Secondary Structure Prediction and Folding of Globular Protein: Refolding of Ferredoxin

被引:0
|
作者
Yukio Kobayashi
Nobuhiko Saitô
机构
[1] Soka University,Department of Information Systems Science, Faculty of Engineering
[2] Waseda University,Department of Applied Physics, School of Science and Engineering
来源
关键词
Protein folding; secondary structure prediction; ferredoxin; island model; hydrophobic interaction;
D O I
暂无
中图分类号
学科分类号
摘要
The physicochemical mechanism of protein folding has been elucidated by the island model, describing a growth type of folding. The folding pathway is closely related with nucleation on the polypeptide chain and thus the formation of small local structures or secondary structures at the earliest stage of folding is essential to all following steps. The island model is applicable to any protein, but a high precision of secondary structure prediction is indispensable to folding simulation. The secondary structures formed at the earliest stage of folding are supposed to be of standard form, but they are usually deformed during the folding process, especially at the last stage, although the degree of deformation is different for each protein. Ferredoxin is an example of a protein having this property. According to X-ray investigation (1FDX), ferredoxin is not supposed to have secondary structures. However, if we assumed that in ferredoxin all the residues are in a coil state, we could not attain the correct structure similar to the native one. Further, we found that some parts of the chain are not flexible, suggesting the presence of secondary structures, in agreement with the recent PDB data (1DUR). Assuming standard secondary structures (α-helices and β-strands) at the nonflexible parts at the early stage of folding, and deforming these at the final stage, a structure similar to the native one was obtained. Another peculiarity of ferredoxin is the absence of disulfide bonds, in spite of its having eight cysteines. The reason cysteines do not form disulfide bonds became clear by applying the lampshade criterion, but more importantly, the two groups of cysteines are ready to make iron complexes, respectively, at a rather later stage of folding. The reason for poor prediction accuracy of secondary structure with conventional methods is discussed.
引用
收藏
页码:647 / 654
页数:7
相关论文
共 50 条
  • [31] Protein folding: From the Levinthal paradox to structure prediction
    Honig, B
    JOURNAL OF MOLECULAR BIOLOGY, 1999, 293 (02) : 283 - 293
  • [32] PROTEIN FOLDING - STRUCTURE PREDICTION AND STATISTICAL-MECHANICS
    FUKUGITA, M
    NUCLEAR PHYSICS B, 1993, : 159 - 167
  • [33] Folding funnels: The key to robust protein structure prediction
    Hardin, C
    Eastwood, MP
    Prentiss, M
    Luthey-Schulten, Z
    Wolynes, PG
    JOURNAL OF COMPUTATIONAL CHEMISTRY, 2002, 23 (01) : 138 - 146
  • [34] PERFECT TEMPERATURE FOR PROTEIN-STRUCTURE PREDICTION AND FOLDING
    FINKELSTEIN, AV
    GUTIN, AM
    BADRETDINOV, AY
    PROTEINS-STRUCTURE FUNCTION AND GENETICS, 1995, 23 (02): : 151 - 162
  • [35] PROTEIN SECONDARY STRUCTURE - ANALYSIS AND PREDICTION
    HIDER, RC
    HODGES, SJ
    BIOCHEMICAL EDUCATION, 1984, 12 (01): : 9 - 18
  • [36] Protein Secondary Structure Prediction with SPARROW
    Bettella, Francesco
    Rasinski, Dawid
    Knapp, Ernst Walter
    JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2012, 52 (02) : 545 - 556
  • [37] Parallelized protein secondary structure prediction
    Qi, YT
    Lin, F
    PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2004, : 2074 - 2077
  • [38] SECONDARY STRUCTURE PREDICTION AND PROTEIN DESIGN
    GARNIER, J
    LEVIN, JM
    GIBRAT, JF
    BIOU, V
    BIOCHEMICAL SOCIETY SYMPOSIA, 1990, (57) : 11 - 24
  • [39] Prediction of protein secondary structure content
    Liu, WM
    Chou, KC
    PROTEIN ENGINEERING, 1999, 12 (12): : 1041 - 1050
  • [40] Structure-based protein folding type classification and folding rate prediction
    Manavalan, Balachandran
    Joung, Insuk
    Kuwajima, Kunihiro
    Lee, Jooyoung
    PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, 2015, : 1759 - 1761