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H. Mikolajek

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Open access Jul 2026

Comprehensive biophysical and structural profiling of alpha-actinin-2 variants reveals mechanistic diversity in hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a genetic disease associated with sudden cardiac death. Variants in alpha-actinin-2 (ACTN2), a Z-disc protein that anchors actin thin filaments have been implicated in HCM, yet their structural consequences remain poorly defined. Here, we characterise seventeen HCM-associated ACTN2 variants spanning multiple domains using an integrated and tiered workflow combining high-throughput assays, structural modelling and biophysical approaches. All variants display reduced solubility, with actin-binding domain (ABD) substitutions showing pronounced thermal instability by differential scanning fluorimetry. Modelling of nine variants predicts diverse pathogenic mechanisms including compromised actin-binding, impaired ABD regulatory conformations, disrupted dimerisation interfaces, and perturbed domain architecture. Crystal structures of two rod-domain variants reveal intact dimerisation despite modelling predictions. Actin-binding assays for ABD variants confirm altered actin engagement suggesting that binding dynamics may drive pathogenicity. Limited proteolysis indicates reduced structural stability across variants, while size-exclusion chromatography coupled with multi-angle light scattering or small-angle X-ray scattering (SEC-MALS/SAXS) shows a strong propensity for aggregation. Batch-mode SAXS further demonstrates early aggregation onset in selected ABD variants at elevated temperatures. Collectively, these findings establish that HCM-linked ACTN2 variants compromise protein integrity through multiple mechanisms, highlight the ABD as a hotspot of vulnerability and provide a potential framework for interpreting cardiomyopathy-associated variants. Inherited cardiac conditions are linked to genetic misspellings (or variants) in essential heart proteins such as alpha-actinin-2. Here, the authors uncover mechanistic diversity by which distinct genetic variants may drive disease, using a comprehensive range of structural analyses.

Maya Noureddine, H. Mikolajek, Nathan Cowieson et al. · 0 citations
Open access Jul 2026

AI-assisted fragment-based drug discovery of SARS-CoV-2 macrodomain binders validated by NMR and X-ray crystallography.

Fragment-based drug discovery (FBDD) is an effective approach for exploring chemical space using small, low-affinity fragments as starting points to facilitate development of lead compounds. Strategies to improve fragment potency include fragment merging and linking to generate higher-affinity inhibitors. Recently, artificial intelligence (AI) and machine learning (ML) have accelerated this process through structure-based optimization and generative compound design. Here, we present an AI-assisted FBDD workflow applied to the SARS-CoV-2 macrodomain (Mac1), a conserved viral protein involved in immune evasion and ADP-ribose metabolism. Using available structural data and previously identified fragments, we combined deep learning with molecular docking to design novel Mac1 binders. Selected compounds were synthesized and validated by NMR spectroscopy and X-ray crystallography, demonstrating improved binding relative to the original fragment hits with KD values in the range of 299-990 µM. This study demonstrates the advantages of integrating AI with FBDD to streamline molecular design, providing a data-driven framework for discovering new Mac1 inhibitors and guiding future antiviral drug development.

Elnaz Aledavood, Sandra Ramos-Inza, Jannis Born et al. · 0 citations