Perspectives in protein-fold recognition

被引:51
|
作者
Torda, AE
机构
[1] Research School of Chemistry, Australian National University, Canberra
关键词
D O I
10.1016/S0959-440X(97)80026-7
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Fold recognition force fields based on statistics from native structures have become commonplace. New, nonphysical force fields based on optimizing parameters rather than reflecting Boltzmann statistics may offer improvement in force-field performance for threading and other applications. Improvements in sequence-to-structure alignments will also be essential for improved fold recognition.
引用
收藏
页码:200 / 205
页数:6
相关论文
共 50 条
  • [41] Enhanced protein fold recognition using a structural alphabet
    Deschavanne, Patrick
    Tuffery, Pierre
    PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, 2009, 76 (01) : 129 - 137
  • [42] Protein fold recognition based on functional domain composition
    Wang, Qin
    Yan, Jinli
    Li, Xiaoqin
    COMPUTATIONAL BIOLOGY AND CHEMISTRY, 2014, 48 : 71 - 76
  • [43] Protein fold recognition and dynamics in the space of contact maps
    Mirny, L
    Domany, E
    PROTEINS-STRUCTURE FUNCTION AND GENETICS, 1996, 26 (04): : 391 - 410
  • [44] Competitive assessment of protein fold recognition and alignment accuracy
    Levitt, M
    PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, 1997, : 92 - 104
  • [45] Protein folding and fold recognition for square lattice models
    Crippen, GM
    FOLDING & DESIGN, 1997, 2 (04): : S58 - S61
  • [46] Boosting methods for Protein Fold Recognition: An Empirical Comparison
    Krishnaraj, Yazhene
    Reddy, Chandan K.
    2008 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, PROCEEDINGS, 2008, : 393 - 396
  • [47] An improved protein fold recognition with support vector machines
    Chmielnicki, Wieslaw
    Roterman-Konieczna, Irena
    Stapor, Katarzyna
    EXPERT SYSTEMS, 2012, 29 (02) : 200 - 211
  • [48] Protein fold recognition model based on cubic lattice
    Peyravi, Farzad
    Latif, Alimohammad
    Moshtaghioun, Seyed Mohammad
    INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, 2019, 22 (01) : 75 - 90
  • [49] Improving Protein Fold Recognition by Deep Learning Networks
    Jo, Taeho
    Hou, Jie
    Eickholt, Jesse
    Cheng, Jianlin
    SCIENTIFIC REPORTS, 2015, 5
  • [50] A novel hierarchical ensemble classifier for protein fold recognition
    Guo, Xia
    Gao, Xieping
    PROTEIN ENGINEERING DESIGN & SELECTION, 2008, 21 (11): : 659 - 664