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A Suggestion of Converting Protein Intrinsic Disorder to Structural Entropy Using Shannon's Information Theory.

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.

SUBMITTER: Guo HB

PROVIDER: S-EPMC7515080 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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A Suggestion of Converting Protein Intrinsic Disorder to Structural Entropy Using Shannon's Information Theory.

Guo Hao-Bo HB Ma Yue Y Tuskan Gerald A GA Qin Hong H Yang Xiaohan X Guo Hong H

Entropy (Basel, Switzerland) 20190614 6

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 ...[more]

PMID: 33267305

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A Suggestion of Converting Protein Intrinsic Disorder to Structural Entropy Using Shannon's Information Theory.

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A Suggestion of Converting Protein Intrinsic Disorder to Structural Entropy Using Shannon's Information Theory.

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