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A practical overview of protein disorder prediction methods
Author(s) -
Ferron François,
Longhi Sonia,
Canard Bruno,
Karlin David
Publication year - 2006
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.21075
Subject(s) - computer science , intrinsically disordered proteins , similarity (geometry) , sequence (biology) , artificial intelligence , data science , computational biology , biology , genetics , image (mathematics) , biochemistry
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. Proteins 2006. © 2006 Wiley‐Liss, Inc.