA can successfully deal with the protein structure prediction problems. Specifically, HHGA significantly outperforms conventional genetic algorithms and is comparable to the state-of-the-art method in terms of free energy.
          
          
            Conclusions
            Thanks to the enhancement of local search on the global search, the proposed HHGA achieves promising results on the 2D triangular protein structure prediction problem. The satisfactory simulation results demonstrate the effectiveness of the proposed HHGA and the utility of the 2D triangular lattice model for protein structure prediction.
          </jats:sec

Crossref

Proteome Science

An effective hybrid of hill climbing and genetic algorithm for 2D triangular protein structure prediction

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An effective hybrid of hill climbing and genetic algorithm for 2D triangular protein structure prediction

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