A unifying framework for amyloid-mediated membrane damage: The lipid-chaperone hypothesis

Tempra, Carmelo; Scollo, Federica; Pannuzzo, Martina; Lolicato, Fabio; Rosa, Cristina La

doi:10.1016/j.bbapap.2022.140767

Cited by 16 publications

(9 citation statements)

References 191 publications

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“…Multiple MD simulations should be performed starting from different tau conformations and starting separation distances between tau and the membrane bilayer. Also, to recapitulate in vivo conditions, the membrane should include cholesterol and sphingomyelin components, , and the presence of free phospholipids in the extracellular milieu may be taken into account. ,− …”

Section: Discussionmentioning

confidence: 99%

Molecular Dynamics Simulations of the Tau R3–R4 Domain Monomer in the Bulk Solution and at the Surface of a Lipid Bilayer Model

Nguyen

Derreumaux

2022

J. Phys. Chem. B

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The aggregation of the tau protein plays a significant role in Alzheimer's disease, and the tau R3−R4 domain spanning residues 306−378 was shown to form the amyloid fibril core of a full-length tau. The conformations of the tau R3−R4 monomer in the bulk solution and at the surface of membranes are unknown. In this study, we address these questions by means of atomistic molecular dynamics. The simulations in the bulk solution show a very heterogeneous ensemble of conformations with low β and helical contents. The tau R3−R4 monomer has the propensity to form transient β-hairpins within the R3 repeat and between the R3 and R4 repeats and parallel β-sheets spanning the R3 and R4 repeats. The simulations also show that the surface of the membrane does not induce β-sheet insertion and leads to an ensemble of structures very different from those in the bulk solution. They also reveal the dynamical properties of the membrane-bound state of the tau R3−R4 monomer, enabling insertion of the residues 306−318 and 376−378.

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Section: Discussionmentioning

confidence: 99%

Molecular Dynamics Simulations of the Tau R3–R4 Domain Monomer in the Bulk Solution and at the Surface of a Lipid Bilayer Model

Nguyen

Derreumaux

2022

J. Phys. Chem. B

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“…The insertion leads to a damaged membrane with lower integrity eventually causing an increase in toxicity of the cell (lipid-chaperone hypothesis). It is observed that the extent of membrane poration and the protein–membrane binding affinity depend on the level of hydrophobicity of the protein sequence and hence on the α-helical propensity of the protein. − …”

Section: Introductionmentioning

confidence: 99%

Scan-Find-Scan-Model: Discrete Site-Targeted Suppressor Design Strategy for Amyloid-β

Bhagavatula¹,

Sarkar

Santra

et al. 2022

ACS Chem. Neurosci.

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Alzheimer’s disease is undoubtedly the most well-studied neurodegenerative disease. Consequently, the amyloid-β (Aβ) protein ranks at the top in terms of getting attention from the scientific community for structural property-based characterization. Even after decades of extensive research, there is existing volatility in terms of understanding and hence the effective tackling procedures against the disease that arises due to the lack of knowledge of both specific target- and site-specific drugs. Here, we develop a multidimensional approach based on the characterization of the common static-dynamic-thermodynamic trait of the monomeric protein, which efficiently identifies a small target sequence that contains an inherent tendency to misfold and consequently aggregate. The robustness of the identification of the target sequence comes with an abundance of a priori knowledge about the length and sequence of the target and hence guides toward effective designing of the target-specific drug with a very low probability of bottleneck and failure. Based on the target sequence information, we further identified a specific mutant that showed the maximum potential to act as a destabilizer of the monomeric protein as well as enormous success as an aggregation suppressor. We eventually tested the drug efficacy by estimating the extent of modulation of binding affinity existing within the fibrillar form of the Aβ protein due to a single-point mutation and hence provided a proof of concept of the entire protocol.

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“…For example, the first pathogenic mutation in the SNCA gene, encoding for α-synuclein was discovered in cases of familial Parkinson's disease [9], some of these point mutations cause Parkinson's with high penetrance. In addition, the severity of cognitive impairment in Alzheimer's disease was later shown to better correlate with low-molecular weight and soluble amyloid-beta aggregates, when amyloid-beta is highly disorganized in shape, it's actually less likely to stick together and form toxic clusters that lead to brain cell death [10]. A considerable number of biophysical studies have shown, type-2 diabetic islets are characterized by islet amyloid protein derived from islet amyloid peptide (IAPP), a protein co-expressed by beta cells with insulin that, when misfolded and present in aggregated form, may lead to beta cell failure [11].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Intrinsically Disordered Proteins Using Machine Learning Based on Low Complexity Methods

Zeng

Liu

2022

Algorithms

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Prediction of intrinsic disordered proteins is a hot area in the field of bio-information. Due to the high cost of evaluating the disordered regions of protein sequences using experimental methods, we used a low-complexity prediction scheme. Sequence complexity is used in this scheme to calculate five features for each residue of the protein sequence, including the Shannon entropy, the Topo-logical entropy, the Permutation entropy and the weighted average values of two propensities. Particularly, this is the first time that permutation entropy has been applied to the field of protein sequencing. In addition, in the data preprocessing stage, an appropriately sized sliding window and a comprehensive oversampling scheme can be used to improve the prediction performance of our scheme, and two ensemble learning algorithms are also used to verify the prediction results before and after. The results show that adding permutation entropy improves the performance of the prediction algorithm, in which the MCC value can be improved from the original 0.465 to 0.526 in our scheme, proving its universality. Finally, we compare the simulation results of our scheme with those of some existing schemes to demonstrate its effectiveness.

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A unifying framework for amyloid-mediated membrane damage: The lipid-chaperone hypothesis

Cited by 16 publications

References 191 publications

Molecular Dynamics Simulations of the Tau R3–R4 Domain Monomer in the Bulk Solution and at the Surface of a Lipid Bilayer Model

Molecular Dynamics Simulations of the Tau R3–R4 Domain Monomer in the Bulk Solution and at the Surface of a Lipid Bilayer Model

Scan-Find-Scan-Model: Discrete Site-Targeted Suppressor Design Strategy for Amyloid-β

Prediction of Intrinsically Disordered Proteins Using Machine Learning Based on Low Complexity Methods

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