Intrinsically Disordered Proteins: Where Computation Meets Experiment
Name
Burger-2014-Intrinsically Disord.pdf
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4.67 MB
Format
Adobe PDF
Checksum (MD5)
7274fc3d795db70d4a7cd0ead9d51f5d
Author(s)
Burger, Virginia M.
Gurry, Thomas
Stultz, Collin M.
Date Issued
October 2014
Journal
Polymers
Publisher
MDPI AG
Citation
Burger, Virginia M., Thomas Gurry, and Collin M. Stultz. “Intrinsically Disordered Proteins: Where Computation Meets Experiment.” Polymers 6, no. 10 (October 2014): 2684–2719.
Version
Final published version
Abstract
Proteins are heteropolymers that play important roles in virtually every biological reaction. While many proteins have well-defined three-dimensional structures that are inextricably coupled to their function, intrinsically disordered proteins (IDPs) do not have a well-defined structure, and it is this lack of structure that facilitates their function. As many IDPs are involved in essential cellular processes, various diseases have been linked to their malfunction, thereby making them important drug targets. In this review we discuss methods for studying IDPs and provide examples of how computational methods can improve our understanding of IDPs. We focus on two intensely studied IDPs that have been implicated in very different pathologic pathways. The first, p53, has been linked to over 50% of human cancers, and the second, Amyloid-β (Aβ), forms neurotoxic aggregates in the brains of patients with Alzheimer’s disease. We use these representative proteins to illustrate some of the challenges associated with studying IDPs and demonstrate how computational tools can be fruitfully applied to arrive at a more comprehensive understanding of these fascinating heteropolymers.
MIT Department
Institute for Medical Engineering and Science
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Research Laboratory of Electronics
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/polym6102684
