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An exaple of template based protein structure modeling by global optimization

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Wybrane pełne teksty z tego czasopisma
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Języki publikacji
EN
Abstrakty
EN
CASP (Critical Assessment of protein Structure Prediction) is a community-wide experiment for protein structure prediction taking place every two years since 1994. In CASP 11 held in 2014, according to the official CASP 11 assessment, our method named `nns' was ranked as the second best server method based on models ranked as first out of 81 targets. In `nns', we applied the powerful global optimization method of conformational space annealing to three stages of optimization, including multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain remodeling. For the fold recognition, a new alignment method called CRF align was used. The good performance of the nns server method is attributed to the successful fold recognition carried out by combined methods including CRF align, and the current modeling formulation incorporating accurate structural aspects collected from multiple templates. In this article, we provide a successful example of `nns' predictions for T0776, for which all details of intermediate modeling data are provided.
Rocznik
Strony
341--352
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
autor
  • Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul 130-722, Korea
  • Center for Advanced Computation, Korea Institute for Advanced Study, Seoul 130-722, Korea
autor
  • Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul 130-722, Korea
  • School of Computational Sciences, Korea Institute for Advanced Study, Seoul 130-722, Korea
autor
  • Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul 130-722, Korea
  • Center for Advanced Computation, Korea Institute for Advanced Study, Seoul 130-722, Korea
  • School of Computational Sciences, Korea Institute for Advanced Study, Seoul 130-722, Korea
Bibliografia
  • [1] Joo K, Lee J, Lee S, Seo J H, Lee S J and Lee J 2007 Prot. Struct. Funct. Bioinf. 69 (S8) 83
  • [2] Krieger E et al. 2009 Prot. Struct. Funct. Bioinf. 77 (S9) 114
  • [3] Joo K, Lee J, Sim S, Lee S Young, Lee K, Heo S, Lee I-H, Lee S Jong and Lee J 2014 Prot. Struct. Funct. Bioinf. 82 188
  • [4] Lee J, Scheraga H A and Rackovsky S 1997 J. Comput. Chem. 18 (9) 1222
  • [5] Lee J, Scheraga H A and Rackovsky S 1998 Biopolymers 46 103
  • [6] Lee J, Liwo A, Ripoll D R, Pillardy J and Scheraga H A 1999 Prot. Struct. Funct. Genet. S3 204
  • [7] Lee J, Liwo A, Ripoll D R, Pillardy J, Gibson K D, Saunders J A and Scheraga H A 2000 J. Comput. Chem. 77 (1) 90
  • [8] Lee J and Scheraga H A 1999 International Journal of Quantum Chemistry 75 (3) 255
  • [9] Lee J, Lee I-H and Lee J 2003 Physical Review Letters 91 (8) 80201
  • [10] Joo K, Lee J, Kim I, Lee S Jong and Lee J 2008Biophysical Journal 95 (10) 4813
  • [11] Joo K, Lee J, Seo J-H, Lee K, Kim B-Gee and Lee J 2009 Prot. Struct. Funct. Bioinf. 75 (4) 1010
  • [12] Soding J 2005 Bioinformatics 21 (7) 951
  • [13] Joo K et al. 2016 Prot. Struct. Funct. Bioinf. 84 (S1) 221
  • [14] Sali A and Blundell T L 1993 Journal of Molecular Biology 234 (3) 779
  • [15] Lee J, Gross S P and Lee J 2012 Physical Review E 85 (5) 56702
  • 16] Lee J, Gross S P and Lee J 2013 Sci. Rep. 3 2197
  • [17] Lee J and Lee J 2013 PloS One 8 (4) , e60372
  • [18] Zhang Y and Skolnick J 2005 Nucleic Acids Research 33 (7) 2302
  • [19] MacKerell A D et al. 1998 J. Phys. Chem. B 102 (18) 3586 doi: 10.1021/jp973084f
  • [20] Joo K, Joung ISuk, Lee J, Lee J, Lee W, Brooks B, Lee S Jong and Lee J 2015 Prot. Struct. Funct. Bioinf. 83 2251
  • [21] Lee J, Lee J, Sasaki T N, Sasai M, Seok C and Lee J 2011 Prot. Struct. Funct. Bioinf. 79 (8) 2403
  • [22] Zhou H and Zhou Y 2002 Protein Science 11 (11) 2714
  • [23] Kortemme T, Morozov A V and Baker D 2003 Journal of Molecular Biology 326 (4) 1239
  • [24] Zhou H and Skolnick J 2011 Biophysical Journal 101 (8) 2043
  • [25] Lee J, Lee K, Joung I, Joo K, Brooks B R and Lee J 2015 BMC Bioinformatics 16 (1) 94
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2e23c1ca-7ff3-4f35-a270-b1c3867c27b4
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