Bioinformatical Approaches to Unstructured/Disordered Proteins and Their Complexes

  • Bálint Mészáros
  • Zsuzsanna Dosztányi
  • Erzsébet Fichó
  • Csaba Magyar
  • István Simon
Part of the Springer Series on Bio- and Neurosystems book series (SSBN, volume 8)


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 their isolated form. Nevertheless, taking advantage of ongoing efforts in the collection, cataloguing, and annotation of known IDPs in publicly available databases, various bioinformatics tools were developed over the last few years. These methods enable the further identification and characterization of IDPs 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.



This work was supported by grants Hungarian Research and Developments Fund (OTKA K108798 for Z.D. and K115698 for I.S.), the “Momentum” grant from the Hungarian Academy of Sciences (LP2014-18) for Z.D. The János Bolyai Research Scholarship of the Hungarian Academy of Sciences for C.M. is also gratefully acknowledged. We would like to thank to Mark Adamsbaum for his critical reading of the manuscript.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bálint Mészáros
    • 1
  • Zsuzsanna Dosztányi
    • 1
  • Erzsébet Fichó
    • 2
  • Csaba Magyar
    • 2
  • István Simon
    • 2
  1. 1.MTA-ELTE Momentum Bioinformatics Research GroupEötvös Loránd UniversityBudapestHungary
  2. 2.Institute of Enzymology, RCNS, HASBudapestHungary

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