Name
Abstract | ||
|---|---|---|
Proteins are essential molecules of life in the cell and are involved in multiple and highly specialized tasks encoded in the amino acid sequence. In particular, protein function is closely related to fundamental units of protein structure called domains. Here, we investigate the distribution of kinds of domains in human cells. Our findings show that while the number of domain types shared by k proteins follows a scale-free distribution, the number of proteins composed of k types of domains decays as an exponential distribution. In contrast, previous data analyses and mathematical modeling reported a scale-free distribution for the protein domain distribution because the relation between kinds of domains and the number of domains in a protein was not considered. Based on this finding, we have developed an evolutionary model based on (1) growth process and (2) copy mechanism that explains the emergence of this mixing of exponential and scale-free distributions. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02466-5_50 | Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering |
Keywords | Field | DocType |
Growing networks,protein domains,scale-free networks | Exponential function,Protein domain,Bipartite graph,Scale-free network,Artificial intelligence,Exponential distribution,Base unit (measurement),Machine learning,Mathematics,Peptide sequence,Protein structure | Conference |
Volume | ISSN | Citations |
4 | 1867-8211 | 2 |
PageRank | References | Authors |
0.63 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jose C Nacher | 1 | 33 | 6.67 |
| T. Ochiai | 2 | 2 | 0.63 |
| Morihiro Hayashida | 3 | 154 | 21.88 |
| Tatsuya Akutsu | 4 | 2169 | 216.05 |