A practical overview of protein disorder prediction methods.
In the past few years there has been a growing awareness that a large number of proteins contain long disordered (unstructured) regions that often play a functional role. However, these disordered regions are still poorly detected. Recognition of disordered regions in a protein is important for two main reasons: reducing bias in sequence similarity analysis by avoiding alignment of disordered regions against ordered ones, and helping to delineate boundaries of protein domains to guide structural and functional studies. As none of the available method for disorder prediction can be taken as fully reliable on its own, we present an overview of the methods currently employed highlighting their advantages and drawbacks. We show a few practical examples of how they can be combined to avoid pitfalls and to achieve more reliable predictions.
Ferron
François
F
Architecture et Fonction des Macromolécules Biologiques, UMR 6098 CNRS et Universités Aix-Marseille I et II, Marseille, France.
Longhi
Sonia
S
Canard
Bruno
B
Karlin
David
D
D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods; D006360:Heat-Shock Proteins / Q000737:chemistry; D006801:Humans; D019590:Nucleocapsid Proteins; D009698:Nucleoproteins / Q000737:chemistry; D017510:Protein Folding; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein; D012984:Software; D014764:Viral Proteins / Q000737:chemistry
C113285:HSBP1 protein, human; D006360:Heat-Shock Proteins; D019590:Nucleocapsid Proteins; D009698:Nucleoproteins; D011506:Proteins; D014764:Viral Proteins; C058314:nucleoprotein, Measles virus