Poster #RP123
Computational analysis of intrinsic disorder in the evolution of eukaryotic proteins
Kevin Koh*, Paula Ashe**, Raju Datla**, Gopalan Selvaraj**
*University of Saskatchewan, Saskatoon, Canada; **National Research Council of Canada, Saskatoon, Canada
The concept that structure determines protein function has profoundly impacted biochemistry, molecular biology and subsequently genomics. However, the lack of discernible 3-D structures for large segments or for the entire sequence in some proteins has nonetheless attracted the attention of computational biologists and more recently experimental biologists. Various computational tools based on training sets of intrinsically disordered proteins (IDP) discerned from experimental methods including NMR, circular dichroism, and x-ray crystallography are available. Recent reports have estimated that 35-63% of all proteins in representative eukaryotes are intrinsically disordered. This statement is mostly based on a working definition that considers a protein to be an IDP if it contains at least one stretch of >39 amino acids that are intrinsically disordered. IDPs are considered to afford functional plasticity, especially in protein-ligand interactions and in processes pertaining to signaling and regulation. In view of the structural, developmental, and functional complexity of multi-cellular eukaryotes, we have examined the landscape of IDPs among the orthologs in eukaryotes and for comparison have included unicellular eukaryotes as well. As an example, computational analyses of the IDPs in the model plant Arabidopsis thaliana and in the human proteome were used to identify the distribution of IDPs among the orthologs and to examine any potential functional relevance.