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Paras, N. A.

Publications and source records attributed to Paras, N. A..

2 recordsLinked to original sources

Stacked binding of a small molecule PET tracer to Alzheimer's tau paired helical filaments

Neurodegenerative diseases (NDs) are characterized by the formation of amyloid filaments that adopt disease-specific conformations in the brain. Recently developed small molecules hold promise as diagnostics and possible therapeutics for NDs, but their binding mechanisms to amyloid filaments remain unknown. Here, we used cryo-electron microscopy (cryo-EM) to determine a 2.7 [A] structure of Alzheimers disease patient-derived tau paired-helical filaments incubated with the GTP-1 PET probe. GTP-1 is bound stoichiometrically along an exposed cleft of each protofilament in a stacked arrangement that matches the fibrils symmetry. Multiscale modeling revealed favorable pi-pi aromatic stacking interactions between GTP-1 molecules that, together with small molecule-protein contacts, result in high affinity binding. This binding mode offers new insight into designing compounds for diagnosis and treatment of specific NDs. One Sentence SummaryCryo-EM structure reveals a novel stacked arrangement of the GTP-1 PET ligand bound to Alzheimers disease tau filaments.

biophysics↗

Trans-channel fluorescence learning improves high-content screening for Alzheimer's disease therapeutics

In microscopy-based drug screens, fluorescent markers carry critical information on how compounds affect different biological processes. However, practical considerations may hinder the use of certain fluorescent markers. Here, we present a deep learning method for overcoming this limitation. We accurately generated predicted fluorescent signals from other related markers and validated this new machine learning (ML) method on two biologically distinct datasets. We used the ML method to improve the selection of biologically active compounds for Alzheimers disease (AD) from high-content high-throughput screening (HCS). The ML method identified novel compounds that effectively blocked tau aggregation, which would have been missed by traditional screening approaches unguided by ML. The method improved triaging efficiency of compound rankings over conventional rankings by raw image channels. We reproduced this ML pipeline on a biologically independent cancer-based dataset, demonstrating its generalizability. The approach is disease-agnostic and applicable across diverse fluorescence microscopy datasets.

pharmacology and toxicology↗