TY - JOUR
T1 - Incorporation of Solvent Effect into Multi-Objective Evolutionary Algorithm for Improved Protein Structure Prediction
AU - Gao, Shangce
AU - Song, Shuangbao
AU - Cheng, Jiujun
AU - Todo, Yuki
AU - Zhou, Meng Chu
N1 - Funding Information:
This research was partially supported by the National Natural Science Foundation of China (Grant No. 61472284), and JSPS KAKENHI Grant Number JP17K12751, JP15K00332.
Publisher Copyright:
© 2004-2012 IEEE.
PY - 2018/7/1
Y1 - 2018/7/1
N2 - 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.
AB - 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.
KW - Protein structure prediction
KW - evolutionary algorithm
KW - multi-objective optimization
KW - solvent-accessible surface area
UR - http://www.scopus.com/inward/record.url?scp=85029187836&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029187836&partnerID=8YFLogxK
U2 - 10.1109/TCBB.2017.2705094
DO - 10.1109/TCBB.2017.2705094
M3 - Article
C2 - 28534784
AN - SCOPUS:85029187836
SN - 1545-5963
VL - 15
SP - 1365
EP - 1378
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics
IS - 4
M1 - 7930531
ER -