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Prediction of protein structural features from sequence data based on Shannon entropy and Kolmogorov complexity.

ABSTRACT: While the genome for a given organism stores the information necessary for the organism to function and flourish it is the proteins that are encoded by the genome that perhaps more than anything else characterize the phenotype for that organism. It is therefore not surprising that one of the many approaches to understanding and predicting protein folding and properties has come from genomics and more specifically from multiple sequence alignments. In this work I explore ways in which data derived from sequence alignment data can be used to investigate in a predictive way three different aspects of protein structure: secondary structures, inter-residue contacts and the dynamics of switching between different states of the protein. In particular the use of Kolmogorov complexity has identified a novel pathway towards achieving these goals.

SUBMITTER: Bywater RP

PROVIDER: S-EPMC4391790 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Prediction of protein structural features from sequence data based on Shannon entropy and Kolmogorov complexity.

Bywater Robert Paul RP

PloS one 20150409 4

While the genome for a given organism stores the information necessary for the organism to function and flourish it is the proteins that are encoded by the genome that perhaps more than anything else characterize the phenotype for that organism. It is therefore not surprising that one of the many approaches to understanding and predicting protein folding and properties has come from genomics and more specifically from multiple sequence alignments. In this work I explore ways in which data derive ...[more]

PMID: 25856073

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Dataset Information

Prediction of protein structural features from sequence data based on Shannon entropy and Kolmogorov complexity.

Publications

Prediction of protein structural features from sequence data based on Shannon entropy and Kolmogorov complexity.

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