Safari, R. | bioRxiv Science

bioRxiv2026

HTLV-1 intasome recruits the PP2A-B56 holoenzyme and restricts its phosphatase activity

Integrase catalyses the insertion of a DNA copy of the retroviral genome into host cell chromatin. Human T-cell lymphotropic virus type 1 (HTLV-1) and other deltaretroviral integrases associate with protein phosphatase 2A holoenzymes containing B56 regulatory subunits (PP2A-B56). Here, we show that integrase mutants defective in binding to most B56 isoforms retain intrinsic DNA strand transfer activity but are impaired in establishing infection. Using single-particle cryo-EM, we determined the structure of the simian T-cell lymphotropic virus type 1 (STLV-1) intasome in a 0.5-MDa complex with two copies of the heterotrimeric PP2A-B56{gamma} holoenzyme at 2.8 [A] resolution. The structure reveals that, in addition to engaging B56, integrase forms direct contacts with the catalytic subunit of PP2A and sterically occludes the phosphatase active site, preventing substrate access. Consistent with these findings, we show that the HTLV-1 intasome suppresses PP2A catalytic activity in a manner dependent on the integrase LxxIxE short linear motif. We further demonstrate that pharmacological inhibition of PP2A does not impair HTLV-1 infection. Together, our results indicate that recruitment of PP2A-B56 by the intasome serves a structural rather than catalytic function.

microbiology↗

bioRxiv2024

AlphaRING: Missense Variant Classification via Protein Modelling and Residue Interaction Network Analysis

BackgroundAccurate interpretation of missense variants remains a significant challenge hindering genomic diagnosis. While state-of-the-art machine learning and deep learning predictors offer high accuracy, they often lack the transparency required for clinical evidence weighting. To bridge this gap between accuracy and clinical utility, we developed AlphaRING-X. MethodsAlphaRING-X is an open-source framework that integrates local and global protein structural stability features using an efficient implementation of gradient boosting, XGBoost, to predict variant deleteriousness. Local stability is interrogated through a combination of AlphaFold-based modelling and residue interaction network analysis. Changes in global stability upon mutation ({Delta}{Delta}G) are estimated using FoldX. For each prediction, AlphaRING-X quantifies the magnitude and direction of each features contribution using Shapley additive explanations (SHAP), providing a unified yet interpretable score. We trained and evaluated the model using a gold standard ClinVar dataset of validated neutral and deleterious variants. ResultsAlphaRING-X achieved an area under the receiver operator curve of 0.94, significantly outperforming widely used predictors such as CADD. Crucially, it provided unambiguous classification for 95.5% of variants at 90% precision in both neutral and deleterious classes. We analysed each predictions SHAP values, identifying local connectivity and disorder as the strongest contributors. ConclusionsAlphaRING-X combines high-performance prediction with the interpretability necessary for clinical decision-making. Its flexible, open-source architecture makes it a powerful, transparent tool for enhancing genomic diagnostics and advancing precision medicine.

bioinformatics↗