The structure of the protein universe and genome evolution

Koonin, Eugene V.; Wolf, Yuri I.; Karev, Georgy P.

doi:10.1038/nature01256

Cited by 512 publications

(508 citation statements)

References 68 publications

Supporting

Mentioning

479

Contrasting

Unclassified

Order By: Relevance

“…"There is no doubt that protein families and superfamilies are monophyletic, that is, they derive from a common ancestor. In contrast, monophyly of protein folds, as opposed to folds originating by convergence from unrelated ancestors, remains an issue of debate" [19]. Our results suggest that folds may indeed retain an evolutionary relationship after all.…”

Section: λ J 'Smentioning

confidence: 64%

A Dynamic Model for the Evolution of Protein Structure

et al. 2016

View full text Add to dashboard Cite

Protein domains are three-dimensional arrangements of atomic structure that are recurrent in the proteomes of organisms. Since the three-dimensional structure of a protein determines its function, it is the fold, much more than the underlying protein sequence and underlying chemistry, that is evolutionarily conserved. We are interested in probing the history of life with these domain structures and glimpsing qualitative changes over time by studying a dynamic model of protein evolution. Using standard phylogenetic methods and a census of protein domain structure in hundreds of genomes, we have reconstructed phylogenetic trees of protein domains, defined using the Structural Classification of Proteins (SCOP), where the nodes are folds or fold superfamilies (FSFs), the character vector for each node is a list of abundances of said fold or FSF across a range of species that spans all three superkingdoms of life, and the character states are linearly polarized by abundance; higher abundance within and among species equates to older structures and determines tree structure.Here we explore at what rate fold or FSF variants and new folds or FSFs appear in evolution. We also explore what collective model of proteome evolution explains such rates. Briefly, what are the dynamics of change? A set of birth-death differential equations was selected to capture the change of interest, with one set for folds and another for FSFs. The models assume that at any given moment there are a certain number of different folds or FSFs, with various abundances, and as each fold or FSF diversifies there are slight changes in the folds or FSFs, producing fold or FSF variants. Eventually as the variants continue to diversify and change as well, a new fold or FSF is born. Thus, there are two rate parameters in each model: the growth rate of fold or FSF variants and the rate of appearance of new folds or FSFs. The model governs the rate change of the average total abundance of a fold or FSF with time. It is fit to the tree so only those fold or FSF transitions ii actually present in the tree are assumed possible in the equations. It assumes a global perspective: the total abundance of a fold or FSF is that of the fold or FSF across all species, not within one organism. This perspective is used to properly discount terms of horizontal transfer in a birth-death model since such a transfer contributes no new folds or FSFs to the net abundance across all organisms.Our model determines 1) that there is a tight connection between the history of folds and FSFs, 2) that the corresponding transition probabilities to new variants of a fold experienced a sharp increase just as the transition probabilities to new folds experienced a steep decline and 3) that this simultaneous sharp increase and decline is explainable by and consistent with the combinatorial explosion of structural domains, referring to the period of high combination and rearrangement of domains and distribution of these new combinations in novel lineages, and the rise of organisma...

show abstract

Section: λ J 'Smentioning

confidence: 64%

A Dynamic Model for the Evolution of Protein Structure

et al. 2016

View full text Add to dashboard Cite

show abstract

“…The overall form of protein family size distributions can be understood in terms of birth (duplication with or without mutations), death (loss) and innovation (de novo acquisition) (BDI) of genes (Huynen and van Nimwegen, 1998;Yanai et al, 2000;Karev et al, 2002;Koonin et al, 2002;Karev et al, 2003Karev et al, , 2004Reed and Hughes, 2004).…”

Section: Dynamical Bdi Modelmentioning

confidence: 99%

Evolution of protein families: Is it possible to distinguish between domains of life?

Sales‐Pardo

Chan

Amaral

et al. 2007

Gene

View full text Add to dashboard Cite

Understanding evolutionary relationships between species can shed new light into the rooting of the tree of life and the origin of eukaryotes, thus, resulting in a long standing interest in accurately assessing evolutionary parameters at time scales on the order of a billion of years. Prior work suggests large variability in molecular substitution rates, however, we still do not know whether such variability is due to species-specific trends at a genomic scale, or whether it can be attributed to the fluctuations inherent in any stochastic process. Here, we study the statistical properties of gene and protein family sizes in order to quantify the long time scale evolutionary differences and similarities across species. We first determine the protein families of 209 species of bacteria and 20 species of archaea. We find that we are unable to reject the null hypothesis that the protein family sizes of these species are drawn from the same distribution. In addition, we find that for species classified in the same phylogenetic branch or in the same lifestyle group, family size distributions are not significantly more similar than for species in different branches. These two findings can be accounted for in terms of a dynamical birth, death, and innovation model that assumes identical protein family evolutionary rates for all species. Our theoretical and empirical results thus strongly suggest that the variability empirically observed in protein family size distributions is compatible with the expected stochastic fluctuations for an evolutionary process with identical genomic evolutionary rates. Our findings hold special importance for the plausibility of some theories of the origin of eukaryotes which require drastic changes in evolutionary rates for some period during the last two billion years.

show abstract

“…levels (73). The addition of genes is balanced by gene loss, as many duplicated genes are lost (51). Duplicated genes usually change their functions quite rapidly (on an evolutionary time scale), which implies a change in link structure (7,71,74).…”

Section: Network Growth and Evolutionmentioning

confidence: 99%

“…Interestingly, such hubs are virtually identical for a variety of organisms (Bacteria and Archaea), reflecting early evolutionary change. They do differ in Eukaryotes (humans), however, and, thus, they can be attractive therapeutic targets for nonhuman cells (51,65). Nodes with few links are more species-specific and are presumably more recent evolutionary additions (21).…”

Section: Network Growth and Evolutionmentioning

confidence: 99%

Exploring the cell's network with molecular imaging

Enzmann

2006

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Molecular imaging is already a powerful tool for investigating molecular interactions within the cell. Interpreting molecular imaging findings will, however, take us into the more unfamiliar, nonlinear realm of networks. The network class of interest is the "scale-free" network, which characterizes not only the cell, but also surprisingly, other real work networks such as the world wide web. This network topology yields insights in how the cell is functionally organized via motifs, modules, and different types of hubs. Additional organizational information is gained from the cell's evolutionary history. Interpretation of molecular images will be deepened by a both qualitative and quantitative knowledge of the cell's network. Importantly, cell network behavior can be independent of molecular detail. For this reason, the same molecule can serve different functions in different cells or even within the same cell. Since a scalefree network's behavior is likely to be nonlinear and exhibit emergent behavior, a degree of caution is prudent in assigning cause and effect to molecular imaging findings in our effort to reengineer some of the cell's functions. Molecular imagers will need to be cognizant of the level of organization in the cell's network they are interrogating.

show abstract

The structure of the protein universe and genome evolution

Cited by 512 publications

References 68 publications

A Dynamic Model for the Evolution of Protein Structure

A Dynamic Model for the Evolution of Protein Structure

Evolution of protein families: Is it possible to distinguish between domains of life?

Exploring the cell's network with molecular imaging

Contact Info

Product

Resources

About