Bioinformatical Approaches to Unstructured/Disordered Proteins and Their Interactions

  • Bálint Mészáros
  • Zsuzsanna Dosztányi
  • Csaba Magyar
  • István Simon
Part of the Springer Series in Bio-/Neuroinformatics book series (SSBN, volume 1)

Abstract

Intrinsically unstructured/disordered proteins (IUPs/IDPs) exist as highly flexible conformational ensembles without adopting a stable three-dimensional structure. Experimental and bioinformatical studies in the past two decades have shown that these proteins play a central role in various signaling and regulatory processes. Accordingly, their frequency in higher eukaryotes reaches high proportions and their malfunction can be connected to a wide variety of diseases. Recognizing the biological importance of these proteins motivated researchers to understand various aspects of disordered proteins and protein segments from the viewpoint of biochemistry, molecular biology and pharmacology. In general, IDPs are difficult to study experimentally because of the lack of a unique structure in the isolated form. Nevertheless, various bioinformatics tools developed over the last few years enable their identification and characterization using only the amino acid sequence. In this chapter — after a brief introduction to IDPs in general — we present a small survey of current methods aimed at identifying disordered proteins or protein segments, focusing on those that are publicly available as web servers. We also discuss in more detail approaches that predict disordered regions and specific regions involved in protein binding by modeling the physical background of protein disorder. Furthermore, we argue that the heterogeneity of disordered segments needs to be taken into account for a better understanding of protein disorder and the correct use and interpretation of the output of disorder prediction algorithms.

Keywords

Binding Region Globular Protein Secondary Structure Prediction Linear Motif Bioinformatical Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Bálint Mészáros
    • 1
  • Zsuzsanna Dosztányi
    • 1
  • Csaba Magyar
    • 1
  • István Simon
    • 1
  1. 1.Institute of EnzymologyRCNS, HASBudapestHungary

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