Autosomal recessive spastic ataxia of Charlevoix-Saguenay, or ARSACS, is a neurodegenerative disorder whose symptoms appear in childhood and is caused by point mutations in the SACS gene (13q11), which encodes the Sacsin protein. Sacsin includes a HEPN domain, which shares structural similarity with the HEPN domains of other known RNA-binding proteins. Notably, individuals with a single missense mutation in this domain exhibit symptoms comparable to those of other ARSACS patients, indicating that altered RNA-binding abilities may play a role in the disease’s pathogenesis. We are investigating the RNA-binding properties of the HEPN domain. With the help of bioinformatic tools RNAA and RNAB, were selected based on their predicted higher (A) and lower (B) affinity for the HEPN domain, respectively. Their interaction with HEPN was analyzed via intrinsic fluorescence, circular dichroism (CD), and size-exclusion chromatography (SEC). Here we present the results of these experiments as well as GST-pulldown assays that confirmed the ability of HEPN to selectively bind to RNAs. We also show the modelling efforts to investigate the interaction of HEPN (monomer and dimer) with short RNA sequences. We used Alphafold3 and Nufold to predict the structure of isolated RNA sequences and of HEPN-RNA complexes (Alphafold3 only). Notwithstanding the difficulty in predicting RNA 3D structures with currently available computational tools, we found a suggestion of a specific interaction at the monomer-monomer interface of the HEPN dimer. We also performed Molecular Dynamics simulations of the HEPN, HEPN dimer and of complexes with strongly and weakly interacting RNA sequences. We plan to design a pipeline of machine learning and traditional tools to explore a wider space of RNA sequences to retrieve stronger and more specific RNA binders of HEPN.
Martorana, V., Cappelli, G., Carrotta, R., Costa, M., Cusimano, A., Longo, L., et al. (2025). Testing the interaction of HEPN domain of Sacsin with RNA through experimental and computational efforts. In Book of abstracts EMBO Workshop When predictions meet experiments: the next challenges in structural biology 09 – 12 September 2025 | Palermo, Italy.
Testing the interaction of HEPN domain of Sacsin with RNA through experimental and computational efforts
Rita Carrotta;Antonella Cusimano;Lisa Longo;Maria Mangione;Rosa Passantino;
2025-09-01
Abstract
Autosomal recessive spastic ataxia of Charlevoix-Saguenay, or ARSACS, is a neurodegenerative disorder whose symptoms appear in childhood and is caused by point mutations in the SACS gene (13q11), which encodes the Sacsin protein. Sacsin includes a HEPN domain, which shares structural similarity with the HEPN domains of other known RNA-binding proteins. Notably, individuals with a single missense mutation in this domain exhibit symptoms comparable to those of other ARSACS patients, indicating that altered RNA-binding abilities may play a role in the disease’s pathogenesis. We are investigating the RNA-binding properties of the HEPN domain. With the help of bioinformatic tools RNAA and RNAB, were selected based on their predicted higher (A) and lower (B) affinity for the HEPN domain, respectively. Their interaction with HEPN was analyzed via intrinsic fluorescence, circular dichroism (CD), and size-exclusion chromatography (SEC). Here we present the results of these experiments as well as GST-pulldown assays that confirmed the ability of HEPN to selectively bind to RNAs. We also show the modelling efforts to investigate the interaction of HEPN (monomer and dimer) with short RNA sequences. We used Alphafold3 and Nufold to predict the structure of isolated RNA sequences and of HEPN-RNA complexes (Alphafold3 only). Notwithstanding the difficulty in predicting RNA 3D structures with currently available computational tools, we found a suggestion of a specific interaction at the monomer-monomer interface of the HEPN dimer. We also performed Molecular Dynamics simulations of the HEPN, HEPN dimer and of complexes with strongly and weakly interacting RNA sequences. We plan to design a pipeline of machine learning and traditional tools to explore a wider space of RNA sequences to retrieve stronger and more specific RNA binders of HEPN.| File | Dimensione | Formato | |
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