Amino acid contact energy networks impact protein structure and evolution

被引:16
|
作者
Yan, Wenying [1 ]
Sun, Maomin [1 ,2 ]
Hu, Guang [1 ]
Zhou, Jianhong [1 ]
Zhang, Wenyu [1 ]
Chen, Jiajia [1 ,3 ]
Chen, Biao [1 ]
Shen, Bairong [1 ]
机构
[1] Soochow Univ, Ctr Syst Biol, Suzhou 215006, Jiangsu, Peoples R China
[2] Soochow Univ, Sch Med, Lab Anim Res Ctr, Suzhou 215006, Jiangsu, Peoples R China
[3] Suzhou Univ Sci & Technol, Dept Chem & Biol Engn, Suzhou 215011, Jiangsu, Peoples R China
基金
中国国家自然科学基金; 国家高技术研究发展计划(863计划); 高等学校博士学科点专项科研基金;
关键词
Clustering coefficient; Long-range link percentage; Network diameter; Protein evolutionary rate; Protein secondary structure density; GENE-EXPRESSION; REGRESSION; DYNAMICS; RATES;
D O I
10.1016/j.jtbi.2014.03.032
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
One of the most challenging tasks in structural proteomics is to understand the relationship between protein structure, biological function, and evolution. An understanding of amino acid networks based on protein topology has an important role in the study of this relationship; however, the relationship between network parameters underlying protein topology with structural properties or evolutionary rate is still unknown. To investigate this further, we modeled the three dimensional structure of proteins as amino acid contact energy networks (AACENs) with nodes represented as amino acid residues and edges established according to environment-dependent residue-residue contact energies. Five other types of networks were also constructed to investigate their topological parameters and compare their effect on protein structure and evolution: (1) a random contact network (RCN), (2) a rewiring network with the same degree of distribution as AACEN (RNDD), (3) long-range contact energy networks with and without the backbone connectivity (LCEN_BBs and LCENs), and (4) short range contact energy networks (SCENs). The results indicated that the long-range link percentage and the network clustering coefficient showed a significantly positive and negative correlation, respectively, with protein secondary structure density. In addition, the long-range link percentage and network diameter had a significantly positive and negative correlation, respectively, with evolutionary rate. According to our knowledge, this is the first study to identify the potential role of long-range links and network diameter in protein evolution. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:95 / 104
页数:10
相关论文
共 50 条
  • [1] Impact of Single Amino Acid Substitution Upon Protein Structure
    Livingstone, Mark
    Folkman, Lukas
    Stantic, Bela
    BIOINFORMATICS 2014: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON BIOINFORMATICS MODELS, METHODS AND ALGORITHMS, 2014, : 123 - 129
  • [2] Evolution of Amino Acid Properties in the Context of Protein Secondary Structure Prediction
    Santos, Jose
    Rivas, Hector
    2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021), 2021, : 426 - 433
  • [3] Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images
    Golkov, Vladimir
    Skwark, Marcin J.
    Golkov, Antonij
    Dosovitskiy, Alexey
    Brox, Thomas
    Meiler, Jens
    Cremers, Daniel
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016), 2016, 29
  • [4] Predict impact of single amino acid change upon protein structure
    Christian Schaefer
    Burkhard Rost
    BMC Genomics, 13
  • [5] Predict impact of single amino acid change upon protein structure
    Schaefer, Christian
    Rost, Burkhard
    BMC GENOMICS, 2012, 13
  • [6] Disease evolution on networks: the role of contact structure
    Read, JM
    Keeling, MJ
    PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, 2003, 270 (1516) : 699 - 708
  • [7] Predicting absolute contact numbers of native protein structure from amino acid sequence
    Kinjo, AR
    Horimoto, K
    Nishikawa, K
    PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, 2005, 58 (01) : 158 - 165
  • [8] Bursts of amino acid replacements in protein evolution
    Stolyarova, Anastasia, V
    Bazykin, Georgii A.
    Neretina, Tatyana, V
    Kondrashov, Alexey S.
    ROYAL SOCIETY OPEN SCIENCE, 2019, 6 (03):
  • [9] Structure of social contact networks and their impact on epidemics
    Eubank, Stephen
    Kumar, V. S. Anil
    Marathe, Madhav V.
    Srinivasan, Aravind
    Wang, Nan
    DISCRETE METHODS IN EPIDEMIOLOGY, 2006, 70 : 181 - +
  • [10] A thermodynamic model of protein structure evolution explains empirical amino acid substitution matrices
    Norn, Christoffer
    Andre, Ingemar
    Theobald, Douglas L.
    PROTEIN SCIENCE, 2021, 30 (10) : 2057 - 2068