Identification of Catechins’ Binding Sites in Monomeric Aβ42 through Ensemble Docking and MD Simulations

International Journal of Molecular Sciences, Journal Year: 2023, Volume and Issue: 24(9), P. 8161 - 8161

Published: May 3, 2023

The assembly of the amyloid-β peptide (Aβ) into toxic oligomers and fibrils is associated with Alzheimer's disease dementia. Therefore, disrupting amyloid by direct targeting Aβ monomeric form small molecules or antibodies a promising therapeutic strategy. However, given dynamic nature Aβ, standard computational tools cannot be easily applied for high-throughput structure-based virtual screening in drug discovery projects. In current study, we propose pipeline-in framework ensemble docking strategy-to identify catechins' binding sites Aβ42. It shown that both hydrophobic aromatic interactions hydrogen bonding are crucial catechins to Additionally, it has been found all studied ligands, especially EGCG, can act as potent inhibitors against aggregation blocking central region Aβ. Our findings evaluated confirmed multi-microsecond MD simulations. Finally, suggested our proposed pipeline, low cost comparison simulations, suitable approach ligand libraries

Language: Английский

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Protein aggregation and neurodegenerative disease: Structural outlook for the novel therapeutics

Proteins Structure Function and Bioinformatics, Journal Year: 2023, Volume and Issue: unknown

Published: Aug. 2, 2023

Abstract Before the controversial approval of humanized monoclonal antibody lecanemab, which binds to soluble amyloid‐β protofibrils, all treatments available earlier, for Alzheimer's disease (AD) were symptomatic. The researchers are still struggling find a breakthrough in AD therapeutic medicine, is partially attributable lack understanding structural information associated with intrinsically disordered proteins and amyloids. One major challenges this area research understand diversity under vitro conditions. Therefore, review, we have summarized applications biophysical methods, aimed shed some light on heterogeneity, pathogenicity, structures mechanisms protein aggregates proteinopathies including AD. This review will also rationalize strategies modulating disease‐relevant pathogenic entities by small molecules using biology approaches characterization. We highlighted tools techniques simulate vivo conditions native cytotoxic tau/amyloids assemblies, urge new chemical replicate assemblies similar those conditions, addition designing novel potential drugs.

Language: Английский

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Computationally Designed Small Molecules Disassemble Both Soluble Oligomers and Protofibrils of Amyloid β-Protein Responsible for Alzheimer’s Disease

ACS Chemical Neuroscience, Journal Year: 2023, Volume and Issue: 14(15), P. 2717 - 2726

Published: July 13, 2023

Alzheimer's disease (AD) is one of the world's most pressing health crises. AD an incurable affecting more than 6.5 million Americans, predominantly elderly, and in its later stages, leads to memory loss, dementia, death. Amyloid β (Aβ) protein aggregates have been pathological hallmarks since initial characterization. The early stages Aβ accumulation aggregation involve formation oligomers, which are considered neurotoxic play a key role further into fibrils that eventually appear brain as amyloid plaques. We recently shown by combining ion mobility mass spectrometry (IM-MS) atomic force microscopy (AFM) Aβ42 rapidly forms dodecamers (12-mers) terminal oligomeric state, these seed protofibrils. link between soluble oligomers fibril essential aspects for understanding root cause state critical developing therapeutic interventions. Utilizing joint pharmacophore space (JPS) method, potential drugs designed specifically amyloid-related diseases. These small molecules were generated based on crucial chemical features necessary target selectivity. In this paper, we utilize our combined IM-MS AFM methods investigate impact three second-generation JPS small-molecule inhibitors, AC0201, AC0202, AC0203, dodecamer well Aβ42. Our results indicate AC0201 works inhibitor remodeler both formation, AC0203 behaves less efficiently, AC0202 ineffective.

Language: Английский

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An integrated machine learning approach delineates an entropic expansion mechanism for the binding of a small molecule to α-synuclein

eLife, Journal Year: 2024, Volume and Issue: 13

Published: June 3, 2024

The mis-folding and aggregation of intrinsically disordered proteins (IDPs) such as α-synuclein (αS) underlie the pathogenesis various neurodegenerative disorders. However, targeting αS with small molecules faces challenges due to lack defined ligand-binding pockets in its structure. Here, we implement a deep artificial neural network-based machine learning approach, which is able statistically distinguish fuzzy ensemble conformational substates neat water from those aqueous fasudil (small molecule interest) solution. In particular, presence solvent either modulates pre-existing states or gives rise new αS, akin an ensemble-expansion mechanism. ensembles display strong conformation-dependence residue-wise interaction molecule. A thermodynamic analysis indicates that small-molecule structural repertoire by tuning protein backbone entropy, however entropy remains unperturbed. Together, this study sheds light on intricate interplay between IDPs, offering insights into entropic modulation expansion key biophysical mechanisms driving potential therapeutics.

Language: Английский

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AlzDiscovery: A computational tool to identify Alzheimer's disease‐causing missense mutations using protein structure information

Protein Science, Journal Year: 2024, Volume and Issue: 33(10)

Published: Sept. 14, 2024

Abstract Alzheimer's disease (AD) is one of the most common forms dementia and neurodegenerative diseases, characterized by formation neuritic plaques neurofibrillary tangles. Many different proteins participate in this complicated pathogenic mechanism, missense mutations can alter folding functions these proteins, significantly increasing risk AD. However, many methods to identify AD‐causing variants did not consider effect from perspective a protein three‐dimensional environment. Here, we present machine learning‐based analysis classify their benign counterparts 21 AD‐related leveraging both sequence‐ structure‐based features. Using computational tools estimate on stability, first observed bias with significant destabilizing effects family proteins. Combining insight, built generic predictive model, improved performance tuning sample weights training process. Our final model achieved area under receiver operating characteristic curve up 0.95 blind test 0.70 an independent clinical validation, outperforming all state‐of‐the‐art methods. Feature interpretation indicated that hydrophobic environment polar interaction contacts were crucial decision phenotypes mutations. Finally, presented user‐friendly web server, AlzDiscovery, for researchers browse predicted possible study will be valuable resource AD screening development personalized treatment.

Language: Английский

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