P2: Algorithms and Evolutionary Dynamics of Modular Protein Evolution
Modularity is a hallmark of molecular evolution, whether considering gene regulation, the components of metabolic pathways or signalling cascades,
the ability to reuse autonomous modules in different molecular contexts can expedite evolutionary innovation. Over times scales of several 100 MY,
the evolution of protein coding genes is dominated by the modular rearrangements of protein domains, their evolutionary, structural and functional units.
While a small core of arrangements is universal, a large fraction of multi-domain arrangements is species specific and has been created recently via gene duplication,
fusion and terminal loss of domains. Surprisingly, thousands of domains are completely (i.e. all copies within a genome) lost from genomes along every lineage in a stochastic manner,
i.e. at a fairly constant rate of ca. four domains per million years.
Novel domains are rarely fixed and arise either as their own genes or terminally, by extension of existing reading frames.
Novel domains are under strong selection pressure and confer a strong fitness value as they rapidly attain high copy numbers
within the genomes and are involved in biotic defence, reproduction and development.
Using cross-species genomic comparisons and population genomics we investigate the genetic mechanisms and biophysical constraints of domain emergence and develop algorithms for rapid screening of many genomes, understanding their phylogenies and comparing, aligning and clustering sequences. This is possible because of complexity reduction (sequences can be characterised as linear arrangements of 5-6 domains drawn from an alphabet of several thousand characters as opposed to ca. 500 amino acids drawn from an alphabet of 20) and the maintenance of linear order and the high reliability of HMMs which characterise the domains.
We test the potential of increased evolvability by rearranged domains experimentally in biochemical pathways, for the defence against pathogens and during developmental processes.
The software we develop is available here: DomainWorld
People: Carsten Kemena, Steffen Klasberg, Elias Dohmen, Baki Coban
Funding: Volkswagen Foundation (2 x, 2009 -- 2013); DFG (2009 -- 2013).
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Techniques employed: Hidden Markov Models, dynamic programming, design and implementation of algorithms, databases and interactive graphical interfaces; DnDs ratio tests (PAML)