MemDis: Predicting Disordered Regions in Transmembrane Proteins.
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ABSTRACT:
SUBMITTER: Dobson L
PROVIDER: S-EPMC8623522 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Many different intrinsically disordered proteins and proteins with intrinsically disordered regions are associated with neurodegenerative diseases. These types of proteins including amyloid-β, tau, α-synuclein, CHCHD2, CHCHD10, and G-protein coupled receptors are increasingly becoming evaluated as potential drug targets in the pharmaceutical-based treatment approaches. Here, we focus on the neurobiology of this class of proteins, which lie at the center of numerous neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases, Huntington's disease, amyotrophic lateral sclerosis, frontotemporal dementia, Charcot-Marie-Tooth diseases, spinal muscular atrophy, and mitochondrial myopathy. Furthermore, we discuss the current treatment design strategies involving intrinsically disordered proteins and proteins with intrinsically disordered regions in neurodegenerative diseases. In addition, we emphasize that although the G-protein coupled receptors are traditionally investigated using structural biology-based models and approaches, current studies show that these receptors are proteins with intrinsically disordered regions and therefore they require new ways for their analysis.
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Project description:Protein structure prediction methods such as Rosetta search for the lowest energy conformation of the polypeptide chain. However, the experimentally observed native state is at a minimum of the free energy, rather than the energy. The neglect of the missing configurational entropy contribution to the free energy can be partially justified by the assumption that the entropies of alternative folded states, while very much less than unfolded states, are not too different from one another, and hence can be to a first approximation neglected when searching for the lowest free energy state. The shortcomings of current structure prediction methods may be due in part to the breakdown of this assumption. Particularly problematic are proteins with significant disordered regions which do not populate single low energy conformations even in the native state. We describe two approaches within the Rosetta structure modeling methodology for treating such regions. The first does not require advance knowledge of the regions likely to be disordered; instead these are identified by minimizing a simple free energy function used previously to model protein folding landscapes and transition states. In this model, residues can be either completely ordered or completely disordered; they are considered disordered if the gain in entropy outweighs the loss of favorable energetic interactions with the rest of the protein chain. The second approach requires identification in advance of the disordered regions either from sequence alone using for example the DISOPRED server or from experimental data such as NMR chemical shifts. During Rosetta structure prediction calculations the disordered regions make only unfavorable repulsive contributions to the total energy. We find that the second approach has greater practical utility and illustrate this with examples from de novo structure prediction, NMR structure calculation, and comparative modeling.
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