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Improvements of network approach for analysis of the folding free‐energy surface of peptides and proteins
Author(s) -
Jiang Xuewei,
Chen Changjun,
Xiao Yi
Publication year - 2010
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.21544
Subject(s) - folding (dsp implementation) , markov chain , protein folding , downhill folding , surface (topology) , energy (signal processing) , folding funnel , computer science , biological system , chemistry , statistical physics , physics , phi value analysis , mathematics , machine learning , biology , engineering , geometry , biochemistry , quantum mechanics , electrical engineering
Folding network is an effective approach to investigate the high‐dimensional free‐energy surface of peptide and protein folding, and it can avoid the limitations of the projected free‐energy surface based on two‐order parameters. In this article, we present improvements of the effectiveness and accuracy of the folding network analysis based on Markov cluster (MCL) algorithm. We used this approach to investigate the folding free‐energy surface of the beta‐hairpin peptide trpzip2 and found the folding network is able to determine the basins and folding paths of trpzip2 more clearly and accurately than the two‐dimensional free‐energy surface. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010