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Rooted Tree Optimization Algorithm for Protein Folding Prediction

Cheng-Hong Yang 1, Yu-Shiun Lin 1, Li-Yeh Chuang 2, and Hsueh-Wei Chang 3
1. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
2. Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan
3. Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, Taiwan

Abstract—Protein function depends on structural folding from the amino acid sequence. Correctly predicting the amino acid sequence is thus helpful for evaluating the protein structure and function. In 1995, a hydrophobic-polar model (HP model) was proposed to simplify the folding process. This model drastically simplifies the real folding space into a lattice and combines an optimization algorithm to predict the protein structure. Many optimization algorithms have been implemented with the HP model for protein structure prediction, but accuracy and speed still need to be improved. This study proposes a fairly new algorithm, namely the rooted tree optimization (RTO), to improve on current algorithm performance. RTO provide three ways to find optimal solution and with the HP model for protein structure prediction. The local search is designed to add to each iteration of RTO to improve performance of the triangular lattice model.

Index Terms—hydrophobic-polar model, rooted tree optimization, protein structure prediction

Cite: Cheng-Hong Yang, Yu-Shiun Lin, Li-Yeh Chuang, and Hsueh-Wei Chang, "Rooted Tree Optimization Algorithm for Protein Folding Prediction," Journal of Life Sciences and Technologies, vol. 4, no. 2, pp. 89-93, December 2016. doi: 10.18178/jolst.4.2.89-93
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