TY - JOUR
T1 - Adoption of an improved PSO to explore a compound multi-objective energy function in protein structure prediction
AU - Song, Shuangbao
AU - Ji, Junkai
AU - Chen, Xingqian
AU - Gao, Shangce
AU - Tang, Zheng
AU - Todo, Yuki
N1 - Publisher Copyright:
© 2018
PY - 2018/11
Y1 - 2018/11
N2 - The protein structure prediction (PSP) problem, i.e., predicting the three-dimensional structure of a protein from its sequence, remains challenging in computational biology. The inaccuracy of existing protein energy functions and the huge conformation search space make the problem difficult to solve. In this study, the PSP problem is modeled as a multi-objective optimization problem. A physics-based energy function and a knowledge-based energy function are combined to construct the three-objective energy function. An improved multi-objective particle swarm optimization coupled with two archives is employed to execute the conformation space search. In addition, a mechanism based on Pareto non-dominated sorting is designed to properly address the slightly worse solutions. Finally, the experimental results demonstrate the effectiveness of the proposed approach. A new perspective for solving the PSP problem by means of multi-objective optimization is given in this paper.
AB - The protein structure prediction (PSP) problem, i.e., predicting the three-dimensional structure of a protein from its sequence, remains challenging in computational biology. The inaccuracy of existing protein energy functions and the huge conformation search space make the problem difficult to solve. In this study, the PSP problem is modeled as a multi-objective optimization problem. A physics-based energy function and a knowledge-based energy function are combined to construct the three-objective energy function. An improved multi-objective particle swarm optimization coupled with two archives is employed to execute the conformation space search. In addition, a mechanism based on Pareto non-dominated sorting is designed to properly address the slightly worse solutions. Finally, the experimental results demonstrate the effectiveness of the proposed approach. A new perspective for solving the PSP problem by means of multi-objective optimization is given in this paper.
KW - Knowledge-based energy function
KW - Multi-objective optimization problem
KW - Non-dominated sorting
KW - Particle swarm optimization
KW - Protein structure prediction
UR - http://www.scopus.com/inward/record.url?scp=85054381311&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2018.07.042
DO - 10.1016/j.asoc.2018.07.042
M3 - 学術論文
AN - SCOPUS:85054381311
SN - 1568-4946
VL - 72
SP - 539
EP - 551
JO - Applied Soft Computing
JF - Applied Soft Computing
ER -