Adoption of an improved PSO to explore a compound multi-objective energy function in protein structure prediction

Shuangbao Song, Junkai Ji, Xingqian Chen, Shangce Gao, Zheng Tang, Yuki Todo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)539-551
Number of pages13
JournalApplied Soft Computing
Volume72
DOIs
StatePublished - 2018/11

Keywords

  • Knowledge-based energy function
  • Multi-objective optimization problem
  • Non-dominated sorting
  • Particle swarm optimization
  • Protein structure prediction

ASJC Scopus subject areas

  • Software

Fingerprint

Dive into the research topics of 'Adoption of an improved PSO to explore a compound multi-objective energy function in protein structure prediction'. Together they form a unique fingerprint.

Cite this