Universal features in the genome-level evolution of protein domains.
BACKGROUND
Protein domains can be used to study proteome evolution at a coarse scale. In particular, they are found on genomes with notable statistical distributions. It is known that the distribution of domains with a given topology follows a power law. We focus on a further aspect: these distributions, and the number of distinct topologies, follow collective trends, or scaling laws, depending on the total number of domains only, and not on genome-specific features.


RESULTS
We present a stochastic duplication/innovation model, in the class of the so-called 'Chinese restaurant processes', that explains this observation with two universal parameters, representing a minimal number of domains and the relative weight of innovation to duplication. Furthermore, we study a model variant where new topologies are related to occurrence in genomic data, accounting for fold specificity.


CONCLUSIONS
Both models have general quantitative agreement with data from hundreds of genomes, which indicates that the domains of a genome are built with a combination of specificity and robust self-organizing phenomena. The latter are related to the basic evolutionary 'moves' of duplication and innovation, and give rise to the observed scaling laws, a priori of the specific evolutionary history of a genome. We interpret this as the concurrent effect of neutral and selective drives, which increase duplication and decrease innovation in larger and more complex genomes. The validity of our model would imply that the empirical observation of a small number of folds in nature may be a consequence of their evolution.
Cosentino Lagomarsino
Marco
M
Università degli Studi di Milano, Dip Fisica Milano, Italy. Marco.Cosentino@unimi.it
Sellerio
Alessandro L
AL
Heijning
Philip D
PD
Bassetti
Bruno
B
D019143:Evolution, Molecular; D016678:Genome; D023281:Genomics / Q000379:methods; D008962:Models, Theoretical; D017510:Protein Folding; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry / Q000235:genetics*; D020543:Proteome; D016008:Statistical Distributions; D013269:Stochastic Processes
D011506:Proteins; D020543:Proteome