Structure-Based Peptide Inhibitor Design of Amyloid-β Aggregation

Lu, Jianping; Cao, Qing; Wang, Chuchu; Zheng, Jing; Feng, Luo; Xie, Jingfei; Li, Yichen; Ma, Xirui; He, Lin; Eisenberg, David; Nowick, James S.; Jiang, Lin; Li, Dan

doi:10.3389/fnmol.2019.00054

Cited by 64 publications

(49 citation statements)

References 62 publications

(76 reference statements)

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“…5, E versus C). As expected, the HIV TAR-Tat interface exhibits substantial shape complementarity in its core, as indicated by an S c value of 0.66 -comparable with antibodyantigen interfaces and peptides designed to inhibit ␤-amyloid aggregation (102,133).…”

Section: Molecular Recognition Of Tar By Tat Peptidessupporting

confidence: 64%

Face-time with TAR: Portraits of an HIV-1 RNA with diverse modes of effector recognition relevant for drug discovery

Chavali

Bonn-Breach

Wedekind

2019

Journal of Biological Chemistry

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Section: Molecular Recognition Of Tar By Tat Peptidessupporting

confidence: 64%

Face-time with TAR: Portraits of an HIV-1 RNA with diverse modes of effector recognition relevant for drug discovery

Chavali

Bonn-Breach

Wedekind

2019

Journal of Biological Chemistry

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“…Notably, these structures represent snapshots of the kinetic cores of aggregation or end-state morphologies of amyloid fibrils and therefore provide limited information on the underlying aggregation pathways and toxicity-related effects of amyloids. On the other hand, the growing number of highresolution cryo-EM structures has highlighted the in vivo structural diversity of amyloid fibrils 60 , whereas steric zippers have been recently used for the development of targeted therapeutics [61][62][63] . However, determining the structural layout of amyloid fibrils still remains challenging.…”

Section: Discussionmentioning

confidence: 99%

Structure-based machine-guided mapping of amyloid sequence space reveals uncharted sequence clusters with higher solubilities

et al. 2020

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The amyloid conformation can be adopted by a variety of sequences, but the precise boundaries of amyloid sequence space are still unclear. The currently charted amyloid sequence space is strongly biased towards hydrophobic, beta-sheet prone sequences that form the core of globular proteins and by Q/N/Y rich yeast prions. Here, we took advantage of the increasing amount of high-resolution structural information on amyloid cores currently available in the protein databank to implement a machine learning approach, named Cordax (https://cordax.switchlab.org), that explores amyloid sequence beyond its current boundaries. Clustering by t-Distributed Stochastic Neighbour Embedding (t-SNE) shows how our approach resulted in an expansion away from hydrophobic amyloid sequences towards clusters of lower aliphatic content and higher charge, or regions of helical and disordered propensities. These clusters uncouple amyloid propensity from solubility representing sequence flavours compatible with surface-exposed patches in globular proteins, functional amyloids or sequences associated to liquid-liquid phase transitions.

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“…The results showed that curcumin derivatives bind strongly to the Aβ peptide. Finally, computational structure-based design was employed for the development of a series of peptide inhibitors for Aβ aggregation [114].…”

Section: Cadd For the Development Of Anti-aβ Aggregation Inhibitorsmentioning

confidence: 99%

Computer-Aided Drug Design of β-Secretase, γ-Secretase and Anti-Tau Inhibitors for the Discovery of Novel Alzheimer’s Therapeutics

Mouchlis

Melagraki

Zacharia

et al. 2020

IJMS

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Aging-associated neurodegenerative diseases, which are characterized by progressive neuronal death and synapses loss in human brain, are rapidly growing affecting millions of people globally. Alzheimer’s is the most common neurodegenerative disease and it can be caused by genetic and environmental risk factors. This review describes the amyloid-β and Tau hypotheses leading to amyloid plaques and neurofibrillary tangles, respectively which are the predominant pathways for the development of anti-Alzheimer’s small molecule inhibitors. The function and structure of the druggable targets of these two pathways including β-secretase, γ-secretase, and Tau are discussed in this review article. Computer-Aided Drug Design including computational structure-based design and ligand-based design have been employed successfully to develop inhibitors for biomolecular targets involved in Alzheimer’s. The application of computational molecular modeling for the discovery of small molecule inhibitors and modulators for β-secretase and γ-secretase is summarized. Examples of computational approaches employed for the development of anti-amyloid aggregation and anti-Tau phosphorylation, proteolysis and aggregation inhibitors are also reported.

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Structure-Based Peptide Inhibitor Design of Amyloid-β Aggregation

Cited by 64 publications

References 62 publications

Face-time with TAR: Portraits of an HIV-1 RNA with diverse modes of effector recognition relevant for drug discovery

Face-time with TAR: Portraits of an HIV-1 RNA with diverse modes of effector recognition relevant for drug discovery

Structure-based machine-guided mapping of amyloid sequence space reveals uncharted sequence clusters with higher solubilities

Computer-Aided Drug Design of β-Secretase, γ-Secretase and Anti-Tau Inhibitors for the Discovery of Novel Alzheimer’s Therapeutics

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