Differential Evolution for Protein Structure Prediction Using the HP Model

被引:0
|
作者
Santos, J. [1 ]
Dieguez, M. [1 ]
机构
[1] Univ A Coruna, Dept Comp Sci, La Coruna, Spain
来源
FOUNDATIONS ON NATURAL AND ARTIFICIAL COMPUTATION: 4TH INTERNATIONAL WORK-CONFERENCE ON THE INTERPLAY BETWEEN NATURAL AND ARTIFICIAL COMPUTATION, IWINAC 2011, PART I | 2011年 / 6686卷
关键词
GENETIC ALGORITHM; OPTIMIZATION; PRINCIPLES; 2D;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We used Differential Evolution (DE) for the problem of protein structure prediction. We employed the HP model to represent the folding conformations of a protein in a lattice. In this model the nature of amino acids is reduced considering only two types: hydrophobic residues (H) and polar residues (P), which is based on the recognition that hydrophobic interactions are a dominant force in protein folding. Given a primary sequence of amino acids, the problem is to search for the folding structure in the lattice that minimizes an energy potential. This energy reflects the fact that the hydrophobic amino acids have a propensity to form a hydrophobic core. The complexity of the problem has been shown to be NP-hard, with minimal progress achieved in this category of ab initio folding. We combined DE with methods to transform illegal protein conformations to feasible ones, showing the capabilities of the hybridized DE with respect to previous works.
引用
收藏
页码:323 / 333
页数:11
相关论文
共 50 条
  • [31] Twin Removal in Genetic Algorithms for Protein Structure Prediction Using Low-Resolution Model
    Hoque, Md Tamjidul
    Chetty, Madhu
    Lewis, Andrew
    Sattar, Abdul
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2011, 8 (01) : 234 - 245
  • [32] Protein Structure Prediction Using Chemical Reaction Optimization
    Chatterjee, Sajib
    Smrity, Resheta Ahmed
    Islam, Md. Rafiqul
    PROCEEDINGS OF THE 2016 19TH INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY (ICCIT), 2016, : 321 - 326
  • [33] A Differential Evolution-Oriented Pruning Neural Network Model for Bankruptcy Prediction
    Tang, Yajiao
    Ji, Junkai
    Zhu, Yulin
    Gao, Shangce
    Tang, Zheng
    Todo, Yuki
    COMPLEXITY, 2019, 2019
  • [34] Emergent Protein Folding Modeled with Evolved Neural Cellular Automata Using the 3D HP Model
    Santos, Jose
    Villot, Pablo
    Dieguez, Martin
    JOURNAL OF COMPUTATIONAL BIOLOGY, 2014, 21 (11) : 823 - 845
  • [35] Estimation of Nonlinear Muskingum Model Parameter Using Differential Evolution
    Xu, Dong-Mei
    Qiu, Lin
    Chen, Shou-Yu
    JOURNAL OF HYDROLOGIC ENGINEERING, 2012, 17 (02) : 348 - 353
  • [36] A Novel LUTI Model Calibration Using Differential Evolution Algorithm
    Skandary, Ahmad Farhad
    Dadashzadeh, Nima
    Zura, Marijan
    IEEE ACCESS, 2021, 9 : 167004 - 167014
  • [37] Water quality prediction using support vector machine with differential evolution optimization
    Wang, X. (wang_xuan0001@163.com), 1600, ICIC Express Letters Office (05):
  • [38] Model Simplification of Linear Discrete Systems Using Differential Evolution
    Wang, Xiaodong
    Wang, Ke
    Wang, Jinshan
    Jiang, Minlan
    Xu, Xiuling
    Feng, Genliang
    Ye, Meiying
    PROGRESS IN MEASUREMENT AND TESTING, PTS 1 AND 2, 2010, 108-111 : 932 - +
  • [39] Protein-ligand docking using differential evolution with an adaptive mechanism
    Song, Shuangbao
    Chen, Xingqian
    Zhang, Yanxin
    Tang, Zheng
    Todo, Yuki
    KNOWLEDGE-BASED SYSTEMS, 2021, 231
  • [40] Optimization of protein folding using chemical reaction optimization in HP cubic lattice model
    Islam, Md Rafiqul
    Smrity, Resheta Ahmed
    Chatterjee, Sajib
    Mahmud, Md Riaz
    NEURAL COMPUTING & APPLICATIONS, 2020, 32 (08) : 3117 - 3134