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intrinsically

Intrinsically unstructured proteins

Intrinsically unstructured proteins, often referred to as naturally unfolded proteins or disordered proteins, are proteins characterized by their lack of stable tertiary structure as isolated subunits. The discovery of intrinsically unfolded proteins challenged the traditional protein structure paradigm, which states that a specific well-defined structure was required for the correct function of a protein and that the structure defines the function of the protein. This is clearly not the case for intrinsically unfolded proteins that remain functional despite the lack of a well-defined structure.

Biological role of intrinsic disorder

Many disordered proteins have the binding affinity with their receptors regulated by post-translational modification, thus it has been proposed that the flexibility of disordered proteins facilitates the different conformational requirements for binding the modifying enzymes as well as their receptors.

Flexible linkers

Disordered regions are often found as flexible linkers connecting two globular domains. Linker sequences vary greatly in length and amino acid sequence, but are similar in amino acid composition (rich in polar uncharged amino acids). Flexible linkers allow the connecting domains to freely twist and rotate through space to recruit their binding partners.

Coupled folding and binding

Many unstructured proteins undergo transitions to more ordered states upon binding to their targets. The coupled folding and binding may be local, involving only a few interacting residues, or it might involve an entire protein domain. It was recently shown that the coupled folding and binding allows the burial of a large surface area that would only be possible for a fully structured proteins if they were much larger (Gunasekaran et al, 2003).

The ability of disordered proteins to bind, and thus to exert a function, shows that stability is not a required condition.

Sequence signatures of disorder

Intrinsically unstructured proteins are characterized by a low content of bulky hydrophobic amino acids and a high proportion of polar and charged amino acids. Thus disordered sequences cannot bury sufficient hydrophobic core to fold like stable globular proteins. In some cases, hydrophobic clusters in disordered sequences provide the clues for identifying the regions that undergo coupled folding and binding.

Many disordered proteins also reveal low complexity sequences, i.e. sequences with overrepresentation of a few residues. While low complexity sequences are a strong indication of disorder, the reverse is not necessarily true, that is, not all disordered proteins have low complexity sequences.

Disordered proteins have a low content of predicted secondary structure.

Identification of intrinsically unstructured proteins

Intrinsically unfolded proteins, once purified, can be identified by various experimental methods. Folded proteins have a high density (partial specific volume of 0.72-0.74 mL/g) and commensurately small radius of gyration. Hence, unfolded proteins can be detected by methods that are sensitive to molecular size, density or hydrodynamic drag, such as size exclusion chromatography, analytical ultracentrifugation, Small angle X-ray scattering (SAXS), and measurements of the diffusion constant. Unfolded proteins are also characterized by their lack of secondary structure, as assessed by far-UV (170-250 nm) circular dichroism (esp. a pronounced minimum at ~200 nm) or infrared spectroscopy.

Unfolded proteins have exposed backbone peptide groups exposed to solvent, so that they are readily cleaved by proteases, undergo rapid hydrogen-deuterium exchange and exhibit a small dispersion (<1 ppm) in their 1H amide chemical shifts as measured by NMR. (Folded proteins typically show dispersions as large as 5 ppm for the amide protons.)

The primary method to obtain information on disordered regions of a protein is NMR spectroscopy. The lack of electron density in X-ray crystallographic studies may also be a sign of disorder.

De novo prediction of intrinsically unstructured proteins

Computational methods exploit the sequence signatures of disorder to predict whether a protein is disordered given its amino acid sequence. The table below, adapted from (Ferron et. al, 2006), shows the main features of softwares for disorder prediction. Note that different softwares use different definitions of disorder.

