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
国家高技术研究发展计划(863计划); 中国国家自然科学基金; 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] 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
  • [2] Protein Evolution via Amino Acid and Codon Elimination
    Goltermann, Lise
    Larsen, Marie Sofie Yoo
    Banerjee, Rajat
    Joerger, Andreas C.
    Ibba, Michael
    Bentin, Thomas
    PLOS ONE, 2010, 5 (04):
  • [3] The construction of an amino acid network for understanding protein structure and function
    Yan, Wenying
    Zhou, Jianhong
    Sun, Maomin
    Chen, Jiajia
    Hu, Guang
    Shen, Bairong
    AMINO ACIDS, 2014, 46 (06) : 1419 - 1439
  • [4] On the Natural Structure of Amino Acid Patterns in Families of Protein Sequences
    Turjanski, Pablo
    Ferreiro, Diego U.
    JOURNAL OF PHYSICAL CHEMISTRY B, 2018, 122 (49) : 11295 - 11301
  • [5] Effects of contact structure on the transient evolution of HIV virulence
    Park, Sang Woo
    Bolker, Benjamin M.
    PLOS COMPUTATIONAL BIOLOGY, 2017, 13 (03)
  • [6] The interface of protein structure, protein biophysics, and molecular evolution
    Liberles, David A.
    Teichmann, Sarah A.
    Bahar, Ivet
    Bastolla, Ugo
    Bloom, Jesse
    Bornberg-Bauer, Erich
    Colwell, Lucy J.
    de Koning, A. P. Jason
    Dokholyan, Nikolay V.
    Echave, Julian
    Elofsson, Arne
    Gerloff, Dietlind L.
    Goldstein, Richard A.
    Grahnen, Johan A.
    Holder, Mark T.
    Lakner, Clemens
    Lartillot, Nicholas
    Lovell, Simon C.
    Naylor, Gavin
    Perica, Tina
    Pollock, David D.
    Pupko, Tal
    Regan, Lynne
    Roger, Andrew
    Rubinstein, Nimrod
    Shakhnovich, Eugene
    Sjoelander, Kimmen
    Sunyaev, Shamil
    Teufel, Ashley I.
    Thorne, Jeffrey L.
    Thornton, Joseph W.
    Weinreich, Daniel M.
    Whelan, Simon
    PROTEIN SCIENCE, 2012, 21 (06) : 769 - 785
  • [7] Clustered tree regression to learn protein energy change with mutated amino acid
    Tu, Hongwei
    Han, Yanqiang
    Wang, Zhilong
    Li, Jinjin
    BRIEFINGS IN BIOINFORMATICS, 2022, 23 (06)
  • [8] Study on The Characters of Different Types of Amino-acid Networks and Their Relations With Protein Folding
    Yan Li-Cheng
    Su Ji-Guo
    Chen Wei-Zu
    Wang Cun-Xin
    PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS, 2010, 37 (07) : 762 - 768
  • [9] The structure and evolution of story networks
    Karsdorp, Folgert
    van den Bosch, Antal
    ROYAL SOCIETY OPEN SCIENCE, 2016, 3 (06):
  • [10] Observing Vibrational Energy Flow in a Protein with the Spatial Resolution of a Single Amino Acid Residue
    Fujii, Naoki
    Mizuno, Misao
    Ishikawa, Haruto
    Mizutani, Yasuhisa
    JOURNAL OF PHYSICAL CHEMISTRY LETTERS, 2014, 5 (18): : 3269 - 3273