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      Long-range Regulation of Partially Folded Amyloidogenic Peptides

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          Abstract

          Neurodegeneration involves abnormal aggregation of intrinsically disordered amyloidogenic peptides (IDPs), usually mediated by hydrophobic protein-protein interactions. There is mounting evidence that formation of α-helical intermediates is an early event during self-assembly of amyloid-β42 (Aβ42) and α-synuclein (αS) IDPs in Alzheimer’s and Parkinson’s disease pathogenesis, respectively. However, the driving force behind on-pathway molecular assembly of partially folded helical monomers into helical oligomers assembly remains unknown. Here, we employ extensive molecular dynamics simulations to sample the helical conformational sub-spaces of monomeric peptides of both Aβ42 and αS. Our computed free energies, population shifts, and dynamic cross-correlation network analyses reveal a common feature of long-range intra-peptide modulation of partial helical folds of the amyloidogenic central hydrophobic domains via concerted coupling with their charged terminal tails (N-terminus of Aβ42 and C-terminus of αS). The absence of such inter-domain fluctuations in both fully helical and completely unfolded (disordered) states suggests that long-range coupling regulates the dynamicity of partially folded helices, in both Aβ42 and αS peptides. The inter-domain coupling suggests a form of intra-molecular allosteric regulation of the aggregation trigger in partially folded helical monomers. This approach could be applied to study the broad range of amyloidogenic peptides, which could provide a new path to curbing pathogenic aggregation of partially folded conformers into oligomers, by inhibition of sites far from the hydrophobic core.

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          Most cited references70

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          Structure and dynamics of micelle-bound human alpha-synuclein.

          Misfolding of the protein alpha-synuclein (aS), which associates with presynaptic vesicles, has been implicated in the molecular chain of events leading to Parkinson's disease. Here, the structure and dynamics of micelle-bound aS are reported. Val3-Val37 and Lys45-Thr92 form curved alpha-helices, connected by a well ordered, extended linker in an unexpected anti-parallel arrangement, followed by another short extended region (Gly93-Lys97), overlapping the recently identified chaperone-mediated autophagy recognition motif and a highly mobile tail (Asp98-Ala140). Helix curvature is significantly less than predicted based on the native micelle shape, indicating a deformation of the micelle by aS. Structural and dynamic parameters show a reduced helical content for Ala30-Val37. A dynamic variation in interhelical distance on the microsecond timescale is complemented by enhanced sub-nanosecond timescale dynamics, particularly in the remarkably glycine-rich segments of the helices. These unusually rich dynamics may serve to mitigate the effect of aS binding on membrane fluidity. The well ordered conformation of the helix-helix connector indicates a defined interaction with lipidic surfaces, suggesting that, when bound to larger diameter synaptic vesicles, it can act as a switch between this structure and a previously proposed uninterrupted helix.
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            Water dispersion interactions strongly influence simulated structural properties of disordered protein states.

            Many proteins can be partially or completely disordered under physiological conditions. Structural characterization of these disordered states using experimental methods can be challenging, since they are composed of a structurally heterogeneous ensemble of conformations rather than a single dominant conformation. Molecular dynamics (MD) simulations should in principle provide an ideal tool for elucidating the composition and behavior of disordered states at an atomic level of detail. Unfortunately, MD simulations using current physics-based models tend to produce disordered-state ensembles that are structurally too compact relative to experiments. We find that the water models typically used in MD simulations significantly underestimate London dispersion interactions, and speculate that this may be a possible reason for these erroneous results. To test this hypothesis, we create a new water model, TIP4P-D, that approximately corrects for these deficiencies in modeling water dispersion interactions while maintaining compatibility with existing physics-based models. We show that simulations of solvated proteins using this new water model typically result in disordered states that are substantially more expanded and in better agreement with experiment. These results represent a significant step toward extending the range of applicability of MD simulations to include the study of (partially or fully) disordered protein states.
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              Dynamical networks in tRNA:protein complexes.

              Community network analysis derived from molecular dynamics simulations is used to identify and compare the signaling pathways in a bacterial glutamyl-tRNA synthetase (GluRS):tRNA(Glu) and an archaeal leucyl-tRNA synthetase (LeuRS):tRNA(Leu) complex. Although the 2 class I synthetases have remarkably different interactions with their cognate tRNAs, the allosteric networks for charging tRNA with the correct amino acid display considerable similarities. A dynamic contact map defines the edges connecting nodes (amino acids and nucleotides) in the physical network whose overall topology is presented as a network of communities, local substructures that are highly intraconnected, but loosely interconnected. Whereas nodes within a single community can communicate through many alternate pathways, the communication between monomers in different communities has to take place through a smaller number of critical edges or interactions. Consistent with this analysis, there are a large number of suboptimal paths that can be used for communication between the identity elements on the tRNAs and the catalytic site in the aaRS:tRNA complexes. Residues and nucleotides in the majority of pathways for intercommunity signal transmission are evolutionarily conserved and are predicted to be important for allosteric signaling. The same monomers are also found in a majority of the suboptimal paths. Modifying these residues or nucleotides has a large effect on the communication pathways in the protein:RNA complex consistent with kinetic data.
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                Author and article information

                Contributors
                damien.thompson@ul.ie
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                5 May 2020
                5 May 2020
                2020
                : 10
                : 7597
                Affiliations
                ISNI 0000 0004 1936 9692, GRID grid.10049.3c, Department of Physics, , Bernal Institute, University of Limerick, ; Limerick, V94 T9PX Ireland
                Author information
                http://orcid.org/0000-0002-4218-0308
                http://orcid.org/0000-0002-6556-7521
                http://orcid.org/0000-0003-2340-5441
                Article
                64303
                10.1038/s41598-020-64303-x
                7200734
                32371882
                22eb93ba-b0cb-4a2f-8d9c-2285b8208e5b
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 June 2019
                : 15 April 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001602, Science Foundation Ireland (SFI);
                Award ID: 15/CDA/3491
                Award ID: 15/CDA/3491
                Award ID: 15/CDA/3491
                Award Recipient :
                Categories
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                Custom metadata
                © The Author(s) 2020

                Uncategorized
                computational models,computational chemistry,molecular dynamics
                Uncategorized
                computational models, computational chemistry, molecular dynamics

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