Predictor What is predicted Based on Generates and uses multiple sequence alignment?
PONDR All regions that are not rigid including random coils, partially unstructured regions, and molten globules Local aa composition, flexibility, hydropathy, etc No
SEG Low-complexity segments that is, “simple sequences” or “compositionally biased regions”. Locally optimized low-complexity segments are produced at defined levels of stringency and then refined according to the equations of Wootton and Federhen No
Disopred2 Regions devoid of ordered regular secondary structure Cascaded support vector machine classifiers trained on PSI-BLAST profiles Yes
Globplot Regions with high propensity for globularity on the Russell/Linding scale (propensities for secondary structures and random coils) Russell//Linding scale of disorder No
Disembl LOOPS (regions devoid of regular secondary structure); HOT LOOPS (highly mobile loops); REMARK465 (regions lacking electron density in crystal structure) Neural networks trained on X-ray structure data No
NORSp Regions with No Ordered Regular Secondary Structure (NORS). Most, but not all, are highly flexible. Secondary structure and solvent accessibility Yes
FoldIndex Regions that have a low hydrophobicity and high net charge (either loops or unstructured regions) Charge/hydrophaty analyzed locally using a sliding window No
Charge/hydropathy method. See (Uversky et al, 2000). Fully unstructured domains (random coils) Global sequence composition No
HCA (Hydrophobic Cluster Analysis) Hydrophobic clusters, which tend to form secondary structure elements Helical visualization of amino acid sequence No
PreLink Regions that are expected to be unstructured in all conditions, regardless of the presence of a binding partner Compositional bias and low hydrophobic cluster content. No
IUPred Regions that lack a well-defined 3D-structure under native conditions Energy resulting from inter-residue interactions, estimated from local amino acid composition No
RONN Regions that lack a well-defined 3D structure under native conditions Bio-basis function neural network trained on disordered proteins No

Since the methods above use different definitions of disorder and they were trained on different datasets, it is difficult to estimate their relative accuracy.

References

  • "Intrinsically unstructured proteins and their functions", HJ Dyson & PE Wright, Nat Rev Mol Cell Biol. 2005 Mar;6(3):197-208.
  • Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF & Jones DT. Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J. Mol. Biol. 337, 635–645, 2004.
  • Iakoucheva LM, Brown CJ, Lawson JD, Obradovic Z & Dunker AK. Intrinsic disorder in cell-signaling and cancer-associated proteins. J. Mol. Biol. 323, 573–584, 2002.
  • Ferron F, Longhi S, Canard B, Karlin B. A practical overview of protein disorder prediction methods. PROTEINS: Structure, Function, and Bioinformatics, 65:1-14, 2006.
  • Uversky VN, Gillespie JR, Fink, AL. Why are "natively unfolded proteins unstructured under physiologic conditions? PROTEINS: Structure, Function, and Bioinformatics, 41:415-427, 2000.
  • Gunasekaran K, Tsai CJ, Kumar S, Zanuy D & Nussinov R. Extended disordered proteins: targeting function with less scaffold. Trends Biochem. Sci. 28, 81–85, 2003.
  • Collins MO, Yu L, Campuzano I, Grant SG, Choudhary JS. Phosphoproteomic analysis of the mouse brain cytosol reveals a predominance of protein phosphorylation in regions of intrinsic sequence disorder. Mol Cell Proteomics. 2008 Apr 3

External links

Database of Protein Disorder

  • Disprot http://www.disprot.org/

Disorder prediction methods

  • PONDR http://www.pondr.com
  • SEG http://mendel.imp.univie.ac.at/METHODS/seg.server.html
  • Disopred2 http://bioinf.cs.ucl.ac.uk/disopred
  • Globplot http://globplot.embl.de
  • Disembl http://dis.embl.de
  • NORSp http://cubic.bioc.columbia.edu/services/NORSp
  • FoldIndex http://bip.weizmann.ac.il/fldbin/findex
  • HCA (Hydrophobic Cluster Analysis) http://smi.snv.jussieu.fr/hca/hca-seq.html
  • PreLink http://genomics.eu.org
  • IUPred http://iupred.enzim.hu
  • RONN http://www.strubi.ox.ac.uk/RONN

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