Search bioRxiv⌕ Search

Biology subjects

Kostova, S.

Publications and source records attributed to Kostova, S..

2 recordsLinked to original sources

Chemical targeting of the ATXN1 aa99-163 interaction site suppresses polyQ-expanded protein dimerization

Spinocerebellar ataxia type 1 (SCA1) is a neurodegenerative disease caused by the expansion of a polyglutamine (polyQ) tract in the ATXN1 protein. This expansion is thought to be responsible for the gradual aggregation of the mutant protein, which is associated with increased cytotoxicity and neuronal cell death. Apart from the polyQ tract, other domains in ATXN1 are also involved in the initial events of protein aggregation such as a dimerization domain that promotes protein oligomerization. ATXN1 interacts with various proteins; among them, MED15 that significantly enhances the aggregation of the polyQ-expanded protein. Therefore, we set to identify the interaction site between ATXN1 and MED15 and assess whether its chemical targeting would affect polyQ protein aggregation. First, we predicted the structure of ATXN1 and MED15 and simulated their interaction. We experimentally validated that amino acids (aa) 99-163 of ATXN1 and aa548-665 of MED15 are critical for this protein-protein interaction (PPI). We also show that the aa99-163 domain in ATXN1 is involved in the dimerization of the mutant isoform. Targeting this domain with a chemical compound identified through virtual screening (Chembridge ID: 5755483) inhibited both the interaction of ATXN1 with MED15 and the dimerization of polyQ-expanded ATXN1. These results strengthen our assumption that the aa99-163 domain of ATXN1 may be involved in polyQ protein aggregation and highlight compound 5755483 as a potent first-in-class therapeutic agent for SCA1.

neuroscience↗

AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays and AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

systems biology↗