Tau Amyloid Corona‐Shelled Nanoparticle‐Based Drug Screening Platform for Discovering Tau Oligomer‐Degrading Drugs

ABSTRACT Tau oligomers are recognized for their critical role in causing neuronal toxicity and synaptic dysfunction in a diverse array of neurodegenerative diseases collectively referred to as tauopathies. However, the discovery of drugs that specifically target tau oligomers has been impeded by the absence of appropriate screening methods. Here, we suggest a drug screening platform based on tau amyloid corona‐shelled nanoparticles (TACONs) to assess the efficacy of tau oligomer‐degrading agents through aggregation‐induced colorimetric responses of TACONs. TACONs were engineered via the encapsulation of gold nanoparticles (AuNPs) with homogeneous tau oligomers by leveraging heparin as a co‐factor. Our TACON‐based strategy harnesses two primary functions of AuNPs: (i) catalytic activators for the selective isolation of tau oligomers and (ii) optical reporters for quantifying colorimetric responses induced by tau oligomer‐degrading agents. To validate this approach, we employed proteases that can degrade tau oligomers (protease XIV and plasmin) along with various small molecules known to aid in the treatment of tauopathies. Furthermore, we significantly enhanced screening efficiency by integrating a time‐series deep learning architecture, enabling rapid identification of effective agents within 1 h. These results highlight the great potential of a deep learning‐assisted TACON‐based drug screening platform as a powerful strategy for streamlining drug discovery in tauopathies.

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Published
2025-08-29
DOI
https://doi.org/10.1002/agt2.70139
Primary Topic
Computational Drug Discovery Methods
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article

Tau Amyloid Corona‐Shelled Nanoparticle‐Based Drug Screening Platform for Discovering Tau Oligomer‐Degrading Drugs

Dongsung Park, Kyo Seon Hwang, Jae Won Jang, Hyo Gi Jung et al.
Aggregate
Computational Drug Discovery Methods
article

Tau Amyloid Corona‐Shelled Nanoparticle‐Based Drug Screening Platform for Discovering Tau Oligomer‐Degrading Drugs

Dongsung Park, Kyo Seon Hwang, Jae Won Jang, Hyo Gi Jung, Jeong Hoon Lee, Yeon Ho Kim, Dongtak Lee, Dae Sung Yoon, Seungmin Lee, Yong-Hwan Kim, Hyunji Kim, Won‐Jin Moon, Junho Bang
article en

Abstract

ABSTRACT Tau oligomers are recognized for their critical role in causing neuronal toxicity and synaptic dysfunction in a diverse array of neurodegenerative diseases collectively referred to as tauopathies. However, the discovery of drugs that specifically target tau oligomers has been impeded by the absence of appropriate screening methods. Here, we suggest a drug screening platform based on tau amyloid corona‐shelled nanoparticles (TACONs) to assess the efficacy of tau oligomer‐degrading agents through aggregation‐induced colorimetric responses of TACONs. TACONs were engineered via the encapsulation of gold nanoparticles (AuNPs) with homogeneous tau oligomers by leveraging heparin as a co‐factor. Our TACON‐based strategy harnesses two primary functions of AuNPs: (i) catalytic activators for the selective isolation of tau oligomers and (ii) optical reporters for quantifying colorimetric responses induced by tau oligomer‐degrading agents. To validate this approach, we employed proteases that can degrade tau oligomers (protease XIV and plasmin) along with various small molecules known to aid in the treatment of tauopathies. Furthermore, we significantly enhanced screening efficiency by integrating a time‐series deep learning architecture, enabling rapid identification of effective agents within 1 h. These results highlight the great potential of a deep learning‐assisted TACON‐based drug screening platform as a powerful strategy for streamlining drug discovery in tauopathies.

AggregateVol. 6(10)
Incheon National University (KR), Korea University (KR), Brookhaven National Laboratory (US), Kyung Hee University (KR), Integrative Medicine Institute (US), Bioengineering Center (RU), Netherlands Institute for Radio Astronomy (NL)
National Research Foundation, Ministry of Science, ICT and Future Planning, Korea Health Industry Development Institute, National Research Foundation of Korea
Openalex Percentile: Top 76%
Computational Drug Discovery Methods
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