Your browser doesn't support javascript.
loading
A Suggestion of Converting Protein Intrinsic Disorder to Structural Entropy Using Shannon's Information Theory.
Guo, Hao-Bo; Ma, Yue; Tuskan, Gerald A; Qin, Hong; Yang, Xiaohan; Guo, Hong.
Afiliação
  • Guo HB; Department of Computer Science and Engineering, SimCenter, University of Tennessee, Chattanooga, TN 37403, USA.
  • Ma Y; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA.
  • Tuskan GA; Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.
  • Qin H; Department of Computer Science and Engineering, SimCenter, University of Tennessee, Chattanooga, TN 37403, USA.
  • Yang X; Department of Biology, Geology, and Environmental Science, University of Tennessee, Chattanooga, TN 37403, USA.
  • Guo H; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA.
Entropy (Basel) ; 21(6)2019 Jun 14.
Article em En | MEDLINE | ID: mdl-33267305
ABSTRACT
We propose a framework to convert the protein intrinsic disorder content to structural entropy (H) using Shannon's information theory (IT). The structural capacity (C), which is the sum of H and structural information (I), is equal to the amino acid sequence length of the protein. The structural entropy of the residues expands a continuous spectrum, ranging from 0 (fully ordered) to 1 (fully disordered), consistent with Shannon's IT, which scores the fully-determined state 0 and the fully-uncertain state 1. The intrinsically disordered proteins (IDPs) in a living cell may participate in maintaining the high-energy-low-entropy state. In addition, under this framework, the biological functions performed by proteins and associated with the order or disorder of their 3D structures could be explained in terms of information-gains or entropy-losses, or the reverse processes.
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Entropy (Basel) Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Entropy (Basel) Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos