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  • 标题:Network measures for protein folding state discrimination
  • 本地全文:下载
  • 作者:Giulia Menichetti ; Piero Fariselli ; Daniel Remondini
  • 期刊名称:Scientific Reports
  • 电子版ISSN:2045-2322
  • 出版年度:2016
  • 卷号:6
  • 期号:1
  • DOI:10.1038/srep30367
  • 语种:English
  • 出版社:Springer Nature
  • 摘要:Proteins fold using a two-state or multi-state kinetic mechanisms, but up to now there is not a first-principle model to explain this different behavior. We exploit the network properties of protein structures by introducing novel observables to address the problem of classifying the different types of folding kinetics. These observables display a plain physical meaning, in terms of vibrational modes, possible configurations compatible with the native protein structure, and folding cooperativity. The relevance of these observables is supported by a classification performance up to 90%, even with simple classifiers such as discriminant analysis.
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