GlobPlot: exploring protein sequences for globularity and disorder. A major challenge in the proteomics and structural genomics era is to predict protein structure and function, including identification of those proteins that are partially or wholly unstructured. Non-globular sequence segments often contain short linear peptide motifs (e.g. SH3-binding sites) which are important for protein function. We present here a new tool for discovery of such unstructured, or disordered regions within proteins. GlobPlot ( is a web service that allows the user to plot the tendency within the query protein for order/globularity and disorder. We show examples with known proteins where it successfully identifies inter-domain segments containing linear motifs, and also apparently ordered regions that do not contain any recognised domain. GlobPlot may be useful in domain hunting efforts. The plots indicate that instances of known domains may often contain additional N- or C-terminal segments that appear ordered. Thus GlobPlot may be of use in the design of constructs corresponding to globular proteins, as needed for many biochemical studies, particularly structural biology. GlobPlot has a pipeline interface—GlobPipe—for the advanced user to do whole proteome analysis. GlobPlot can also be used as a generic infrastructure package for graphical displaying of any possible propensity.

References in zbMATH (referenced in 7 articles )

Showing results 1 to 7 of 7.
Sorted by year (citations)

  1. Małysiak-Mrozek, Bożena: Uncertainty, imprecision, and many-valued logics in protein bioinformatics (2019)
  2. He, Hao; Zhao, Jiaxiang: A low computational complexity scheme for the prediction of intrinsically disordered protein regions (2018)
  3. Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)
  4. Via, Allegra; Gould, Cathryn M.; Gemünd, Christine; Gibson, Toby J.; Helmer-Citterich, Manuela: A structure filter for the eukaryotic linear motif resource (2009) ioport
  5. Bulashevska, Alla; Eils, Roland: Using Bayesian multinomial classifier to predict whether a given protein sequence is intrinsically disordered (2008)
  6. Cheng, Jianlin; Sweredoski, Michael J.; Baldi, Pierre: DOMpro: protein domain prediction using profiles, secondary structure, relative solvent accessibility, and recursive neural networks (2006) ioport
  7. Linding, Rune; Russell, Robert B.; Neduva, Victor; Gibson, Toby J.: Globplot: Exploring protein sequences for globularity and disorder. (2003) ioport