The evolution of domain arrangements in proteins and interaction networks

Bornberg‐Bauer, Erich; Beaussart, Francois; Kummerfeld, Sarah; Teichmann, Sarah A.; Weiner, January

doi:10.1007/s00018-004-4416-1

Cited by 118 publications

(91 citation statements)

References 90 publications

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“…Proteins are therefore modular, and modularity expresses at different hierarchical levels. In multidomain proteins, domains can be either repeated or combined in defined order (100,(166)(167)(168). However, the topology of domain combinations is highly conserved.…”

Section: The Hierarchical Nature Of the Protein Worldmentioning

confidence: 99%

Origins and evolution of modern biochemistry: insights from genomes and molecular structure

et al. 2008

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Section: The Hierarchical Nature Of the Protein Worldmentioning

confidence: 99%

Origins and evolution of modern biochemistry: insights from genomes and molecular structure

et al. 2008

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“…12-14 Many earlier studies analyzed domain combinations to better understand how domains work together to promote the function of a protein. [15][16][17] They established that domains combine under selection rather than by chance. 18,19 Specifically, some combinations appear more frequently than others, and the distribution of the number of domain neighbors follows a power law.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Evolution of Protein Domain Architectures Using Maximum Parsimony

Fong¹,

Geer²,

Panchenko³

et al. 2007

Journal of Molecular Biology

109

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Domains are basic evolutionary units of proteins and most proteins have more than one domain. Advances in domain modeling and collection are making it possible to annotate a large fraction of known protein sequences by a linear ordering of their domains, yielding their architecture. Protein domain architectures link evolutionarily related proteins and underscore their shared functions. Here, we attempt to better understand this association by identifying the evolutionary pathways by which extant architectures may have evolved. We propose a model of evolution in which architectures arise through rearrangements of inferred precursor architectures and acquisition of new domains. These pathways are ranked using a parsimony principle, whereby scenarios requiring the fewest number of independent recombination events, namely fission and fusion operations, are assumed to be more likely. Using a data set of domain architectures present in 159 proteomes that represent all three major branches of the tree of life allows us to estimate the history of over 85% of all architectures in the sequence database. We find that the distribution of rearrangement classes is robust with respect to alternative parsimony rules for inferring the presence of precursor architectures in ancestral species. Analyzing the most parsimonious pathways, we find 87% of architectures to gain complexity over time through simple changes, among which fusion events account for 5.6 times as many architectures as fission. Our results may be used to compute domain architecture similarities, for example, based on the number of historical recombination events separating them. Domain architecture "neighbors" identified in this way may lead to new insights about the evolution of protein function.

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“…[79,80] Wuchty [81] studied co-occurrence networks of the ProDom, Pfam, and Prosite domains, with edges between domains that co-occur in at least one protein. He showed that the resulting network does not have random graph characteristics; rather, it is scale-free.…”

Section: Co-occurrence Networkmentioning

confidence: 99%

Representation of the Protein Universe using Classifications, Maps, and Networks

Ben‐Tal

Kolodny

2014

Israel Journal of Chemistry

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A meaningful and coherent global picture of the protein universe is needed to better understand protein evolution and the underlying biophysics. We survey the studies that tackled this fundamental challenge, providing a glimpse of the protein space. A global picture represents all known local relationships among proteins, and needs to do so in a comprehensive and accurate manner. Three types of global representations can be used: classifications, maps, and networks. In these, the local relationships are derived, based on the similarity of the proteins′ sequences, structures, or functions (or a combination of these). Alternatively, the local relationships can be co‐occurrences of elements in the protein universe. The representations can be based on different objects: full polypeptide chains, fragments, such as structural domains, or even smaller motifs. Different protein qualities were revealed in each study; many point out the uniqueness of domains of the alpha/beta SCOP (structural classification of proteins) class.

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The evolution of domain arrangements in proteins and interaction networks

Cited by 118 publications

References 90 publications

Origins and evolution of modern biochemistry: insights from genomes and molecular structure

Origins and evolution of modern biochemistry: insights from genomes and molecular structure

Modeling the Evolution of Protein Domain Architectures Using Maximum Parsimony

Representation of the Protein Universe using Classifications, Maps, and Networks

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