Document Detail

Heuristic energy landscape paving for protein folding problem in the three-dimensional HP lattice model.
MedLine Citation:
PMID:  22551826     Owner:  NLM     Status:  Publisher    
The protein folding problem, i.e., the prediction of the tertiary structures of protein molecules from their amino acid sequences is one of the most important problems in computational biology and biochemistry. However, the extremely difficult optimization problem arising from energy function is a key challenge in protein folding simulation. The energy landscape paving (ELP) method has already been applied very successfully to off-lattice protein models and other optimization problems with complex energy landscape in continuous space. By improving the ELP method, and subsequently incorporating the neighborhood strategy with the pull-move set into the improved ELP method, a heuristic ELP algorithm is proposed to find low-energy conformations of 3D HP lattice model proteins in the discrete space. The algorithm is tested on three sets of 3D HP benchmark instances consisting 31 sequences. For eleven sequences with 27 monomers, the proposed method explores the conformation surfaces more efficiently than other methods, and finds new lower energies in several cases. For ten 48-monomer sequences, we find the lowest energies so far. With the achieved results, the algorithm converges rapidly and efficiently. For all ten 64-monomer sequences, the algorithm finds lower energies within comparable computation times than previous methods. Numeric results show that the heuristic ELP method is a competitive tool for protein folding simulation in 3D lattice model. To the best of our knowledge, this is the first application of ELP to the 3D discrete space.
Jingfa Liu; Gang Li; Jun Yu; Yonglei Yao
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-3-5
Journal Detail:
Title:  Computational biology and chemistry     Volume:  38C     ISSN:  1476-928X     ISO Abbreviation:  -     Publication Date:  2012 Mar 
Date Detail:
Created Date:  2012-5-3     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101157394     Medline TA:  Comput Biol Chem     Country:  -    
Other Details:
Languages:  ENG     Pagination:  17-26     Citation Subset:  -    
Copyright Information:
Copyright © 2012 Elsevier Ltd. All rights reserved.
School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044, China; Network Information Center, Nanjing University of Information Science & Technology, Nanjing 210044, China; Department of Mathematics & Statistics, The University of Vermont, Burlington, VT 05405, USA.
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