Biology subjects

Axenopoulos, A.

Publications and source records attributed to Axenopoulos, A..

2 recordsLinked to original sources

bioRxiv2025

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↗

bioRxiv2024

ParaSurf: A Surface-Based Deep Learning Approach for Paratope-Antigen Interaction Prediction

MotivationIdentifying antibody binding sites, is crucial for developing vaccines and therapeutic antibodies, processes that are time-consuming and costly. Accurate prediction of the paratopes binding site can speed up the development by improving our understanding of antibody-antigen interactions. ResultsWe present ParaSurf, a deep learning model that significantly enhances paratope prediction by incorporating both surface geometric and non-geometric factors. Trained and tested on three prominent antibody-antigen benchmarks, ParaSurf achieves state-of-the-art results across nearly all metrics. Unlike models restricted to the variable region, ParaSurf demonstrates the ability to accurately predict binding scores across the entire Fab region of the antibody. Additionally, we conducted an extensive analysis using the largest of the three datasets employed, focusing on three key components: (1) a detailed evaluation of paratope prediction for each Complementarity-Determining Region loop, (2) the performance of models trained exclusively on the heavy chain, and (3) the results of training models solely on the light chain without incorporating data from the heavy chain. Availability and ImplementationSource code for ParaSurf, along with the datasets used, preprocessing pipeline, and trained model weights, are freely available at https://github.com/aggelos-michael-papadopoulos/ParaSurf. Contactangepapa@iti.gr, axenop@iti.gr Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