Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
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Pritišanac, I; Vernon, RM; Moses, AM; Forman, Kay, JD.
Entropy and Information within Intrinsically Disordered Protein Regions.
Entropy (Basel). 2019; 21(7):
Doi: 10.3390/e21070662
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- Abstract:
- Bioinformatics and biophysical studies of intrinsically disordered proteins and regions (IDRs) note the high entropy at individual sequence positions and in conformations sampled in solution. This prevents application of the canonical sequence-structure-function paradigm to IDRs and motivates the development of new methods to extract information from IDR sequences. We argue that the information in IDR sequences cannot be fully revealed through positional conservation, which largely measures stable structural contacts and interaction motifs. Instead, considerations of evolutionary conservation of molecular features can reveal the full extent of information in IDRs. Experimental quantification of the large conformational entropy of IDRs is challenging but can be approximated through the extent of conformational sampling measured by a combination of NMR spectroscopy and lower-resolution structural biology techniques, which can be further interpreted with simulations. Conformational entropy and other biophysical features can be modulated by post-translational modifications that provide functional advantages to IDRs by tuning their energy landscapes and enabling a variety of functional interactions and modes of regulation. The diverse mosaic of functional states of IDRs and their conformational features within complexes demands novel metrics of information, which will reflect the complicated sequence-conformational ensemble-function relationship of IDRs.
- Find related publications in this database (Keywords)
- intrinsically disordered proteins
- Shannon entropy
- information theory
- evolutionary conservation
- conformational entropy
- low-complexity sequences
- liquid-liquid phase separation
- post-translational modifications
- conformational ensembles
- biophysics