Abstract
Distributions of protein families and folds in genomes are highly skewed, having a small number of prevalent superfamiles/superfolds and a large number of families/folds of a small size. Why are the distributions of protein families and folds skewed? Why are there only a limited number of protein families? Here, we employ an information theoretic approach to investigate the protein sequence-structure relationship that leads to the skewed distributions. We consider that protein sequences and folds constitute an information theoretic channel and computed the most efficient distribution of sequences that code all protein folds. The identified distributions of sequences and folds are found to follow a power law, consistent with those observed for proteins in nature. Importantly, the skewed distributions of sequences and folds are suggested to have different origins: the skewed distribution of sequences is due to evolutionary pressure to achieve efficient coding of necessary folds, whereas that of folds is based on the thermodynamic stability of folds. The current study provides a new information theoretic framework for proteins that could be widely applied for understanding protein sequences, structures, functions, and interactions.
Citations
-
1 9
CrossRef
-
0
Web of Science
-
1 9
Scopus
Authors (3)
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1038/srep08166
- License
- open in new tab
Keywords
Details
- Category:
- Articles
- Type:
- artykuł w czasopiśmie wyróżnionym w JCR
- Published in:
-
Scientific Reports
no. 5,
ISSN: 2045-2322 - Language:
- English
- Publication year:
- 2015
- Bibliographic description:
- Magner A., Szpankowski W., Kihara D.: On the Origin of Protein Superfamilies and Superfolds// Scientific Reports. -Vol. 5, iss. 1 (2015), s.8166-
- DOI:
- Digital Object Identifier (open in new tab) 10.1038/srep08166
- Verified by:
- Gdańsk University of Technology
seen 99 times
Recommended for you
Characterizing the Performance of <span class="sc">xor</span> Games and the Shannon Capacity of Graphs
- R. Ramanathan,
- A. Kay,
- G. Murta
- + 1 authors