Template based protein structure modeling by global optimization in CASP11.
For the template-based modeling (TBM) of CASP11 targets, we have developed three new protein modeling protocols (nns for server prediction and LEE and LEER for human prediction) by improving upon our previous CASP protocols (CASP7 through CASP10). 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 more successful fold recognition, a new alignment method called CRFalign was developed. It can incorporate sensitive positional and environmental dependence in alignment scores as well as strong nonlinear correlations among various features. Modifications and adjustments were made to the form of the energy function and weight parameters pertaining to the chain building procedure. For the side-chain remodeling step, residue-type dependence was introduced to the cutoff value that determines the entry of a rotamer to the side-chain modeling library. The improved performance of the nns server method is attributed to successful fold recognition achieved by combining several methods including CRFalign and to the current modeling formulation that can incorporate native-like structural aspects present in multiple templates. The LEE protocol is identical to the nns one except that CASP11-released server models are used as templates. The success of LEE in utilizing CASP11 server models indicates that proper template screening and template clustering assisted by appropriate cluster ranking promises a new direction to enhance protein 3D modeling. Proteins 2016; 84(Suppl 1):221-232. © 2015 Wiley Periodicals, Inc.
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.
Keehyoung
K
Joo
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.
InSuk
I
Joung
Center for in Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea.
Sun Young
SY
Lee
Center for in Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea.
Jong Yun
JY
Kim
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.
Qianyi
Q
Cheng
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.
Balachandran
B
Manavalan
School of Computational Sciences, Korea Institute for Advanced Study, Seoul, 130-722, Korea.
Jong Young
JY
Joung
Center for in Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea.
Seungryong
S
Heo
Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, 20852.
Juyong
J
Lee
Center for in Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea.
Mikyung
M
Nam
Center for in Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea.|Korea Research Institute of Standards and Science (KRISS), Seoul, 305-600, Korea.
In-Ho
IH
Lee
Center for in Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea.|Department of Physics, University of Suwon, Hwaseong-Si, Gyeonggi-Do, 445-743, Korea.
Sung Jong
SJ
Lee
Center for in Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea. jlee@kias.re.kr.|Center for Advanced Computation, Korea Institute for Advanced Study, Seoul, 130-722, Korea. jlee@kias.re.kr.|School of Computational Sciences, Korea Institute for Advanced Study, Seoul, 130-722, Korea. jlee@kias.re.kr.
Jooyoung
J
Lee
D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data*; D003198:Computer Simulation; D030562:Databases, Protein; D006801:Humans; D020407:Internet; D008958:Models, Molecular; D015233:Models, Statistical; D017510:Protein Folding; D054730:Protein Interaction Domains and Motifs; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment; D012984:Software; D040681:Structural Homology, Protein; D013816:Thermodynamics
D011506:Proteins
casp; fold recognition; global optimization; homology modeling; protein structure modeling; sequence alignment; template based modeling