title: Prediction of intrinsic disorder and its use in functional proteomics.
abstract: The number of experimentally verified, intrinsically disordered (ID) proteins is rapidly rising. Research is often focused on a structural characterization of a given protein, looking for several key features. However, ID proteins with their dynamic structures that interconvert on a number of time-scales are difficult targets for the majority of traditional biophysical and biochemical techniques. Structural and functional analyses of these proteins can be significantly aided by disorder predictions. The current advances in the prediction of ID proteins and the use of protein disorder prediction in the fields of molecular biology and bioinformatics are briefly overviewed herein. A method is provided to utilize intrinsic disorder knowledge to gain structural and functional information related to individual proteins, protein groups, families, classes, and even entire proteomes.
authors.affiliation: School of Medicine, Indiana University, Indianapolis, USA.
authors.forename: Vladimir N
authors.initials: VN
authors.lastname: Uversky
authors.forename: Predrag
authors.initials: P
authors.lastname: Radivojac
authors.forename: Lilia M
authors.initials: LM
authors.lastname: Iakoucheva
authors.forename: Zoran
authors.initials: Z
authors.lastname: Obradovic
authors.forename: A Keith
authors.initials: AK
authors.lastname: Dunker
mesh_terms: D000596:Amino Acids / Q000032:analysis; D001665:Binding Sites; D019295:Computational Biology; D030562:Databases, Protein; D006801:Humans; D010766:Phosphorylation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D040901:Proteomics / Q000706:statistics & numerical data*; D012984:Software
chemical_list: D000596:Amino Acids; D011506:Proteins