Incorporation of Solvent Effect into Multi-Objective Evolutionary Algorithm for Improved Protein Structure Prediction

Shangce Gao, Shuangbao Song, Jiujun Cheng, Yuki Todo, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

Abstract

The problem of predicting the three-dimensional (3-D) structure of a protein from its one-dimensional sequence has been called the 'holy grail of molecular biology', and it has become an important part of structural genomics projects. Despite the rapid developments in computer technology and computational intelligence, it remains challenging and fascinating. In this paper, to solve it we propose a multi-objective evolutionary algorithm. We decompose the protein energy function Chemistry at HARvard Macromolecular Mechanics force fields into bond and non-bond energies as the first and second objectives. Considering the effect of solvent, we innovatively adopt a solvent-accessible surface area as the third objective. We use 66 benchmark proteins to verify the proposed method and obtain better or competitive results in comparison with the existing methods. The results suggest the necessity to incorporate the effect of solvent into a multi-objective evolutionary algorithm to improve protein structure prediction in terms of accuracy and efficiency.

Original languageEnglish (US)
Article number7930531
Pages (from-to)1365-1378
Number of pages14
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume15
Issue number4
DOIs
StatePublished - Jul 1 2018

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Genetics
  • Applied Mathematics

Keywords

  • Protein structure prediction
  • evolutionary algorithm
  • multi-objective optimization
  • solvent-accessible surface area

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